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Quasar detection in the UBC-NASA multi-narrowband survey Braglia, Katia 2002

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Quasar detection in the UBC-NASA multi-narrowband survey. by KATIA BRAGLIA Laurea i n Astronomia, Universita' degli Studi d i Bologna, Italy, 1999 A THESIS S U B M I T T E D IN P A R T I A L F U L F I L M E N T O F THE REQUIREMENTS FOR T H E DEGREE OF M A S T E R OF SCIENCE in T H E F A C U L T Y OF G R A D U A T E STUDIES (Department of Physics and Astronomy)  We accept this thesis as conforming to the required standard  THE UNIVERSITY OF BRITISH C O L U M B I A August 11, 2002 © K A T I A B R A G L I A , 2002  In presenting this thesis i n partial fulfilment of the requirements for an advanced degree at the University of B r i t i s h C o l u m b i a , I agree that the L i b r a r y shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission.  Department of Physics and Astronomy  The University O f B r i t i s h C o l u m b i a Vancouver, C a n a d a  ii  ABSTRACT  ABSTRACT The goal of this thesis is to select quasars by applying a novel analysis to the U B C - N A S A M u l t i - N a r r o w band Survey ( U N M S 1 ) catalog. The database consists of drift-scan observations taken w i t h the 3-m N A S A L i q uid M i r r o r Telescope ( L M T ) i n 1996-1997 and i n 1999, using 35 narrow band filters, from 4500 to 9500 A , and 4 broad bands (B, V, R, I), necessary for calibration purposes. The method presented here is based on the comparison between U N M S 1 catalog sources and stellar, galaxy, and quasar templates through a x  2  imization procedure combined w i t h Bayesian analysis. T h e x  2  m  m  -  parameter is  useful to determine which model is the best fit for an observed Spectral E n ergy D i s t r i b u t i o n ( S E D ) ; the odds ratio parameter, from the Bayes' theorem, is necessary i n order to know the most likely category the source belongs, and it involves information such as the number of models for each category and the surface densities of stars, galaxies and quasars at a given magnitude. After the method was applied to templates, treated as test-objects, it is concluded that more than 85% of quasar candidates, selected w i t h at least 30 filter measurements, are correctly classified: the misclassification is due mostly to the similarity between stellar models and quasars when important spectral features are missed. O f a l l the 39040 selected sources, 3056 quasar candidates were identified:  ABSTRACT  iii  most of them have typical redshift (z ~ 0.3 - 4), apparent V magnitude (V ~ 16 - 20) and spectral index ( a ~ -2 - 0.8), but there is also a non-negligible number of objects localized well outside these ranges. A n a l y s i n g the position of these sources i n the redshift - V magnitude plane, redshift - spectral i n dex plane and V magnitude - spectral index plane, it is possible to identify misclassified quasars and remove them away from the sample. T h e final list of quasar candidates includes 2294 objects, among which 1 is i n common w i t h Veron's quasar catalog.  CONTENTS  iv  CONTENTS ABSTRACT  ii  CONTENTS  iv  LIST OF T A B L E S  vii  LIST OF F I G U R E S  ix  I  1  1  2  THESIS INTRODUCTION  2  1.1  5  R E C E N T SURVEYS A N D METHODS TO FIND QUASARS.  LIQUID M I R R O R TELESCOPES  13  2.1  LIQUID M I R R O R T E L E S C O P E S IN T H E P A S T  13  2.2  T H E NODO LIQUID M I R R O R T E L E S C O P E  15  2.3  DATA COLLECTION  17  2.4  LMT ADVANTAGES  19  2.5  L M T DISADVANTAGES  20  2.6  L M T T E C H N I C A L ISSUES  22  2.7  DATA ANALYSIS  23  2.7.1  23  OBJECT DETECTION AND PHOTOMETRY  CONTENTS 2.7.2  3  4  ASTROMETRIC AND PHOTOMETRIC CALIBRATION  25  THE OBJECT CATALOG  26  CHARACTERISTICS OF THE SURVEY  27  2.8.1  30  2.7.3 2.8  v  PHOTOMETRIC SELECTION CRITERIA  QUASI STELLAR OBJECTS  33  3.1  QSO'S F U N D A M E N T A L C H A R A C T E R I S T I C S  33  3.2  QSO S T R U C T U R E  38  THE UNMS QUASAR SURVEY  41  4.1  T H E UNMS1 C A T A L O G  41  4.2  METHOD  44  4.3  TEMPLATES  48  4.3.1  STELLAR TEMPLATES  48  4.3.2  GALAXY TEMPLATES  50  4.3.3  QUASAR TEMPLATES  51  4.4  METHOD CALIBRATION FROM TEMPLATES  58  4.4.1  A FIRST ANALYSIS  60  4.4.2  x  2  MINIMIZATION M E T H O D A N D ODDS RATIO  P R O C E D U R E A P P L I E D T O O R I G I N A L T E M P L A T E S . 66 4.4.3  x  2  A N D ODDS RATIO PROCEDURES  APPLIED  TO ALTERED TEMPLATES 5  77  QUASARS IN T H E U N M S l C A T A L O G  98  5.1  RESULTS  98  5.1.1  99  S T E L L A R C A N D I D A T E S IN T H E U N M S S U R V E Y . .  CONTENTS 5.1.2 5.2  vi  G A L A X Y C A N D I D A T E S I N T H E U N M S S U R V E Y . . 100  PROPERTIES  OF Q U A S A R CANDIDATES  107  5.2.1  T H E N U M B E R - REDSHIFT DISTRIBUTION.  . . .  5.2.2  T H E N U M B E R - A P P A R E N T V M A G N I T U D E DISTRIBUTION  5.2.3  6  THE NUMBER - SPECTRAL INDEX a  107  110 DISTRIBU-  TION  112  5.3  DISCUSSION  114  5.4  QUASAR CANDIDATES  119  5.5  IDENTIFICATION  123  S U M M A R Y AND CONCLUSIONS  BIBLIOGRAPHY  124 127  LIST  OF TABLES  vii  LIST O F T A B L E S 1.1  A r e a covered, epoch, magnitude, A A and number of expected or detected quasar candidates for the most recent optical Q S O surveys  2.1  6  Filter specifications, (a) mean wavelength (nm), from transmission curve; (b) bandwidth (nm): equivalent w i d t h / c e n t r a l transmission; (c) log of central frequency (Hz): c/mean wavelength; (d) log frequency bandwidth: 0.434 x b a n d w i d t h / m e a n wavelength; (e) central transmission; (f) equivalent w i d t h (nm): integral of transmission curve  29  3.1  Inventory of emission lines [23]  37  4.1  Parameters i n the U N M S 1 catalog  42  4.2  F l u x corrections by R e m i Cabanac [priv. comm.]  43  4.3  Surface densities for stars, galaxies and quasars i n the broad bands:  the units are n u m / d e g  num/deg  2  2  for the B and / filters, and  0.5 mag i n the R and V bands. T h e "a" case is for  B < 19.5 and the "b" is for B > 19.5  47  4.4  Parameters i n Pickles' stellar library  49  4.5  Spectral templates from K i n n e y & C a l z e t t i galaxies' library.  4.6  F i t t e d power-law index  .  51 54  LIST OF TABLES 4.7  viii  Power-law index i n frequency and wavelength for quasar templates i n the U V and optical-near-infrared (Optical-IR) region.  58  4.8  Template sequence for the x  69  5.1  Star, galaxy and quasar identifications  5.2  Conversion table between stellar type and number  5.3  Logarithmic star counts [# / 10 pc ] by stellar type and l u m i -  2  analysis  4  102  3  nosity class from data published i n A l l e n (1973) [1] 5.4  98  102  Logarithmic star counts [# / 10 pc ] for candidates found i n 4  3  the L M T survey.  103  5.5  Conversion table between galaxy type and number  103  5.6  Fraction of ellipticals ( E ) , SO galaxies, Sa+Sb, S c + S d and S m spirals, Irregular (Im) galaxies, Starburst (SB1-6) and U V h o t galaxies i n the C f A l , C f A 2 and L M T galaxy samples  5.7  105  E q u a t o r i a l coordinates ( R A , D E C ) , apparent V magnitude and redshift z for the quasar common to Veron's catalog and  6.1  U N M S 1 quasar catalog  123  D i s t r i b u t i o n of star, galaxy and quasar candidates  125  LIST  OF FIGURES  ix  LIST O F F I G U R E S 1.1  E v o l u t i o n of telescope aperture over time  4  1.2  Two-colour diagram: quasars (points) are well separated by m a i n sequence stars (line w i t h stellar types BO - MO) ( h t t p : / / n e d www.ipac.caltech.ca/Cambridge/Cambridge l_3_3.html).  . . .  2.1  Components for a liquid mirror  2.2  Basic optical layout for the U B C - L a v a l 2.7-m liquid mirror  9 16  telescope  18  2.3  F i l t e r transmission curves  31  3.1  General layout for a quasar spectrum  35  3.2  Structure of A G N ' s inner region ( h t t p : / / nedwww.ipac.caltech. edu/level5/Urryl/UrryPl.html)  39  4.1  Corrections w i t h the best fit curve  44  4.2  K i n n e y - C a l z e t t i ' s galaxy templates normalized at 4000 A. . . .  52  4.3  Composite spectrum of 101 quasars, binned to 2 A  53  4.4  Composite spectrum: power law fits to the estimated flux are shown  4.5  55  F i r s t part of the composite spectrum i n the 300-3400 A range: it can be noted that the L y a (1215 A) and C I V (1549 A) are among the strongest emission lines  56  LIST OF FIGURES 4.6  x  Second part of the composite "flat" spectrum i n the 34008550 A range. Ha (6563 A ) and [OIII] (4363 A ) are among the strongest emission lines  4.7  57  Quasar templates i n the rest frame: lines I, II, III and I V represent respectively template I, II, III and I V  4.8  59  Galactic (blue line) and quasar (red line) templates i n the rest frame w i t h Supergiant star templates (green line)  4.9  61  Galactic (blue line) and quasar (red line) templates i n the rest frame w i t h bright giant templates (green line)  62  4.10 Galactic (blue line) and quasar (red line) templates i n the rest frame w i t h different giant star templates (green line)  63  4.11 Galactic (blue line) and quasar (red line) templates i n the rest frame w i t h different subgiant star templates (green line).  . . .  64  4.12 Galactic (blue line) and quasar (red line) templates i n the rest frame w i t h different main sequence star templates (green line).  65  4.13 Galactic (blue line) and quasar (red line) templates at redshift z—1.2 w i t h supergiant star templates (green line)  66  4.14 Galactic (blue line) and quasar (red line) templates at redshift z=1.2 w i t h bright giant star templates (green line)  67  4.15 Galactic (blue line) and quasar (red line) templates at redshift z=1.2 w i t h giant star templates (green line)  70  4.16 Galactic (blue line) and quasar (red line) templates at redshift z=1.2 w i t h sub-giant star templates (green line)  71  4.17 Galactic (blue line) and quasar (red line) templates at redshift z=1.2 w i t h main sequence stars (green line)  72  LIST OF FIGURES 4.18 x  2  xi  distribution for 4 QSO N.14 at z=0.3: line I, II a n d III  come from the comparison respectively to galactic, quasar and stellar templates  73  4.19 x distribution for QSO N.15 at z=1.4: line I, II and III come 2  from the comparison respectively to galactic, quasar and stellar templates  74  4.20 x distribution for QSO N.16 at z=3.5: line I, II and III come 2  from the comparison respectively to galactic, quasar and stellar templates  75  4.21 x distribution for QSO N.17 at z=5.6: line I, II and III come 2  from the comparison respectively to galactic, quasar and stellar templates 4.22 x  2  76  distribution for the altered QSO N.14 at z=0.3: line I,  II and III come from the comparison respectively to galactic, quasar and stellar templates 4.23 x  2  78  distribution for the altered QSO N.15 at z=1.4: line I,  II and III come from the comparison respectively to galactic, quasar and stellar templates 4.24 x  2  79  distribution for the altered QSO N.16 at z=3.5: line I,  II and III come from the comparison respectively to galactic, quasar and stellar templates 4.25 x  2  80  distribution for the altered QSO N.17 at z=5.6: line I,  II and III come from the comparison respectively to galactic, quasar and stellar templates  81  LIST OF FIGURES  xii  4.26 D i s t r i b u t i o n of stellar (hexagon), galaxian (cross) and quasar (triangle) template-objects as classified according to the odds ratio method  83  4.27 Normalized distribution for correctly classified stars (hexagon) and for objects wrongly classified as stars (cross)  84  4.28 Normalized distribution i n the redshift range z=0-0.6 for correctly classified galaxies (hexagon) and for objects wrongly classified as galaxies (cross)  85  4.29 Normalized distribution i n the redshift range z=0-0.6 for correctly classified quasars (hexagon) and for objects wrongly classified as quasars (cross)  86  4.30 Normalized distribution i n the redshift range z=0-0.6 for correctly classified galaxies (hexagon) and for objects wrongly classified as galaxies (cross)  87  4.31 Normalized distribution i n the redshift range z=0.6-1.2 for correctly classified quasars (hexagon) and for objects wrongly classified as quasars (cross)  88  4.32 Normalized distribution i n the redshift range z=l.2-4.1 for correctly classified quasars (hexagon) and for objects wrongly classified as quasars (cross)  89  4.33 Normalized distribution i n the redshift range z=4.1-7 for correctly classified quasars (hexagon) and for objects wrongly classified as quasars (cross)  90  LIST OF FIGURES  xiii  4.34 Normalized odds ratio distribution for a Sa spiral galaxy (N.4) at redshift 0.2, 0.6, 0.8 and 1.2 i n favor of stellar (hexagon), galactic (croos) and quasar (triangle) template  92  4.35 Normalized odds ratio distribution for a starburst galaxy (N.7) at redshift 0.2, 0.6, 0.8 and 1.2. i n favour of stellar (hexagon), galactic (cross) and quasar (triangle) template  93  4.36 Normalized odds ratio distribution for Q S O N.14 at redshift 0.3, 2.1, 4.2 and 7.in favour of stellar (hexagon), galactic (triangle) and quasar (cross) template  94  4.37 Normalized odds ratio distribution for Q S O N.15 at redshift 0.3, 2.1, 4.2 and 7.  i n favour of stellar (hexagon), galactic  (triangle) and quasar (cross) template  95  4.38 Normalized odds ratio distribution for Q S O N.16 at redshift 0.3, 2.1, 4.2 and 7.in favour of stellar (hexagon), galactic (triangle) and quasar (cross) template  96  4.39 Normalized odds ratio distribution for Q S O N.17 at redshift 0.3, 2.1, 4.2 and 7.in favour of stellar (hexagon), galactic (triangle) and quasar (cross) template  97  5.1  Star counts by spectral types  99  5.2  G a l a x y distribution by the morphological type  5.3  D i s t r i b u t i o n of galaxy counts w i t h spectral type and redshift. . 106  5.4  Redshift distribution of quasar candidates  5.5  The redshift distribution for quasars i n the Hewett&Burbidge catalog (top) and i n the Sloan Sky Survey catalog (bottom).  5.6  The V magnitude distribution for quasar candidates  104  108  . 109 Ill  LIST 5.7  OF FIGURES  xiv  T h e V magnitude distribution from the Hewett&Burbidge catalog  5.8  112  Spectral index distribution for quasar candidates i n the U N M S l catalog  5.9  114  T h e spectral index distribution by Warren et al. (top picture, upper line) and by Francis et al. (bottom)  115  5.10 Quasar candidates i n the redshift - V magnitude plane 5.11 Quasar candidates i n the redshift - spectral index plane.  117 . . . 118  5.12 Quasar candidates i n the spectral index - V magnitude plane.  119  5.13 The number - redshift distribution for the "cleaned" set of quasars  120  5.14 T h e number - V magnitude distribution for the "cleaned" set of quasars  121  5.15 T h e number - a distribution for the "cleaned" set of quasars. . 122  PART I Thesis  CHAPTER  1.  INTRODUCTION  2  CHAPTER 1 INTRODUCTION Quasars were first discovered i n 1963, when several faint radio sources inside the 3rd Cambridge Catalogue of Discrete R a d i o Sources (3C) were identified as optical objects undistinguishible from stars [13]. Their diameters, smaller than 1 arc sec, indicated an unusually high surface in the optical and radio spectral regions.  brightness  Their positions, away from the  galactic equator, suggested an extragalactic nature. The name quasar (quasi stellar radio source) was coined because of their stellar appearance and the fact that their radio emission is comparable to that of Cygnus A . The first discovered quasar was 3 C 273 [62], a star-like source identified as the optical counterpart of a radio galaxy whose position was known accurately. F i r s t considered a stellar object, its real extragalactic nature became clear only after A l a n Sandage saw broad emission lines at positions unusual for stars and M . Schmidt identified a continuum and strong emission features w i t h redshift z=0.158, i m p l y i n g a distance around 950 M p c [73]. In 1965 Sandage and Veron [61], working on the identification of radio sources, realized that there were many galaxies very similar to stars on photographic plates, having a compact structure, a high surface brightness and weak radio emission or no emission at a l l . It was therefore necessary to distinguish between Q S R s (Quasi Stellar R a d i o sources), w i t h radio emission,  CHAPTER  1.  INTRODUCTION  3  and Q S O s (Quasi Stellar Objects), w i t h little or no emission [13]. It was eventually realized how important these "new" objects were, not only as peculiar sources (as described i n §3, they radiate an amount of energy around 1 0  53  - 10  55  Joule from regions as small as the Solar System), but  also for their role i n cosmology and for what could be understood about host galaxies, and the nature and properties of the material between the observer and the source. T h e small angular size and faintness of Q S O s is a challenge for their detection: the faintest objects can be investigated only by the largest telescopes, characterized by a greater light-gathering power and a superior resolution. T h e light-gathering power of a telescope depends on the objective's area: the bigger it is, the more light is collected i n a given time yielding bright images of even distant objects. T h e telescope's resolving power is the ability to reveal fine details: according to the formula  0 = 1.22^  (i.i)  where 8 is the angular distance between two bodies, A is the observation wavelength and D is the primary mirror diameter, the resolving power can be improved either by increasing the diameter D or reducing the wavelength A. Since the optical wavelength range of the electromagnetic spectrum spans from 4500 A to 9500 A, progress has come from larger diameters. F i g 1.1 shows the evolution of telescope's aperture over time: the diameter has increased slowly w i t h time due to the difficulty of producing accurate optics (both i n terms of casting the primary mirror substrate and of polishing  CHAPTER  1500  1.  INTRODUCTION  4  1600  Figure 1.1: E v o l u t i o n of telescope aperture over time.  it). Sophisticated technology has overcome this problem, but more time has been necessary to improve the ground-based optical telescope resolution, proportionate to the diameter of the primary lens or mirror. Images produced by  CHAPTER  1.  INTRODUCTION  5  large telescopes suffer various problems because of the E a r t h ' s atmosphere. Its turbulent nature causes density changes over small distances, creating regions where the light is refracted i n nearly random directions; i n this situation the image of a point source is effectively blurred. Correctors to remove optical aberrations and adaptive optics to counter atmospheric turbulence are now functioning on most modern telescopes. T h o u g h the liquid mirror telescope ( L M T ) technology still needs developments, the optics production and other factors (such as the mount, required to support telescopes often heavy and to move them w i t h accuracy) are not an issue and this fact and other advantages, such as the low cost w i t h respect to a conventional glass telescopes, make it a really interesting and alternative technological project, described i n detail i n chapter §2.  1.1  Recent surveys and methods to find quasars.  T h e scientific goal of this thesis is to find quasars using a novel method which is applied to a database of objects observed w i t h the N A S A L M T . Beside a detailed description of the method, it is also useful to compare this survey to those which characterized the last decade or earlier, w i t h particular attention to the Sloan, and discuss briefly how they selected quasar candidates. Table 1.1 shows the area, the epoch, the l i m i t i n g magnitude, the spectral coverage and the number of objects found i n the most important optical surveys. T h e L M T - N O D O survey is explained i n detail i n §2; nevertheless some  CHAPTER  Survey  Ref.  Epoch  1.  6  INTRODUCTION  Area  magn  AA  quasars  A  (deg ) 2  MBQS  [52]  1978-1981  109  B17.6  3500-7000  32  PBQS  [63]  1981  10714  B16.2  U,B  114  PTQS  [65]  1985-1989  61  R22  4400-7500  232  LBQS  [34]  1986-1989  454  B19  3400-5100  1058  HNQS  [32]  1988-1998  14000  R18.6  3600-6500  376  HBQS  [18]  1989  153  B18.8  U,B,V,R  284  AAT  [11]  1989-1990  1.6  B21  3500-6400?  420  EQS  [27]  1989-1997  330  B18.5  U,B,V,R,I  224  AQS  [28]  1990  14019  R15.4  B,V,R  46  HSQS  [31]  1990-1994  10000  B17.5  3200-5400  160  FQS  [12]  1991  0.9  B22  U^^r/.I  66  APM1  [71]  1991-1995  2500  B19  B,R,I  31  SA94  [17]  1992  10  B19.9  U,B,V  200  LMT  [39]  1996-1999  20  B21.5  4500-9500  ~ 3000  TQS  [64]  1997  20  B21.3  U,B,V  368  2dF  [68]  1997-2002  740  B20.9  U,Bj,R  25000  SDSS  [79]  1998-...  10225  g23.3  4000-7500  10  APM2  [72]  1999-2000  5500  B20.8  Bj,R,I  31  Table 1.1:  5  A r e a covered, epoch, magnitude, A A and number of expected or detected quasar candidates for the most recent optical Q S O surveys.  CHAPTER  1.  INTRODUCTION  7  points can be made here. Compared to these surveys, the l i m i t i n g magnitude reached by the U B C N A S A M u l t i b a n d Survey ( U N M S 1 ) catalog (21-22 mag i n the most sensitive bands) is inferior only to those of the Palomar Transit (22 mag), Faint (22.3 mag) and Sloan D i g i t a l (23 mag) surveys. T h i s means these surveys include fainter objects than those observed by the liquid mirror telescope. Considering the sky area covered, regions observed by the L M T - N O D O survey overlap those studied by the Sloan and the H a m b u r g N o r t h surveys. Therefore, observations taken w i t h the liquid mirror telescope are matched only by those of the Sloan survey. T h i s fact, which could t u r n out useful since it can provide an independent confirmation of quasar candidates selected w i t h the method described i n §4, is not surprising: the Sloan D i g i t a l Sky Survey (SDSS) [79] is i n fact one of the biggest surveys and quasar study (involving m a i n l y quasar clustering effects, Q S O evolution and association w i t h galaxies ) represents only one of the many fields of investigation ( w w w . a s t r o . p r i n c e t o n . e d u / P B O O K / science/quasars.htm). Inside the group of 113 Q S O s found so far, 27 have redshift 3.5 < z < 4.5 and magnitude 18.55 < m ; < 20.97, 4 have redshift z > 4.95; more than 100000 quasars are expected to be detected when the survey is completed. T h e method adopted to select quasars varies according to the redshift: at low redshifts ( z < 2.2), the lack of a detectable B a l m e r j u m p i n spectra helps to distinguish between Q S O s and stars; at higher redshifts, the presence of the strong emission line Lya and the absorption by the L y forest cause the broadband colours of quasars to become increasingly redder w i t h redshift. T h e H a m b u r g survey ( H Q S ) [32] for the Northern sky and the first A P M U K S T colour survey  CHAPTER  1.  8  INTRODUCTION  ( A P M 1 ) [71] base their selection process on these absorption systems as well. A t 3.5 < z < 5, quasars and stars occupy different zones of the  g'r'i'z'  colour space (modified T h u a n - G u n n system, see transmission curves i n F i g . 1 of [29]) but at z > 5 the quasar track approaches the red end of the stellar locus i n the r'i'z' diagram and new discriminators are required. T h e selection method is then based on three regions i n S D S S colour space: • r' - V > 1.35 and i' - z' < 0.3; • r' - i' > 2 and V - z' < 0.7; • z-band detection only, i.e.  z' < 20.8 and the detection i n the other  bands is below 5<7. In addition, an object must be classified as a point source by the S D S S processing software and have z' < 20.8. Surveys listed i n Table 1.1 use other methods to select quasar candidates. T h e A S I A G O - E S O / R A S S Q S O survey ( A Q S ) [28], derived by merging the R A S S (Rosat-All-Sky-Survey) [76], G S C (Guide Star Catalogue) [47], U S N O (United States N a t i o n a l Observatory) [54] and D S S (Digitized Sky Survey: http://arch-http.hq.eso.org/dss/dss) catalogs, found quasars according to their point appearance and a magnitude criterion: 11 < VGSC < 14.5 and 13.5 < R NO US  <  15.4.  C o m m o n optical selection criteria are based on colours: i n the two-colour diagram showed i n F i g . 1.2, quasars are always much brighter i n the ultraviolet than m a i n sequence stars w i t h the same B-V JJ-B=-0.4  is used to separate these two categories.  colour and the threshold  CHAPTER  1.  INTRODUCTION  9  Figure 1.2: Two-colour diagram: quasars (points) are well separated by m a i n sequence stars (line w i t h stellar types BO - MO) (http://ned www.ipac.caltech.ca/Cambridge/Cambridge l_3_3.html).  T h i s method, used i n the Palomar B r i g h t Q S O survey ( P B S Q ) [63], the M e d i u m B r i g h t Quasar survey ( M B Q S ) [52], the Homogeneous bright Q S O survey ( H B Q S ) [18], the S A 94 Q S O survey (SA94) [17] and the AngloAustralian-Telescope survey ( D U R H A M / A A T ) [11], must be used carefully as that quasar region inside the two-colour diagram is also well populated by white dwarfs that, at B magnitude smaller than 16, are much more numerous than quasars. Other surveys rely on the red excess: the spectroscopic survey of faint Q S O s ( F Q S ) [12] and the second A P M U K S T colour survey ( A P M 2 ) [72]  CHAPTER  1.  INTRODUCTION  10  (for which only sources w i t h Bj — R > 2.5 are considered quasar candidates), are an example. Techniques w i t h more colours can be more efficient: besides U-B and B- V colours, U-J and J-F (where J is at 12500 A and F is at 6250 A) are used. Quasars w i t h redshift higher than 2.2 have a colour J-F bluer than stars w i t h the same U-J colour. T h i s method is adequate to find objects no more distant than z ~ 3.2. M u l t i c o l o u r techniques w i t h broader bands, already mentioned for the S D S S , are efficient to identify more distant quasars: this is the way i n which the 2dF Q S O Redshift survey (2dF) [68] (http://www.2dfquasar.org) operated as well. Other research methods are based on broad lines: the spectrum of a quasar, w i t h strong emission features, is very different, at low resolution, from that of red stars and galaxies. A G r i s m (grating-lens system), characterized by a prism objective associated to a diffraction grating, allow simultaneous measurements of low resolution spectra for many objects. T h i s led to the discovery of quasars at redshift higher than 4 [22]. T h i s is the case of the Large Bright Q S O survey ( L B Q S ) [34] and the Palomar Transit G r i s m survey ( P T G S ) [65]. Other possible methods to identify quasars could rely on their variability or on the absence of proper motions. These methods require long exposures on different time scales to separate possible candidates: the Tautenburg survey ( T Q S ) [64] worked i n fact on Schmidt plates taken over the last three decades. Recently, the P r i n c i p a l Component Analysis approach ( P C A ) has resulted  CHAPTER  1.  INTRODUCTION  11  in successful classification of objects such as stars, galaxies, Q S O s etc. from multi-band photometry, using mock catalogs which match as closely as possible the observations of the Large Zenith Telescope ( L Z T ) , mentioned i n §2 [15]. T h e P C A is a non-parametric approach already employed w i t h multicolour photometry (generally fewer than 10 colour bins) or w i t h medium to high-resolution spectroscopy, but it was not tested on spectral energy distribution ( S E D ) w i t h R ~ 40. T h e P C A methodology can be summarized i n this way: from a set of N vectors "f" w i t h M elements (where M is the number of filters) and each of them normalized to have unit scalar product, the P C A derives a set of orthogonal eigenvectors e\j , using criteria of decreasing m a x i m u n variance of the spectra when projected onto the eigenvectors. T h e n , vectors fxi are the linear combinations of eigenvectors e\j (where e\ is the mean vector over f\i,  e  2  lies i n the direction of the highest variance orthogonal to d , etc.)  and eigencomponents  which are the weight of the "j " th  eigenvector i n the  "«**" vector. T h e main advantage of this approach is that when the vectors f\i  are  correlated (as it is for astronomical S E D s ) , most of the classification power of the linear combination mentioned above is carried by the first 10 eigenvectors (ei, ... eio) when describing a set of objects of different classes and at different redshift intervals. In order to use the P C A for spectral classification, one must relate the internal correlations outlined by the P C A w i t h the physical properties of the objects: it is necessary then to apply the P C A to mock catalogs of templates and extract important information for classying objects and measuring their  CHAPTER  1.  INTRODUCTION  12  redshifts. T h i s was the approach chosen by [15] : once the principal eigencomponents of the catalog are measured, the first 10 eigenvectors are used to defined a 10-dimensional eigenbasis, i n which each class of objects occupies a given locus, defined by its spectral type among its class, and its redshift for galaxies and quasars. For a median signal-to-noise ratio of 6, 98% of stars, 100% of galaxies and 93% of quasars are correctly classified. T h i s technique, tested only on simulated observations so far, is going to be calibrated on the same data studied i n this thesis.  CHAPTER  2. LIQUID MIRROR  TELESCOPES.  13  CHAPTER 2 LIQUID M I R R O R TELESCOPES. 2.1  Liquid mirror telescopes in the past.  T h e idea of a liquid mirror telescope dates back to 1850, when a scientist at the Italian Observatory of Naples, Ernesto Capocci, thought to use for astronomical observations a well known physical concept according to which the equilibrium configuration of a liquid surface, which rotates uniformly, assumes just a paraboloid shape w i t h a focal length /  (2.1) where co is the rotation angular frequency, g is the gravitational constant. Capocci realized that a highly-dense liquid, such as mercury, might be used as a reflecting surface. The New Zealander Henry Skey, from the observatory of Dunedin, was the first to build such an instrument i n 1872. Unfortunately, the fact it couldn't point and track objects, and complications related to a liquid such as mercury, prevented this k i n d of telescope from being useful for astronomical research until a century later. Here is a brief list of the most important liquid mirror telescopes built i n the past or still i n the development phase [25]:  CHAPTER  2.  LIQUID  MIRROR  TELESCOPES.  14  • i n 1992, E . B o r r a created the first rotating mercury mirror, 1.5 m i n diameter, that was able to produce diffraction limited images: it was followed by a second one w i t h a diameter of 2.5 m; • i n M a r c h 1994, a 2.7-m telescope designed and built by P . Hickson was used for astronomical observations. Its resolution was limited only by the atmospheric seeing. It was equipped w i t h a C C D operating i n T D I mode [36], [37], [38]; • i n the Spring of 1996, the 3-m Nasa O r b i t a l Debris Observatory ( N O D O ) built by M . Mulrooney and P. Hickson began operation; • i n 1997, a 3.7-m liquid mirror was completed by B o r r a [9]: its optical testing is on its way; • the 6-m Large Zenith Telescope ( L Z T ) was recently built by P. Hickson near Vancouver, B r i t i s h Columbia: it w i l l be operational before the end of 2002. One of the most ambitious projects for future L M T investigations is L A M A , the Large-Aperture M i r r o r A r r a y :  it is an optical interferometry project  based on an array of 18 fixed 10-meter liquid-mirror telescopes, covering a circle of 60-m diameter and w i t h an effective aperture ~ 42m. Likely located either at an altitude of 5000 meters i n Cerro Chajnantor, i n Northern Chile or at an altitude of 2800 meters i n the Sacramento Mountains of New Mexico, this instrumentation w i l l produce distortion-free diffraction-limited images over a 1 arcmin instantaneous field of view, w i t h pointing and tracking as far as 4° from the zenith ( h t t p : / / w w w . a s t r o . u b c . c a / L M T / l a m a / i n d e x . h t m l ) .  CHAPTER  2.2  2. LIQUID  MIRROR  TELESCOPES.  15  The NODO liquid mirror telescope.  T h e data analyzed and discussed i n this thesis were obtained from the N O D O liquid mirror telescope i n the state of New Mexico, i n the Sacramento mountains, at 2756 meters of altitude. T h i s 3-m mirror w i t h a 4.5-m focal length was built by P.Hickson and M . Mulrooney for N A S A . T h e principal objective was to characterize orbital debris, w i t h a second goal of astronomical research [57]. The most important component of the entire structure is the mirror support: a styrofoam core enclosed by K e v l a r . T h i s material was chosen because of its light weight, high stiffness, and good dampening characteristics. T h e K e v l a r surface is covered by a layer of polyurethane, created by spincasting. T h e final surface, which is parabolic to w i t h i n a fraction of a millimeter, is then covered by 1.5 m m of spinning mercury. A t h i n oxide layer forms over the surface after the mercury is exposed for a few hours to air and it has the advantages of dampening surface waves and eliminating the evaporation. T h e reflectivity of M g is i n the range 70 - 80%. T h e structure is able to support 170 kg of mercury without vibration and w i t h a m a x i m u m deviation from a paraboloid around 0.1 m m . T h e deviation of the mercury surface from a perfect parabola is approximately A / 2 0 , where A is the wavelength. To achieve this performance, there are two fundamental  requirements:  a stable rotational velocity and the rotation axis alignment parallel to the gravitational field of the E a r t h . The first requirement is obtained using a synchronous motor which drives the mirror support: the rotation period is approximately 6 seconds. The second requirement is achieved by employing a precise air bearing and a three-point mount w i t h which the rotation axis  CHAPTER  2.  LIQUID  MIRROR  TELESCOPES.  16  can be aligned to within 0.25 arcseconds [9]. The basic mirror setup is shown in Fig. 2.1. Container  A i r compresser + Drier  Figure 2.1: Components for a liquid mirror.  The telescope structure consists of a steel tripod 5.8 meters high which supports correcting lenses, filters, an alignment system, focus mechanism and a C C D camera. The corrector contains 4 elements which reduce the optical aberrations (especially field distortions) to less than 1 arcsec over a 0.5° field at infrared  CHAPTER  2. LIQUID  MIRROR  TELESCOPES.  17  and optical wavelengths. T h e N O D O L M T ' s basic layout is similar to that shown F i g 2.2 for the U B C - L a v a l 2.7-m liquid mirror telescope: m a i n differences are i n the absence of the brace at half the height and of the drive belt. T h e drive system includes a motor stator (which is mounted directly on the air-bearing base), a rotor (which is attached to the rotating spindle) and an optical encoder (which senses the angular velocity).  2.3  D a t a collection.  A front illuminated 2048x2048 pixel L o r a l C C D (for 1996-1997 observations) and a back illuminated 1024x1024 S I T E C C D (for 1999 observations) are the detectors used for this survey.  The first chip, for which the light  enters through t h i n polysilicon gates of the parallel register, is transparent to long wavelength and opaque for wavelengths shorter than 4000 A. T h e second device, for which the light enters through the thinned backside of the C C D register where there is no gate structure, exhibits high sensitivity to light up to the near-infrared regions of the spectrum due to its thicknesss reduced to 10 fxm. B o t h devices don't show a good response i n the blue. E a c h pixel has a size of 15 pm. w i t h an image scale of 0.6 arcsec/pixel for the first detector, and a size of 24 fj,m w i t h an image scale of 0.96 arcsec/pixel for the second detector, i n right ascension and declination. T h e C C D is situated inside a Dewar and is cooled thermically to a temperature of -30°C, reducing the dark current level. D u r i n g observations, the C C D is scanned continuously i n the time-delay  CHAPTER  2.  LIQUID  MIRROR  air bearing  TELESCOPES.  18  stand  Figure 2.2: Basic optical layout for the U B C - L a v a l 2.7-m liquid mirror telescope.  CHAPTER  2. LIQUID  MIRROR  TELESCOPES.  19  integrate ( T D I ) mode [51]. In this operation, the charges accumulated by the C C D are moved across it at the siderial rate. T h e effective integration time is around 97 seconds for the 2048x2048 C C D and 78 seconds for the 1024x 1024 C C D . T h i s represents an object's cross i n time along the chip due to our planet's rotation. D a t a are accumulated continuously at a rate of ~ 50 K b / s e c for a total of 1.8 G b each night. Software written by P . Hickson supplies interactive control of data acquisition parameters and a continuous display of image data; zoom and contrast controls on the display allow one to monitor the focus and to check image quality during observations. T h e L o r a l C C D is connected to a photometric controller which, being designed for high-speed operation (necessary for satellite debris observations), has a relatively-high read noise (28e~); for the S I T E C C D the read noise is of l i e . T h i s fact represents a limit for the detector, particularly at short -  wavelengths where the sky is not so luminous and the C C D does not have a good response [39]. T h i s fact could influence the study of quasars i n this thesis since the selection process is based on the comparison of the observed source and templates: if features i n the blue are missed or not very accurate, it is likely to misclassify the source.  2.4  L M T advantages.  For clear reasons of construction, the L M T is a non-steerable instrument and it observes objects inside a narrow strip of sky passing through the zenith. Here we can find the first advantage: only zenith observations minimize the atmospheric extinction and refraction, allowing one to observe  CHAPTER  2. LIQUID  MIRROR  TELESCOPES.  20  w i t h the best seeing. T h e increasing interest i n L M T s is justified by numerous practical advantages w i t h respect to conventional telescopes: a l l the challenges related to the mounting, which supports most of the telescope weight, and to the trailing of celestial bodies, are here eliminated. T h e simple general design of the entire instrument simplifies maintenance. Dust can be removed from the mirror surface i n less than a hour loosing a negligible amount of mercury: this allows one to use the telescope continuously w i t h the opportunity of observing the variability of certain sources night by night. L M T s are well-suited for many different kinds of surveys, from large-scale structure to galactic evolution, galaxy luminosity function evolution, Q S O observations, supernovae rates, gravitational lenses and cosmology. A l l these research topics would be difficult to study continuously w i t h conventional telescopes. Most of the success of this type of instrument is due also to its low cost: an order of magnitude lower than a conventional telescope w i t h the same aperture.  2.5  L M T disadvantages.  Since the liquid mirror rotates around a vertical axis, the field is limited to ~ 0.5° square near the zenith and, thanks to the terrestrial rotation, the survey studies a narrow strip of sky, whose extent depends on the integration time.  CHAPTER  2. LIQUID  MIRROR  TELESCOPES.  21  The mechanical disadvantages, such as the lack of tracking and directed pointing, might be overcome i n the near future using correctors w i t h movable optics [42], correctors w i t h movable mirrors [8], introducing magnetic particles into the liquid and deforming its configuration through magnetic fields or using a viscous liquids, covered by a very t h i n reflecting film so that the instrument can point a few tens of degrees off the zenith without altering its parabolic shape [10]. Star-trail curvature, which affects observations away from the celestial equator, can be distinguished from other problems since these trails are lightly concave, to N o r t h direction, about an amount which depends on the size of the C C D and is practically constant over the entire field. A t N O D O latitude, the peak-to-edge curvature is around 3.3 arcsec. T h i s problem could be avoided by using a corrector having tilted and decentreted lenses [40]. For this telescope, the curvature produced by the rotation of the field is smaller than the average seeing, around 2 arcsec. T h e field distorsion, experienced by the N O D O telescope w i t h its original 3-elements corrector (later replaced by a low distortion 4-element design), is a more serious issue: it causes an increasing degree of image smearing from the center of the field to the extremites N o r t h and South (up to 2 degrees). Just for this reason the survey is limited to 75% of the field, starting from the center [39]. Different factors must be accounted for the image degradation: seeing, instrumental aberrations, and the T D I observation mode. Aberrations of a parabolic mirror depend.on the field angle: using the corrector, they are smaller than 1 arcsec, negligible w i t h respect to the see-  CHAPTER  2. LIQUID  MIRROR  TELESCOPES.  22  ing. T h e final contribution to the image aberration comes from T D I : this observation mode is necessary since the instrument does only zenith observations and then it is fundamental that images of a l l the sources drift at the same rate on parallel linear tracks, otherwise there w i l l be an image spread. T h i s requirement is challenged by the star-trail curvature mentioned above and the siderial rate varying w i t h declination: there appear errors dependent on the latitude and increasing proportionally to the square of the angular field of view. Since the magnitude of the effect is typically 1-2 arcsec, it is important to use 4-optical-element correctors at the prime focus [40].  2.6  L M T technical issues.  A s already mentioned, the layout of this telescope is very simple, but this doesn't mean that the telescope is free from technical difficulties, some of them already solved and other i n search of a solution. Besides the technical problem to give a uniform rotation to the mirror, today solved w i t h a modern drive system, a real difficulty is the formation of surface waves, which degrades the image quality. Such waves can result from two causes: vibration transmission from the floor to the mirror; inaccuracy in leveling the mercury (avoided w i t h a better alignment between the mirror rotation axis and the gravitational field) and an unstable rotation speed. Some optical tests carried out at Laval University on the quality of a mercury surface [3] showed that the total amplitude of ripples is between A / 1 0 and A / 1 5 at 5000 A.  CHAPTER  2.  LIQUID  MIRROR  TELESCOPES.  23  Vibrations can be caused by wind as well: the mercury surface doesn't tolerate turbulent local air velocities, stronger than 12 m / s . T h e telescope enclosure must therefore provide wind protection.  2.7 2.7.1  Data analysis. Object detection and photometry.  Before data analysis, it is necessary to subtract the dark current and the sky, and to correct for response variations (flat  field).  T h e dark current is corrected using data accumulated i n T D I mode w i t h the C C D covered; the flat field is obtained from observations on nights w i t h moon-brightened cloudy skies. Cosmic rays and stellar images are removed by median  filtering.  Once these corrections are done, consecutive C C D rows are put together in blocks of 2048 overlapping lines: the sky subtraction is then obtained by subtracting the mode (the most common value inside the distribution) of each line and row. After this procedure, since the good uniformity of C C D images, systematic background variations are lower than 0.02%. T h e object detection and the photometry are done on individual data blocks: i n order to diminish the noise, the object identification is conducted on a smoothed image copy, obtained using a 5 x 5 pixel boxcar filter which provides a five-fold reduction i n background noise. In each block, the background mean standard deviation is determined eliminating iteratively the stellar images row by row: then, a l l the pixels w i t h a value exceeding the mean by 2.5 standard deviation are marked, areas of contiguous pixels are  CHAPTER  2. LIQUID  MIRROR  TELESCOPES.  24  identified and the outline of each of them is considered the object isophote. For sources inside the image, the first three moments about the intensity distribution are determined w i t h i n the isophote considered: - the zero-order moment, the isophotal flux; - the first-order moment, from which the image coordinates can be obtained; - the second-order moment, from which the inertia tensor is determined and whose eigenvalues give the major axis, the minor axis and the position angle of the intensity distribution. A l l the measurements are done on the un-smoothed image and inside the area of the object isophote determined from the smoothed image. Since raw isophotal magnitudes are not realistic for d i m objects (an i n creasing fraction of light falls outside the isophote and adding flux from outside the same isophote increases the measurement noise), a correction is applied to these magnitudes depending on the flux and on the mean intensity inside the isophote registered. T h i s correction was calculated assuming the relationship between the intensity and the flux is the same as for a Gaussian intensity profile, which is a good approximation to seeing-altered profiles of the faintest galaxies i n the image. In order to identify and separate objects whose images overlap, the detection algorithm is repeated w i t h increasing detection thresholds of surface brightness: at each step, the area inside the isophotal outline of an object previously detected is examined. T h e isophote level is increased u n t i l either the object no longer reaches the threshold, or it breaks into two parts. In the latter case, the total flux and the isophotal flux of the "initial" source is divided between the two parts, proportionally to their isophotal fluxes.  CHAPTER  2. LIQUID  MIRROR  TELESCOPES.  25  For data obtained w i t h this telescope, the fraction of objects which suffer blending is around 1%. T h e photometry program produces a list of a l l the sources recorded i n one night of observation: instrumental magnitudes, positions, errors, image parameters and the full-width at half-maximum intensity ( F W H M ) . Instrumental coordinates were obtained by applying corrections for aberration and nutation of the coordinates of the image centroids, and then referring these value to the standard epoch (J2000).  2.7.2  Astrometric and photometric calibration.  For the astrometric calibration process the U S N O A l stellar catalog, produced by the U S Naval Observatory for the Precise Measurement Machine Project [55], was used. The first stage is to find a number of astrometric reference stars from the astrometry file and then to fit the astrometric data using the nearest calibration star. Astrometric fitting equations i n right ascension and declination are based on 7 parameters including offsets and linear and quadratic scale factors for b o t h the coordinates. T h e resulting coordinates have typical errors around 0.3 arcsec, b o t h i n right ascension and declination. The photometric calibration was conducted using spectro-photometric stars inside the survey  field.  These were calibrated by observations con-  ducted at the Kitt Peak National  Observatory: 22 stars were chosen to cross  the telescope field every 30 minutes, producing a calibration accurate to w i t h i n ~ 5 % at every wavelength [41]. For each band, the product between the filter transmission curve and the  CHAPTER  2. LIQUID  MIRROR  TELESCOPES.  26  specific flux for the standard star is integrated to give the magnitude zero point i n that band. To account for possible sky transparency variations during the observation, a second order polynomial is fit to the magnitude zero points from the standard stars and applied to the instrumental magnitudes to give the calibrated values for a l l the objects. A B magnitudes are used, defined i n this way:  m „ = - 5 6 . 1 0 - 2.5log{f )  (2.2)  v  where f„ is the average specific flux for the filter passband i n W m~  2  Hz'  1  [39].  2.7.3  The object catalog.  In order to obtain the final catalogue of objects, it is necessary to merge the photometry files of a l l the nights for which the same filter was used. T h e y were identified as objects a l l those detected on more than one night (in order to reject cosmic rays and spurious detection of noise), having a difference of magnitude smaller than 1 and the same position to w i t h i n an error smaller than 3.5 arcsec. For each night the magnitude error is estimated from the object flux, the isophotal area and the background variance. The average magnitude is determined weighing the magnitude for each night by the reciprocal variances. T h e same weights are then used when the average values for other photometric and astrometric parameters are calculated. T h e final photometric errors are calculated easily: for each source, the  CHAPTER  2.  LIQUID  MIRROR  TELESCOPES.  27  variance of the mean is found from the magnitude variances and this allowed the estimation of the random noise. T h e total error, the sum of random and systematic one, is estimated by calculating directly the variance i n the magnitude obtained on different nights. T h e highest of this estimation is kept for final record. The entire procedure gives a single photometric file for each band and then, i n order to obtain the S E D (Spectral Energy D i s t r i b u t i o n ) , the final files correspondent to all the bands must be grouped.  2.8  Characteristics of the survey.  T h e survey was conducted i n different narrow bands covering 20.13° square of sky, centered at the declination of + 3 3 ° and at right ascension from lOhr to 18hr. T h e observation i n a band is done by inserting a glass filter between the corrector lenses and the C C D window.  A filter is positioned at the  beginning of the observation and used for all the night. T h i s survey is based on observations taken i n 1996, 1997 and 1999 using 35 narrow band-filters covering 454 - 993 n m , and 4 broad band-filters: 5(440 nm), 7(550 nm), #(700 nm), 7(900 nm). These filters, being broader than the narrow ones, reach a fainter l i m i t i n g magnitude and they make easier the comparison w i t h other broad-band photometries. Table 2.1 shows the characteristics for narrow filters. F i g 2.3 displays their transmission curves.  T h e fact blue filters have  lower transmission curves (a lower percentage of photons is received at wave-  CHAPTER  \  a  2.  LIQUID  MIRROR  TELESCOPES.  Alog(u)  ta  28  o  AA  948  947.7  39.08  14.5003  0.019  0.933  36.43  925  924.5  40.04  14.5111  0.019  0.928  36.96  906  906.3  35.32  14.5198  0.018  0.900  31.71  883  883.1  41.28  14.5311  0.021  0.924  38.10  868  867.9  35.10  14.5388  0.018  0.952  33.38  844  843.8  35.58  14.5509  0.019  0.932  33.09  825  824.8  33.67  14.5608  0.018  0.950  31.96  806  805.9  34.62  14.5709  0.019  0.936  32.27  788  787.5  33.31  14.5809  0.019  0.927  30.85  770  769.6  31.86  14.5910  0.018  0.937  29.79  752  752.4  33.25  14.6008  0.019  0.955  31.72  735  734.7  32.17  14.6111  0.019  0.940  30.17  719  718.7  30.54  14.6208  0.019  0.954  29.13  704  704.4  29.88  14.6293  0.019  0.930  27.78  688  688.0  29.20  14.6397  0.019  0.936  27.30  671  671.3  29.08  14.6503  0.019  0.933  27.10  655  654.6  27.99  14.6612  0.019  0.930  26.03  641  641.1  23.98  14.6705  0.016  0.919  21.99  629  628.7  26.39  14.6789  0.018  0.952  25.10  614  613.7  23.62  14.6893  0.018  0.910  21.45  598  597.6  24.31  14.7010  0.018  0.717  17.41  586  585.6  23.10  14.7099  0.018  0.720  16.63  571  571.1  21.71  14.7207  0.017  0.750  16.26  ID  A  log{v)  6  c  d  Table 2.1: Continue.  l  e  wf  CHAPTER  2. LIQUID MIRROR TELESCOPES.  \ o  AA  557  557.0  21.35  14.7314  0.017  0.707  15.05  545  545.1  21.00  14.7409  0.017  0.726  15.22  533  532.7  22.76  14.7505  0.019  0.730  16.59  519  519.0  22.72  14.7609  0.022  0.679  15.38  510  510.2  22.36  14.7698  0.019  0.689  15.39  498  498.1  21.91  14.7798  0.019  0.670  14.66  486  486.0  20.22  14.7904  0.019  0.752  15.18  476  475.6  19.30  14.7998  0.018  0.690  13.31  466  465.9  18.48  14.8090  0.018  0.673  12.42  455  454.5  17.67  14.8196  0.018  0.632  11.17  ID  a  A  6  log(v)  c  Alog(v)  d  to e  l  29  wf  Table 2.1: F i l t e r specifications, (a) mean wavelength (nm), from transmission curve; (b) bandwidth (nm): equivalent w i d t h / c e n t r a l transmission; (c) log of central frequency (Hz): c/mean wavelength; (d) log frequency bandwidth: 0.434 x b a n d w i d t h / m e a n wavelength; (e) central transmission; (f) equivalent w i d t h (nm): integral of transmission curve. lenghts shorter than 600 nm) and the exposure time is the same for each filter, influence signal-to-noise ratios i n such a way that, for blue bands, the signal-to-noise ratios result lower than those of red filters. E a c h filter was used on more than one night i n order to recognize cosmic rays, to create an independent estimate of the photometric accuracy and to improve the object signal-to-noise ratio. T h e sequence of the filter observations depended on lunar phase. In order to minimize the effect of the moon  CHAPTER  2. LIQUID  MIRROR  TELESCOPES.  30  on the sky brightness, the red and infrared bands were used when the moon was bright and the blue filter were used when the moon was new.  2.8.1  Photometric selection criteria.  T h e recorded objects are limited by the surface brightness, by the apparent magnitude and by the angular separation. In order to distinguish an object, the surface brightness must exceed the detection threshold for a m i n i m u m number of connected pixels corresponding to a m i n i m u m area of A  = 1.788 arcsec . Then, i f / a n d i are respectively 2  m  the flux and the median intensity inside the isophote, the first selection criterion is  f/i > A .  (2.3)  m  To record the object  ~i > i  (2.4)  m  where i is the detection intensity threshold: to maximize the number of dem  tected objects, i is set to the lowest possible level allowed by the background m  noise, which is generally dominated by the sky light and varies according to the lunar phase. T h e formula 2.4 represents the second criterion. T h e t h i r d criterion requires a m i n i m u m signal-to-noise ratio £. T h e noise is due to both the image's Poissonian noise and the background noise, then  / > C (g + ° l~i) 2  2  (2.5)  CHAPTER  j  i  i  2.  LIQUID  I i i i l_i  i  i  MIRROR  l i i i l i i i  8 0 9 0 K0 Z d uo L s s i uusue J +  TELESCOPES.  31  CHAPTER  2. LIQUID MIRROR TELESCOPES.  32  where a is the background noise variation and g is the system gain (signal produced by a single photo-electron). T h e l i m i t i n g magnitude for each block of survey data can be estimated from the surface brightness, by the m i n i m u m area required to find the object and by the seeing F W H M . T h i s function can change on short time scales since it depends on atmospheric conditions.  CHAPTER  3. QUASI  STELLAR  33  OBJECTS.  CHAPTER 3 QUASI S T E L L A R O B J E C T S . 3.1  QSO's fundamental characteristics.  Schmidt described the optical properties of quasars in 1964 in the following way [13]: - starlike objects identified with radio sources; - variable light; - large UV lines in spectra; - broad emission lines in spectra; - large redshift of the spectrum. Since then, many surveys have been completed at different frequencies and much has been learnt about quasars. Now we know that even though quasars werefirstfound as radio sources, only 10% of those optically selected are radio loud, with a power higher than 10  26  W Hz' . 1  Their radio structure is not so different from that of giant  ellipticals. Their X emission is very strong as well, L  ~ 10 —10 W 21  x  23  Hz'  1  [22]. Since the liquid mirror telescope works in the optical range, it is useful to describe quasar optical properties in detail. In general the optical luminosity is extremely high: typical values are around L  opt  ~ 10  22  — 10  24  watt Hz'  1  which corresponds to an absolute  CHAPTER magnitude M  o p <  3.  QUASI STELLAR  34  OBJECTS.  ~ -11 - -28 (to make a comparison, the absolute magnitude  of giant ellipticals is -22 - -23). T h e optical variability, from weeks to a few months, is of few tenths of magnitude. F r o m that, it is possible to calculate the linear scale implied by the duration of this variation through this formula:  D < c r = c r / ( l + z) 0  (3-1)  where r and r are respectively the time variation i n the quasar's and i n the 0  observer's frame. It is found that a l l the energy comes from a region smaller than 10  16  — 10  17  c m i n diameter; this situation can be explained only by  means of relativistic effects. T h e general trend for a quasar spectrum beyond the optical range is shown i n F i g . 3.1. It is characterized by a continuum plus strong absorption and emission lines. The continuum is well approximated by a power law  f <xu  a  v  (3.2)  where / „ is the monochromatic flux at frequency v and a is the spectral index: different values of this parameter were found from surveys, but the most common range is [-2, 0.8] as we describe i n §4. In the optical region the continuum tends to flatten and it shows a more or less pronounced " U V B u m p " or " B i g Blue B u m p " , a real deviation from the power law. It has been proposed by Shields [69], that the blue bump might be due to thermal emission from the surface of an optically thick but geometrically t h i n accretion disc which surrounds the central source. T h e emitted spectrum is then the superposition of black bodies of temperatures decreasing from the inner  CHAPTER  3.  QUASI STELLAR WAVELENGTH  10.0  J  13.5  (MICRONS)  3.0  :  LA  14.0 LOG  35  OBJECTS.  1.0  i  L_  14.5 FREQUENCY  0.3  15.0 (Hz)  Figure 3.1: General layout for a quasar spectrum.  radius to the outer radius of the disc. M a n y features of this emission could be explained by accretion disc models which are based on possible matter accretion w i t h some angular momentum, provided that an adequate source of viscosity is available to transport this angular momentum towards the outer regions of the nucleus [69]. Table 3.1 lists the most important quasar emission lines i n the U V and  CHAPTER  3.  QUASI STELLAR  OBJECTS.  36  optical range. These lines are so important to identify quasars that i f a few of them are missed, since the method is based on the comparison between the observed S E D and models, it is likely to misclassify objects. M a n y lines which fall i n the U V range are detectable from optical instruments only at high redshift, according to the relation  Kbs  = A  e m  ( l + z)  where A ;, is the observation wavelength, A 0  s  e m  (3.3) is the rest frame wavelength  and z is the redshift. Besides numerous and strong emission lines, absorption features are also observed. Since they are caused by material between the observer and the luminous source along the line of sight, the absorbing material has a redshift smaller the the quasar. For the same object, three categories of absorption lines can be found: - metallic  systems:  groups of two or three lines at wavelengths very  close to each other and w i t h the same redshift.  The most common are  CIVA1548/1551 A and M g IIA 2796/2803 A but also C , N , 0 , S i , S, A l and Z n systems can be present. - the La forest: at emission wavelengths lower than 1260 A there are many weak and narrow absorption lines which have the dramatic effect of strongly reducing the quasar emission intensity. In this case, each line has its own redshift. - broad absorption  lines: very large features which begin from the blue  part of strong emission lines and extend for many thousands of k m sec~ . l  CHAPTER  3.  QUASI STELLAR  line  A  Ly/3  1025.7  0 VI  1035  Lya  1215.7  N V  1241.5  0 I + [S II] Si I V + 0 I V  1305 1400.0  C IV  1549  He II  1640  A l III  1859  C III  1909  M g II  2800  Ne V  3426  [0 II]  3728  Ne III  3869  U6  4101.7  H  4340.5  7  [0 III] He II H/? He I Ha  OBJECTS.  4363 4686.5 4861 5875.6 6563  Table 3.1: Inventory of emission lines [23].  37  CHAPTER  3.  QUASI STELLAR  OBJECTS.  38  Their connection to strong emission lines is perhaps due to fact they originate close to the quasar.  3.2  QSO structure. Quasars are the most powerful sources among A G N s , a heterogeneous  class of objects characterized by a great manifestation of energy which cannot be explained by ordinary stellar processes. There is a growing convergence of opinions that A G N s are i n reality the same type of object: it might be possible to develop a model common to most A G N s which could account for factors such as variability, evolution and orientation effects. T h e variability is a significant problem: since many objects show a variable emission over few years, it is easy to put them into the wrong category. Fairall 9, a quasar w i t h a S y l spectrum, became a Sy2 galaxy decreasing its magnitude by a factor of 3 i n 5 years, or a B L L a c , PKS0521-36, became a S y l galaxy i n less than 6 years. T h e evolution factor could be important i f the variability time was very long and systematic: for example, since quasars radio loud show an optic a l / U V / X luminosity which decreases slowly and systematically to the point to be negligible, we could think of this transformation from quasars to radiogalaxies as a sort of evolution. Recently, attention has focussed on models that explain A G N s ' observational characteristics as due to different orientations w i t h which objects are seen from E a r t h , w i t h different accretion rates and masses of the cen-  CHAPTER  3.  QUASI  STELLAR  OBJECTS.  39  tral black hole and with something which prevents the radiation from being isotropic. According to the most recent model, a funnel-shaped thin shell outflow creates all these features (fig. 3.3).  Figure 3.2: Structure of A G N ' s inner region (http:// nedwww.ipac.caltech. edu/level5/Urryl/UrryPl.html). In more detail, a torus of opaque material such as dust, with a radius of few parsecs, surrounds a nucleus which is likely a 10  17  cm black hole, from which  a thermal and not-thermal continuum is emitted. Inside the torus there is a Broad Line Region (BLR), dense and highly mobile clouds, and outside the torus there is a Narrow Line Region (NLR), less dense and slow clouds. Sometimes a relativistic jet can emerge from the nucleus and relativistic effects will be seen if the line of sight is close to the  CHAPTER  3.  QUASI STELLAR  OBJECTS.  40  jet direction. L o o k i n g at directly into the jet, we should see O V V ( O p t i c a l Violent Variable, characterized by an exceptional optical variability) and B L Lac objects (with agreat activity and variability from the radio to the X - p a r t of the spectrum). If the B L R material is absent or the boosting doppler i n the optical continuum is very strong, the B L R s shouldn't be visible. A t a larger angle, radio quasars should appear. W h e n the central source is obscured but the B L R s are partially covered, broad line radio galaxies ( B L R G ) could be observed. Increasing the angle, w i t h the central source and the B L R s now obscured, only the narrow lines are visible: this is a N L R G , narrow line radio galaxy. W h e n the radio jet is very weak or absent, it is likely to have a radio quiet quasar or Seyfert 1 (mainly associated to spiral galaxies having strong emission continuum w i t h broad and forbidden narrow emission lines) when the central source is not covered, or a Seyfert 2 (with broad and narrow emission lines and a non-negligible X - r a y emission) when the the nucleus is obscured. Even without a collimated jet, one can expect a strong w i n d from the nucleus, able to blow away part of the torus or a B L R cloud: i n this case a B r o a d A b s o r p t i o n Line quasar ( B A L ) could be the end result.  CHAPTER  4.  THE UNMS  QUASAR  41  SURVEY.  CHAPTER 4 THE UNMS QUASAR SURVEY. The  4.1  UNMS1  catalog.  T h e U N M S 1 catalog [Hickson, P., priv.  comm.]  includes more than  2000000 objects: Table 4.1 displays parameters provided for each object, besides their spectral energy distribution ( S E D ) i n as many as 35 narrow bands and 4 broad bands. Parameters "nband" and "pa" represent respectively the number of bands i n which an object was observed and the angle between the image semi-major axis and the line North-South direction, measured from East to West starting from N o r t h . Details about filters were given i n §2. A s explained i n §4.4.3, only objects w i t h reliable flux measurements i n 30 or more filters were used i n this study: the selected sample contains 39040 objects. Recently R e m i Cabanac ( E S O ) , using P r i n c i p a l Component Analysis on bright stars, discovered that U N M S 1 S E D s show a systematic deviation from spectra published by Pickles [56]. The U N M S l S E D s tend to be brighter at long and short wavelengths. T h i s could result from an error i n the absolute calibration derived from K P N O observations. To make the U N M S l catalog more consistent w i t h that of Pickles, the original fluxes F i (fUteri) or g  corrected according to the relation  were  CHAPTER  4.  THE UNMS QUASAR  parameter  abbr.  units  ra  hh:mm:ss  dec  deg: arcmin: arcsec  Right Ascension Declination band number  42  SURVEY.  nband  semimajor axis  a  arcsec  semiminor axis  b  arcsec  position angle  pa  grad  broad bands  B-V-R-I  mag  narrow bands  14.48 - 14.82  mag  Table 4.1: Parameters in the U N M S 1 catalog.  Fcorr  (f Uteri) = F {f orig  where F (f  ilter i)/C(f  UterA  Uteri) is the corrected flux i n the i " a  corr  th  (4.1)  filter  and  C(filteri)  is the correction correspondent to this filter. T h i s formula can be directly applied to narrow bands since the largest bandwidth is around 40 A; for broad bands the most accurate way to proceed is to convolve the transmission curves w i t h the best fit curve of the discrete set of corrections, according to the formula  C(fUteri) where C\  k  =  ^ Zi  Xk  is the value from the correction curve and W{  curve value for the "i " th  filter  (4-2) is the transmission  at wavelength A*.. Table 4.2 shows the  corrections provided by R e m i Cabanac.  flux  CHAPTER  4.  THE UNMS  QUASAR  43  SURVEY.  A  correction  A  correction  4541.0  1.180418  6873.0  0.925380  4865.0  1.059962  7033.0  0.923201  4979.0  0.992161  7197.0  0.915377  5095.0  1.055990  7364.0  0.908164  5213.0  1.031924  7536.0  0.948763  5335.0  0.967368  7711.0  0.941986  5459.0  0.990099  7891.0  0.950050  5586.0  0.997617  8075.0  1.010598  5716.0  0.997977  8263.0  1.015200  5850.0  0.958713  8455.0  1.033491  5986.0  0.996552  8652.0  1.087448  6125.0  0.937052  8854.0  1.064739  6268.0  0.964520  9060.0  1.109830  6414.0  0.903055  9271.0  1.204436  6563.0  0.893435  9487.0  1.224466  Table 4.2: F l u x corrections by R e m i Cabanac [priv. comm.]. T h e best fitting curve, found from a x  m i n i m i z a t i o n procedure, is  2  C  = 2.893959 - 5.80649710 A + 4 . 2 6 5 0 2 7 1 0 A _4  x  _8  2  (4.3)  and it is graphically represented by the line i n F i g . 4.2 w i t h the discrete set of corrections.  CHAPTER  5000  4. THE UNMS QUASAR SURVEY.  6000  7000  8000  44  9000  lambda  Figure 4 . 1 : Corrections w i t h the best fit curve.  4.2  Method.  T h e method used to separate quasars from stars and galaxies is based on the x  2  m i n i m i z a t i o n procedure combined w i t h a Bayesian approach.  According to the Bayesian theory, given two models and a data set, it  CHAPTER  4.  THE UNMS QUASAR  45  SURVEY.  is often more useful to consider the ratio of model probabilities than the probabilities directly [30]. The so-called odds ratio i n favor of the model M j over the model Mj is defined as  ..  0  where p(Mi\D,  =  pmp,i)  I) is the probability of "i " th  (44)  model Mi given the data D and  the prior information I. According to Bayes' theorem, the posterior probability of the model M j based on data and the prior information is  mnmBdi  pmDJ)=  where p(Mi\I) I, p(D\Mi,I)  .  (4 5)  is the probability of the model M j given the prior information is the global likehood for the model M j (data influence the  choice of the model) and p(D\I)  is the probability of data given the prior  information (this parameter is independent of the model). Substituting these terms i n (4.7), the odds ratio becomes: p(Mi\I)p(D\M I)  =  h  piMillMDlM^I)  U i j  where  is the Bayesian  If p(9\Mi,I)  (Mj\I)  =P  piMAlf  -°  l4  j  factor.  is the prior probability of each sub-model of that specific  category M j , characterized by the parameter 9 (it is reasonable to take this probability equal to 1/N  iy  where A^j is the total number of sub-models of that  category) and p(D\9, Mi, I) is the posterior probability of the template (the likehood function of that template), the global likehood of the model M j is defined as  CHAPTER  4. THE UNMS QUASAR SURVEY.  p(D\Mi,I)  jpWMiJWD^MiJ)^  =  =  ^Jp{D\e,M i)r\e h  =  ^•EpW.JWi,/) *  (4.7)  Oi=l  iV  For  46  one template of the category M  iy  the m a x i m u n likehood for " N "  detected bands is just the product of the i n d i v i d u a l probability functions for the observed fluxes and the model fluxes i n the same filter: assuming a Gaussian distribution, the m a x i m u m likehood is then [4] N  {D\e M i)  P  u  u  I  -1  = m—TH  e  n=l °ViV lK where F  and a  n  N  p _  A  ^ [ - o EI " 1  „=i  "H  (4-8)  °V»  are the observed flux and its error i n the n " filter, F* is u  n  pT  th  the template flux i n the "n " filter and A is the normalization constant. th  The x  2  parameter is defined as J? — Ah?  N  X = 2  T  E[  ?  n  (4-9)  n=l  therefore  p(D|(9 ,M ,/)=n[^=][c-* ] xa  i  1  (4-10)  n=l CnV^TT  M a x i m i z i n g the likehood function involves the m i n i m i z a t i o n of the exponent: that involves the x  2  m i n i m i z a t i o n through the right choice of the  normalization constant, whcih can be found from the condition  S\  2  CHAPTER  4. THE UNMS  QUASAR  47  SURVEY.  T h e final formula for the odds ratio is  O  p{Mi\I)  Nj[e-^]  J  B  (4.12)  p(M -|J)^[ -h?]'  stars  galaxies  1Q(0.162B[J]-0.516)  lQ(0.29*B[i]-2.61)  e  quasars A  .  -|_Q(0.86*B[i]-15.80)  b: -10.95 + 15.61 i o ° - * ( W - - ) 28  V  ^Q(0.15*V[i]+0.05)  R  lQ(0.41*ii[i]-5.52)  I  1Q(0.38*/[»]-4.42)  B  same as for B filter  same as for B filter  •j_Q(0.11*fl[i]+0.15)  same as for B filter  1 0  (0.1*/[i]+0.5)  19  15  same as for the B filter  Table 4.3: Surface densities for stars, galaxies and quasars i n the broad bands:  the units are n u m / d e g  num/deg  2  2  for the B and I filters, and  0.5 m a g i n the R and V bands.  T h e "a" case is for  B < 19.5 and the "b" is for B > 19.5. The quantity p(Mi\I)  is related to the surface density of quasars, stars  and galaxies given the magnitude i n a filter. Table 4.3 shows these quantities, extrapolated from other surveys, for the broad bands B, V, R, I. A l l the consulted quasar surveys report surface densities i n the B band: this situation, which explains why V, R, I bands have the same surface density as the B, is due to the fact that these surveys studied quasars at small and medium redshift ( 0.3 - 3) for which important spectral features, such as the Lya, are still i n the B. For high redshift quasars (z > 3), the observed flux i n the B band is reduced dramatically by the presence of the increasingly prevalent Lya forest lines and of the higher column density metal-line absorption  CHAPTER systems.  4.  THE UNMS  QUASAR  48  SURVEY.  For surveys of high redshift quasars, working i n the R band or  at longer wavelenths is a fundamental prerequisite to select this category of sources: these k i n d of studies have just started. The unaccuracy to have the same surface density for different broad bands doesn't influence the object classification: the amount of quasars is a really small percentage of that of stars and galaxies i n every bands. T h e identification is done through a code which, for each object, calculates the odds ratio for every pair of categories (star-galaxy, star-quasar, galaxy-star, galaxy-quasar, quasar-star and quasar-galaxy, six combinations in total), and chooses the highest one: for example, i f the highest odds ratio is 0  Sjq  between a star and a quasar template, the object i n question is iden-  tified as a star.  4.3 4.3.1  Templates. Stellar templates.  Stellar templates were taken from Pickles' database [56].  T h e stellar  spectral library includes 131 flux-calibrated spectra encompassing a l l normal spectral types and luminosity classes at solar abundances, metal-weak and metal-rich F - K dwarfs and G - K giant components. T h e library has complete spectral coverage from  1150-10620A,  i n steps of  5A, up  to  25000A  for about  half of them (mainly later stellar types). Monochromatic fluxes F\ are tabulated and normalized to unity at  5556  A.  Table 4.4 shows how these templates are organized inside the library: the  CHAPTER 4. THE UNMS QUASAR SURVEY.  49  first column gives the library number, the second column the stellar type, and the t h i r d column the luminosity class. The first group includes 5 metalweak and 5 metal-rich G - K V spectra, the t h i r d group includes 7 metal-weak and 7 metal-rich G - K III spectra. stellar type  luminosity class  1-45  05-M6  V  46-59  B2-K3  IV  60-105  O8-M10  III  106-113  B2-M3  II  114-131  B0-M2  I  library number  Table 4.4: Parameters i n Pickles' stellar library. In order to be compared to the observed spectral energy distributions, these templates must first be convolved w i t h the transmission curves of those filters used i n this survey. For the " T " " template, the monochromatic flux 1  integrated over the "i " th  filter,  F  is given by  ^f FlWJ Q  T  =l  k  Xk  (4.13)  nuwi Q k  where Fj  k  Xk  is the template's flux at wavelength \ , W{  transmission value at wavelength \  k  k  and Q\  k  is the "i " th  k  filter  is the system response.  Since U N M S 1 spectra are given i n frequency, F versus v, monochromatic v  fluxes in wavelength are converted to fluxes i n frequency according to the formula  CHAPTER  4.  THE UNMS  QUASAR  SURVEY.  Fu = -^F  (4.14)  x  The system response Q\  k  50  is approximated by the C C D Q u a n t u m Effi-  ciency ( Q E ) . C C D s used i n this survey don't show any response for wavelengths higher than 1 pm. Therefore, the sum i n (4.4) is carried only as far as A = 1000 n m : this involves those filters whose transmission curves extend further than this threshold, the broad-band I and the narrow bands log(u) = 14.48 and log(v) = 14.49 (these narrow filters were used i n 1999 only). The procedure was applied to galaxy and quasar templates as well.  4.3.2  Galaxy templates.  Most of the galaxy S E D s were taken from K i n n e y & Calzetti's library [44] and integrated with few others from Rocca-Volmerange's database [59] in order to have a complete set of templates. The first group of spectra are given as F\ versus A, w i t h the flux i n erg cm  - 2  s  _ 1  A  - 1  and steps of 1.5 A. T h e galactic types used are elliptical, bulge,  SO, Sa, Sb and Starburst galaxies from 1200 A to 8000 A, in some cases up to 10000 A. Table 4.5 summarizes the template types.  A l l these spectra,  normalized at 4000 A, are shown i n F i g . 4.2. The second group of templates includes Sc and U V - h o t E / S 0 models, 12.08 G y r old, extended from f a r - U V (1220  A) to 1 /im w i t h a spectral  resolution of 10A. A l l the templates were convolved with filter transmission curves i n the same way as the stellar templates. Since models provided are built i n the rest frame, they were redshifted from z=0.00 to z=1.20, w i t h step 5z=0.01;  CHAPTER  4.  THE UNMS QUASAR  galactic type  colour excess  SB1  starburst galaxy  E ( B - V ) < 0.10  SB2  starburst galaxy  0.11 < E ( B - V ) < 0.21  SB3  starburst galaxy  0.25 < E ( B - V ) < 0.35  SB4  starburst galaxy  0.39 < E ( B - V ) < 0.50  SB5  starburst galaxy  0.51 < E ( B - V ) < 0.60  SB6  starburst galaxy  0.61 < E ( B - V ) < 0.70  library name  SO  SO galaxy  Sa  Sa galaxy  Sb  Sb galaxy  bulge elliptical  51  SURVEY.  bulges tempi. elliptical tempi.  Table 4.5: Spectral templates from K i n n e y & C a l z e t t i galaxies' library, in this way, 1573 templates were obtained.  4.3.3  Quasar templates.  The rest-frame template of quasars is the result of merging two templates taken from different authors, i n order to cover the largest possible wavelength range (330-8550 A). The first S E D model comes from Zheng et al. [78] and was constructed using 284 H S T F O S spectra of 101 quasars w i t h redshifts  z > 0.33.  The  uncalibrated spectrum is given i n F i g . 4.3 and it covers wavelengths between 350 and 3000 A i n the rest frame.  CHAPTER  4.  THE  UNMS  QUASAR  SURVEY.  52  CHAPTER  4. THE UNMS  QUASAR  SURVEY.  53  Lya+N V 10  FOS Quasar Spectrum  8  4 h  i 0.8 400  600  800  1000  2000  3000  Rest—frame Wavelength (A) Figure 4.3: Composite spectrum of 101 quasars, binned to 2 A.  It is clear there is a significant steepening of the continuum slope around 1050 A. Table 4.6 shows continuum fit parameters using radio-loud quasars, radioquiet quasars and both types together: power law indexes a = -1.96 for the v  range 350-1050 A and a  v  = -0.99 for the range 1050-2200 A characterize  the original template. The second model, i n F i g 4.4, was taken from Vanden Berk et al. [74]. T h e composite spectrum, w i t h resolution of 1 A, is the result of a homogeneous data set based on more than 2200 spectra from the Sloan D i g i t a l Sky Survey. T h e median composite template covers a range from 800 to 8555 A. A double power law w i t h a = -0.46 for A < 4700 and a - -1.58 for A > 4700 A is the v  v  CHAPTER  4.  THE UNMS QUASAR  SURVEY.  54  A l l Quasars  Radio-loud  Radio-quiet  Number of objects  101  60  41  M e a n redshift  0.93  0.87  0.95  1050-2200  -0.99 ± 0.01  -1.02 ± 0.01  -0.86 ± 0.01  600-1050  -2.02 ± 0.05  -2.45 ± 0.05  -1.83 ± 0.03  350-1050  -1.96 ± 0.02  -2.16 ± 0.03  -1.77 ± 0.03  Sample  Wavelength range (A):  Table 4.6: F i t t e d power-law index. best fit. T h e spectral region blueward of the L y a emission line was ignored when calculating the flux density scaling, since the L y a forest flux density varies greatly from spectrum to spectrum.  T h i s is another reason which  explains why this region was then replaced by the first template. T h e first step was to integrate these two models. T h i s was done making them completely flat (spectral features were reproduced proportionally to their reciprocal intensities on a null-slope spectral continuum), calculating the normalization constant i n the wavelength range they overlap and applying this constant to obtain a unique template. F i g . 4.5 and F i g . 4.6 show this unique "flat" model respectively for the range 300-3400 A  and the range  3400-8550 A. M a n y important features such as the L y a , Ly/3, H a and O I I I emission lines are well pronounced. T h e second step was to build different types of templates introducing various slopes for the spectral continuum.  T h e idea was to look for the  largest possible range of the spectral index a „ cited i n papers, and then to divide this range into equal intervals. For the U V part of the template to be  CHAPTER  4. THE UNMS QUASAR 1  1  1  1  1  1  1  I  1  1  1  SURVEY. 1  1  1  55  1 1 1 1 1 1—1—I—I | I I I ( ! I I I I | i  Ly a  1000  2000 4000 Rest Wavelength, X (A)  6000  8000  Figure 4.4: Composite spectrum: power law fits to the estimated flux are shown. constructed, only one value of slope was used, a = -1.96, which is the average v  between the lowest U V slope i n literature, -2.16 from Zheng et. a l [78], and the highest value, a ~ -1.76 found from Hatziminaoglou et. a l [33]. T h e same u  idea was used for the optical-near infrared (Opt-IR) range: the lowest slope value is -2.5 from Richstone & Schmidt [58] and the highest value is 1 from Hatziminaoglou et. a l [33]. D i v i d i n g this range into three equal intervals, I obtained those values of ct used i n the O p t - I R part: -2.5, -1.33, -0.16 and v  1.01. Table 4.7 shows a l l the slopes i n frequency and wavelength used for the  CHAPTER  4.  THE UNMS QUASAR  SURVEY.  Figure 4.5: F i r s t part of the composite spectrum i n the 300-3400 A  56  range:  it can be noted that the L y a (1215 A) and C I V (1549 A) are among the strongest emission lines.  quasar templates considering that the relationship between the power law index i n frequency and that i n wavelength is ct\ = —(a + 2). u  CHAPTER  4. THE UNMS  QUASAR  SURVEY.  57  o  a  *4  _L  4000  J  5000  6000 lambda(A)  I  I  7000  I  I  I  l_  _L  8000  9000  Figure 4.6: Second part of the composite "flat" spectrum i n the 3400-8550 A range. H a (6563 A) and [OIII] (4363 A) are among the strongest emission lines.  In F i g 4.7 a l l the quasar templates (in the rest frame) are shown. E a c h of these models was then redshifted to z=7.00 w i t h step 5z=0.02,  CHAPTER  4.  THE UNMS  QUASAR  a\  SURVEY.  58  template  UV  -1.96  -0.04  Opt-IR  -2.5  0.5  I  -1.33  -0.67  II  -0.16  -1.84  III  1  -3  IV  Table 4.7: Power-law index i n frequency and wavelength for quasar templates in the U V and optical-near-infrared (Optical-IR) region. but only those w i t h z > 0.30 were compared to U N M S 1 catalog objects: i n total 1344 templates were used. T h e choice of z=7.00 is due to the presence of the Lya emission line: as long as it appears inside the optical range, it is a useful tool to identify quasars.  4.4  M e t h o d calibration from templates.  So far, we have not calibrated the reliability of quasar detections and how their identification is contaminated from other astronomical objects, especially stars. A good way to test this method is to apply it to something we already know and to see if results are consistent w i t h this "a priori" knowledge. The idea is to work w i t h templates of each category at different redshifts and to consider them as "test objects", called also "template-objects". from a pure m i n i m i z a t i o n x  2  Results  procedure and from the odds ratio procedure  are important i n order to see if new conditions should be considered when  CHAPTER  1  ,  ,  4.  1  _J 2000  THE UNMS QUASAR  59  SURVEY.  ,  ,  ,  1  1  1  1  1  1  I  I  |  I  I  I  I  I  I  I  I  I  I  I  I  4000  6000  L  8000  LAMBDA  Figure 4.7: Quasar templates i n the rest frame: lines I, II, III and I V represent respectively template I, II, III and I V .  the method is applied to real sources.  CHAPTER  4.4.1  4.  THE UNMS  QUASAR  SURVEY.  60  A first analysis.  A s already discussed, the database includes 131 stellar templates, 13 galactic templates spread from redshift 2=0.00 to 2=1.20 and step A z = 0 . 0 1 (1573 galaxies i n total) and 4 quasar types from 2=0.30 to 2=7.00 and step A 2 = 0 . 0 2 (1344 i n total). For an appropriate analysis, models were normalized to the flux i n the filter  "671" ( l o g f » = 1 4 . 6 5 , A=6716 A , see Table 2.1) and plotted i n few  diagrams. In this way it is possible to recognize peculiar features that may be i m portant to separate quasars from stars and galaxies. F i g . 4.8 - 4.12 show a l l the galactic (blue line) and quasar (red line) templates i n the rest frame w i t h different stellar templates (green line): m a i n sequence stars, giant stars, supergiant stars, subgiant stars and bright giant stars.  To avoid crowded diagrams, labels were added only to indicate the  galactic and quasar models. Peculiar features at log(y) = 14.49, log(i/) = 14.63, \og(u) = 14.73 and log(^) = 14.85 correspond to the broad bands J, R, V and B. Quasar templates are truncated at log(i/)=14.56 since the original highresolution model was not complete enough to cover the entire optical range. T h i s inconvenient doesn't influence results from the odds ratio method since only quasar models at redshift higher than 0.3 are used. T h e Q S O plots show two evident lines: the H a line at log(i/)=14.66 and the H/? at log(^)=14.79. In any of these diagrams, the range \og{y)= 14.55-14.65 is characterized by a particular feature, common only to quasars and starburst galaxies. If this structure persisted at any redshift, it could be an interesting tool  firstly  CHAPTER  I 14.5  I  I  4.  I  I  THE UNMS  I  I  I  I  14.6  QUASAR  I  I  14.7  I  61  SURVEY.  I  I  I  I  L  14.8  log(fre)  Figure 4.8: Galactic (dashed line) and quasar (dashed-dot line) templates i n the rest frame w i t h Supergiant star templates (solid line). to separate stars from extragalactic objects, and secondly to find quasars using b o t h the Bayesian approach and the % procedures. In fact if a source 2  is already identified as a quasar by the Bayesian approach and a quasar tern-  CHAPTER  14.5  4.  THE UNMS  14.6  QUASAR  14.7  SURVEY.  62  14.8  log(fre)  Figure 4.9: Galactic (dashed line) and quasar (dashed-dot line) templates i n the rest frame w i t h bright giant templates (solid line). plate is considered the best fit when the pure x  2  is calculated only i n the  region 14.55-14.65, then that object can reasonably be considered a quasar candidate.  CHAPTER  14.5  4.  THE UNMS  14.6  QUASAR  14.7  SURVEY.  63  14.8  log(fre)  Figure 4.10: Galactic (dashed line) and quasar (dashed-dot line) templates in the rest frame w i t h different giant star templates (solid line). Fig.  4.13-4.17 show a l l the galactic and quasars templates at redshift  z=1.2 w i t h stellar templates. T h e structure has disappeared and, i n another region, the M g l l line is now prominent for only two quasar templates. B e -  CHAPTER  4.  THE UNMS  QUASAR  SURVEY.  64  Figure 4.11: Galactic (dashed line) and quasar (dashed-dot line) templates in the rest frame w i t h different subgiant star templates (solid line). cause these characteristics are a function of redshift, they are not useful for the separation quasars.  CHAPTER  1  — I  1  4.  1  14.5  1  THE UNMS  1  1  i  i  14.6  QUASAR  i  I  14.7  i  SURVEY.  i  i  i  65  L_  14.8  log(fre)  Figure 4.12: Galactic (dashed line) and quasar (dashed-dot line) templates i n the rest frame w i t h different main sequence star templates (solid line).  CHAPTER  I 14.5  I  I  4.  I  I  THE UNMS  I  I  I  I  14.6  QUASAR  I  I  66  SURVEY.  I  I  14.7  I  I  I  I  L  14.8  log(fre)  Figure 4.13: Galactic (dashed line) and quasar (dashed-dot line) templates at redshift z=1.2 w i t h supergiant star templates (solid line).  4.4.2  x minimization method and odds ratio 2  procedure applied to original templates. T h e first test is to compare each template-object to a l l the models available, finding not only the one giving the lowest x  2  a  n  d the highest odds ratio  CHAPTER  I  i  14.5  i  4.  i  i  THE UNMS QUASAR  i  i  14.6  i  14.7  SURVEY.  i  i  i  67  i 14.8  log(fre)  Figure 4.14: Galactic (dashed line) and quasar (dashed-dot line) templates at redshift z=1.2 w i t h bright giant star templates (solid line). but also the x  2  distribution from all the templates.  These were organized  firstly according to the stellar and galactic type, and secondly according to the luminosity class for stellar models. Table 4.8 displays this sequence.  CHAPTER  4.  THE  UNMS  QUASAR  SURVEY.  68  N.  TEMPL.  N.  TEMPL.  N.  TEMPL.  N.  TEMPL.  1  elliptical  38  blv  75  gOiv  112  k3i  2  bulge  39  b2ii  76  gOv  113  k34ii  3  SO  40  b2iv  77  wgOv  114  k3iii  4  Sa  41  b3i  78  rgOv  115  rk3iii  5  Sb  42  b3iii  79  g2i  116  wk3iii  6  Sc  43  b3v  80  g2iv  117  k3iv  7  SB1  44  b5-7v  81  g2v  118  k3v  8  SB2  45  b5i  82  g5i  119  k4i  9  SB3  46  b5ii  83  g5ii  120  k4iii  10  SB4  47  b5iii  84  g5iii  121  wk4iii  11  SB5  48  b6iv  85  wg5iii  122  rk4iii  12  SB6  49  b8i  86  rg5iii  123  k4v  13  UVhot  50  b8v  87  g5iv  124  k5iii  14  QSOl  51  b9iii  88  g5v  125  rk5iii  15  QS02  52  b9v  89  wg5v  126  k5v  16  QS03  53  fOi  90  rg5v  127  k7v  17  QS04  54  fOii  91  g8i  128  mOiii  18  o8iii  55  fOiii  92  g8iii  129  mOv  19  o5v  56  f0-2iv  93  wg8iii  130  mliii  20  o9v  57  fOv  94  g8iv  131  mlv  21  aOi  58  f2ii  95  g8v  132  m2i  22  aOiii  59  f2iii  96  kOlii  133  m2iii  23  aOiv  60  f2v  97  kOiii  134  m2v  24  aOv  61  f5i  98  wkOiii  135  m2.5v  Table 4.8: Continue.  CHAPTER  4.  THE  UNMS QUASAR  SURVEY.  69  N.  TEMPL.  N.  TEMPL.  N.  TEMPL.  N.  TEMPL.  25  a2i  62  f5iii  99  rkOiii  136  m3ii  26  a2v  63  f5iv  100  kOiv  137  m3iii  27  a3iii  64  f5v  101  kOv  138  m3v  28  a3v  65  wf5v  102  rkOv  139  m4iii  29  a47iv  66  f6v  103  kliii  140  m4v  30  a5iii  67  rf6v  104  wkliii  141  m5iii  31  a5v  68  f8i  105  rkliii  142  m5v  32  a7iii  69  f8iv  106  kliv  143  m6iii  33  a7v  70  f8v  107  k2i  144  m6v  34  bOi  71  wf8v  108  k2iii  145  m7iii  35  bOv  72  rf8v  109  wk2iii  146  m8iii  36  bli  73  gOi  110  rk2iii  147  m9iii  37  bl-2iii  74  gOiii  111  k2v  148  mlOiii  Table 4.8: Template sequence for the x  2  analysis.  CHAPTER  I 14.5  i  i  4.  i  i  THE UNMS  i  I  i  i  14.6  QUASAR  i  i 14.7  i  SURVEY.  i  i  '  i  70  i  14.8  log(fre)  Figure 4.15: Galactic (dashed line) and quasar (dashed-dot line) templates at redshift z=1.2 w i t h giant star templates (solid line). Fig.  4.18-4.21 show the \  distribution for several quasar template-  2  objects when compared to a l l the 3048 models. T h i s is useful to understand which templates have similar % and confuse the object identification (this 2  CHAPTER  4.  THE  UNMS  QUASAR  SURVEY.  71  Figure 4.16: Galactic (dashed line) and quasar (dashed-dot line) templates at redshift z=1.2 w i t h sub-giant star templates (solid line). explains why these plots show only the b o t t o m region and not the complete range). The blue line represents the comparison w i t h galactic templates, the red line quasar templates and the green line stellar template.  CHAPTER  14.5  4.  THE  UNMS  14.6  QUASAR  14.7  72  SURVEY.  14.8  log(fre)  Figure 4.17: Galactic (dashed line) and quasar (dashed-dot line) templates at redshift z=1.2 w i t h main sequence stars (solid line). W h e n the template-object is just one of the models, it is logical that the lowest x  2  comes from the original template. F r o m these plots it is important  to see is that there are some stellar and galactic models w i t h low % and close 2  CHAPTER  4.  THE UNMS QUASAR  73  SURVEY.  QSO templ.14z=0.3  50  100  150  TEMPLATE  Figure 4.18: x  2  distribution for 4 Q S O N.14 at z=0.3: line I, II and III come  from the comparison respectively to galactic, quasar and stellar templates. to the object i n question. For example, quasar N.14 is close to the first six galactic models at low redshift (mostly ellipticals and spirals) and to K and  CHAPTER  Figure 4.19: x  2  4.  THE UNMS QUASAR  SURVEY.  74  distribution for Q S O N.15 at z=1.4: line I, II and III come  from the comparison respectively to galactic, quasar and stellar templates. M stellar templates; quasar N.15 is very close to late spirals and starburst galaxies and to G and K stars, quasar N.16 is mostly near F stars and finally  CHAPTER  4.  THE UNMS QUASAR  75  SURVEY.  QSO templ.16z=3.5  _J  i  i  i  i  50  i 100  i  i  i  i  i 150  TEMPLATE  Figure 4.20: x  2  distribution for Q S O N.16 at z=3.5: line I, II and III come  from the comparison respectively to galactic, quasar and stellar templates. quasar N.17 is very close to starburst galaxies . T h i s situation is confirmed by the odds ratio's results: 40 objects, all from  CHAPTER  Figure 4.21: x  2  4.  THE UNMS QUASAR  SURVEY.  76  distribution for Q S O N.17 at z=5.6: line I, II and III come  from the comparison respectively to galactic, quasar and stellar templates. the quasar category, are infact misidentified. In particular N.14 quasars i n the redshift range 1.30-1.46 are mostly identified as U V h o t and spiral galaxies at  CHAPTER  4.  THE UNMS  QUASAR  77  SURVEY.  low and m e d i u m redshift. Quasars N.15, N.16 and N.17 are mostly confused w i t h F and B stars. T h e 40 misclassified elements are just a small portion of the entire sample which involves more than 3000 test objects. Since the final goal is to find quasars i n a catalog of real objects, it is interesting to analyze the results from the same test applied to altered models, as we explain below.  4.4.3  x  2  a n  d odds ratio procedures applied to altered  templates. For the second test, template-objects were modified to simulate realistic conditions. Noise was added to them simulating 39 measurements, from the standard Gaussian distribution [4]:  (4.15) w i t h mean 0 and standard deviation 1. T h e "i " th  deviated flux is given by  (4.16)  Fi,dev — Fi -f- SFiOj where Fi and 8Fi are the flux w i t h its error i n the "i " th  F i g . 4.22-4.25 show the x  2  filter.  distribution found for the same objects cited  in the previous paragraph, this time w i t h noise added: as expected, the  x  2  between the smeared template-object and the original model is no longer null but is of the same order as those of other templates. T h i s k i n d of ambiguity is fatal for ~ 5 0 0 template-objects for which the X method fails to recover the correct model. 2  CHAPTER  Figure 4.22: x  2  4.  THE UNMS QUASAR  SURVEY.  78  distribution for the altered Q S O N.14 at z=0.3: line I, II and  III come from the comparison respectively to galactic, quasar and stellar templates. F r o m the odds ratio approach, 1082 template-objects of 3048 are misclassified. In particular, N.14 quasars are confused w i t h U V h o t , elliptical and  CHAPTER  Figure 4.23: x  2  4.  THE UNMS QUASAR  SURVEY.  79  distribution for the altered Q S O N.15 at z=1.4: line I, II and  III come from the comparison respectively to galactic, quasar and stellar templates. spiral galaxies, N.15 quasars are mainly classified as starburst and late spiral galaxies, and G and F stellar types, N.16 quasars is mostly seen as F type  CHAPTER  Figure 4.24: x  2  4.  THE UNMS  QUASAR  SURVEY.  80  distribution for the altered Q S O N.16 at z=3.5: line I, II and  III come from the comparison respectively to galactic, quasar and stellar templates. stars and quasars N.17 w i t h B type stars. U n t i l now a l l the results are based on template-objects w i t h 39 flux mea-  CHAPTER  Figure 4.25: x  2  4.  THE UNMS QUASAR  SURVEY.  81  distribution for the altered Q S O N.17 at z=5.6: line I, II and  III come from the comparison respectively to galactic, quasar and stellar templates. surements i n the optical range. T h i s situation is not very realistic: i n the U N M S l catalog, most of the sources have 10-20 filters and only 4% of the  CHAPTER  4.  THE UNMS QUASAR  SURVEY.  82  entire database has more than 35 magnitudes. So, it is important not only to add noise to these template-objects, but also to analyze the results when more and more filters are randomly neglected (jackknife approach). F i g . 4.26 shows the distribution of stars (green line), galaxies (blue line) and quasars (red line) as classified by the program against the number of filters: each distribution is normalized to the object number available for each category (131 stars, 1573 galaxies and 1344 quasars).  Note that the num-  ber of stars and quasars increases and decreases respectively when more and more filters are randomly neglected, while the galaxy distribution is nearly unaffected. 10-15 point quasar S E D s (with some emission lines missed) can be easily confused w i t h star S E D s and this suggests the importance of using a criterion on the m i n i m u m filter number when the odds ratio method is applied. T h e fact the galaxy distribution is practically flat means galaxies are not easily confused w i t h other types of objects and the number of filters is not so important for this category. It is interesting to split the distribution of stars, galaxies and quasars into 4 redshift ranges and include the fraction of template-objects correctly classified (coloured line) and the fraction of models incorrectly classified i n that category (black line). In this way it is possible to understand how this misclassification between stars and quasars changes w i t h redshift. In order to do that, 4 redshift ranges are considered: • range I at 2=0-0.6 which involves 131 stars, 793 galaxies and 64 quasar (Fig. 4.27-4.29) ; • range II at 2=0.6-1.2 which includes 780 galaxies and 120 quasars ( F i g . 4.30-4.31);  CHAPTER  _i  i  i  10  4.  i  THE UNMS QUASAR  i  i  i  i  i  20  i  84  SURVEY.  i  i  i  30  i  i  i  40  FILTERS  Figure 4.27: Normalized distribution for correctly classified stars (hexagon) and for objects wrongly classified as stars (cross). A s usual, these distributions were normalized to the object number available in each redshift range. For galaxies the distribution is almost flat i n every range. W h e n S E D s  CHAPTER  4.  THE UNMS QUASAR  85  SURVEY.  z  > 3 o  O  0.5  10  20  30  40  FILTERS  Figure 4.28: Normalized distribution i n the redshift range z=0-0.6 for correctly classified galaxies (hexagon) and for objects wrongly classified as galaxies (cross). are constructed w i t h only 10 flux points, 85% of galaxies are still correctly classified.  CHAPTER  4.  THE UNMS QUASAR  SURVEY.  86  z w > P '2" o  o  FILTERS  Figure 4.29: Normalized distribution i n the redshift range z=0-0.6 for correctly classified quasars (hexagonal)  and for objects wrongly  classified as quasars (cross). For quasars, as the number of filters decreases, the distribution drops very quickly, first of all i n the second redshift range. T h i s behavior is consistent  CHAPTER  4.  THE UNMS QUASAR  SURVEY.  87  z  > o o o  FILTERS  Figure 4.30: Normalized distribution i n the redshift range z=0-0.6 for correctly classified galaxies (hexagon) and for objects wrongly classified as galaxies (cross). w i t h that of stars uncorrectly classified i n the first range: the black line i n creases when fewer filters are used and this opposite trend implies that most  CHAPTER  4.  THE UNMS QUASAR  SURVEY.  88  z > XI  o  x> o  FILTERS  Figure 4.31: Normalized distribution i n the redshift range z=0.6-1.2 for correctly classified quasars (hexagon) and for objects wrongly classified as quasars (cross). of them are i n real terms Q S O s . T h i s could be due to the presence of lines in the quasar S E D which resemble those i n the stellar spectrum or, since  CHAPTER  4.  THE UNMS QUASAR  SURVEY.  89  2 >  £1 O  XI O  FILTERS  Figure 4.32: Normalized distribution i n the redshift range z=l.2-4.1 for correctly classified quasars (hexagon) and for objects wrongly classified as quasars (cross). filters are neglected randomly, some emission features are not considered and the quasar's continuum is easily confused to a stellar "spectrum".  Another  CHAPTER  4.  THE UNMS QUASAR  SURVEY.  90  Figure 4.33: Normalized distribution i n the redshift range z=4.1-7 for correctly classified quasars (hexagon) and for objects wrongly classified as quasars (cross). interesting consideration is that the black line, which represents the number of sources misclassified as quasars, does not appear i n any plot for quasars.  CHAPTER  4.  THE UNMS  QUASAR  SURVEY.  91  T h i s means that when a test object is classified as quasar, it is really so. We can conclude that i n order to have at least 80% of well classified quasars, the method should be applied only to sources w i t h 30 or more magnitudes. T h e situation is improved for galaxies: i n this case the threshold is closer to 20 magnitudes. To confirm the validity of this criterion, fig. 4.34-4.39 show the distribution of the odds ratio's logarithm normalized to the one from the correct category and calculated w i t h 39 filters, for 4 quasars and 2 galaxies at different redshift (black line). T h e odds ratio can be used to determine which of two models is the preferred one considering the ratio of their probabilities. In each plot, the black line represents the odds ratio distribution i n favour of the correct category, and the red, green and blue lines are the odds ratio distributions i n favour of quasar, star and galaxy models as the best fit of the template-object analysed. W h e n a line exceeds the other ones i n a plot, the related category of models is the preferred one. For quasar test objects, i n most cases the odds ratio i n favour of stars and galaxies (wrong classification) is lower than the one i n favour of quasars (correct classification) for more than 25 filters at least. For galaxies fewer filters are required to avoid an eventual misclassification: it is reasonable to expect that only 20 filters are required.  CHAPTER  4.  THE UNMS QUASAR  SURVEY.  92  Figure 4.34: Normalized odds ratio distribution for a Sa spiral galaxy (N.4) at redshift 0.2, 0.6, 0.8 and 1.2 i n favor of stellar galactic (croos) and quasar (triangle) template.  (hexagon),  CHAPTER  4.  THE UNMS QUASAR  SURVEY.  93  Figure 4.35: Normalized odds ratio distribution for a starburst galaxy (N.7) at redshift 0.2, 0.6, 0.8 and 1.2. i n favour of stellar (hexagon), galactic (cross) and quasar (triangle) template.  CHAPTER  4.  THE UNMS QUASAR  SURVEY.  94  Figure 4.36: Normalized odds ratio distribution for Q S O N.14 at redshift 0.3, 2.1, 4.2 and 7.in favour of stellar (hexagon), galactic (triangle) and quasar (cross) template.  CHAPTER  4.  THE UNMS QUASAR  SURVEY.  95  Figure 4.37: Normalized odds ratio distribution for Q S O N.15 at redshift 0.3, 2.1, 4.2 and 7. i n favour of stellar (hexagon), galactic (triangle) and quasar (cross) template.  CHAPTER  4.  THE UNMS QUASAR  SURVEY.  96  Figure 4.38: Normalized odds ratio distribution for Q S O N.16 at redshift 0.3, 2.1, 4.2 and 7.in favour of stellar (hexagon), galactic (triangle) and quasar (cross) template.  CHAPTER  4.  THE UNMS QUASAR  SURVEY.  97  Figure 4.39: Normalized odds ratio distribution for Q S O N.17 at redshift 0.3, 2.1, 4.2 and 7.in favour of stellar (hexagon), galactic (triangle) and quasar (cross) template.  CHAPTER  5.  QUASARS  IN THE UNMSl  98  CATALOG.  CHAPTER 5 Q U A S A R S IN T H E U N M S l CATALOG. 5.1  Results.  A s shown i n the previous chapter, Q S O s can be found and identified w i t h a completeness of ~ 85% when the method described i n §4 is applied to the U N M S l catalog for objects satisfying two criteria: a m i n i m u m number of 30 flux measurements and a m i n i m u m separation radius of 3 arcsecs. Based on these criteria, our algorithm selects 39040 sources out of the 20.13-deg  2  field.  Table 5.1 summarizes the number of objects. Category  #  STARS  25193  GALAXIES  10791  QUASARS  3056  Table 5.1: Star, galaxy and quasar identifications. We first describe star and galaxy candidates; quasar candidates w i l l be discussed i n §5.2.  CHAPTER  5.  QUASARS  IN THE UNMSl  CATALOG.  99  Figure 5.1: Star counts by spectral types.  5.1.1  Stellar candidates in the U N M S survey.  F i g . 5.1 shows the distribution of stars, organized according to the order presented i n Table 5.2. T w o major structures appear: from the 46th to the 99th stellar type and  CHAPTER  5. QUASARS  IN THE UNMSl  CATALOG.  100  from the 108th to the 131th stellar type. The first group corresponds to F, G, K and M stars of any luminosity class; the second group correspond to metal-weak and metal-rich F, G, K and M stars, mainly main sequence and giant stars. The most common templates are main sequence G stars and giant G stars. A and B stars (1-32) and O stars (105-108) represent just a small fraction of the stellar distribution. This result is in agreement with the star counts by spectral type commonly known. Table 5.3 shows the number density logarithm of stars [ # / 10 pc ] by spectral types from data published in 4  3  Allen (1973) [6] [1]: for every luminosity class, F-G-K-M stars are the most frequent. Table 5.4 is similar to Table 5.3 but shows the results from the L M T survey: it is useful to see if the method can recognize stars not only of different type but also of different luminosity classes. The comparison of these two tables confirms that the method seperate stars of different stellar types: many F, G, k and M stars are found with respect to other types; the only discrepancy is in large offsets of O stars in the L M T sample. The star distribution according to the luminosity class, is not good: the number of giants and supergiants is too high with respect to that of main sequence stars.  5.1.2  Galaxy candidates in the U N M S survey.  Fig. 5.2 shows the galaxy counts by morphological type according to the order described in Table 5.5: the term "Bulge" refers to galaxies with a pronounced bulge (spheroidal central region); the term "SB" refers to starburst  CHAPTER  5.  QUASARS  IN THE  UNMSl  CATALOG.  101  NUM.  TYPE  NUM.  TYPE  NUM.  TYPE  NUM.  TYPE  1  aOi  34  fOi  67  kOiv  100  m5v  2  aOiii  35  fOii  68  kOv  101  m6iii  3  aOiv  36  fOiii  69  kliii  102  m6v  4  aOv  37  fOv  70  kliv  103  m7iii  5  a2i  38  f2ii  71  k2i  104  m8iii  6  a2v  39  f2iii  72  k2iii  105  m9iii  7  a3iii  40  f2v  73  k2v  106  o5v  8  a3v  41  f5i  74  k34ii  107  o8iii  9  a47iv  42  f5iii  75  k3i  108  o9v  10  a5iii  43  f5iv  76  k3iii  109  rf6v  11  a5v  44  f5v  77  k3iv  110  rf8v  12  a7iii  45  f6v  78  k3v  111  rgOv  13  a7v  46  f8i  79  k4i  112  rg5iii  14  bOi  47  f8iv  80  k4iii  113  rg5v  15  bOv  48  f8v  81  k4v  114  rkOiii  16  bl2iii  49  gOi  82  k5iii  115  rkOv  17  bli  50  gOiii  83  k5v  116  rkliii  18  blv  51  gOiv  84  k7v  117  rk2iii  19  b2ii  52  gOv  85  mOiii  118  rk3iii  20  b2iv  53  g2i  86  mOv  119  rk4iii  21  b3i  54  g2iv  87  mlOiii  120  rk5iii  22  b3iii  55  g2v  88  mliii  121  wf5v  23  b3v  56  g5i  89  mlv  122  wf8v  Table 5.2: Continue.  CHAPTER  5.  QUASARS  IN THE UNMSl  CATALOG.  102  NUM.  TYPE  NUM.  TYPE  NUM.  TYPE  NUM.  TYPE  24  b57v  57  g5ii  90  m2.5v  123  wgOv  25  b5i  58  g5iii  91  m2i  124  wg5iii  26  b5ii  59  g5iv  92  m2iii  125  wg5v  27  b5iii  60  g5v  93  m2v  126  wg8iii  28  b6iv  61  g8i  94  m3ii  127  wkOiii  29  b8i  62  g8iii  95  m3iii  128  wkliii  30  b8v  63  g8iv  96  m3v  129  wk2iii  31  b9iii  64  g8v  97  m4iii  130  wk3iii  32  b9v  65  kOlii  98  m4v  131  wk4iii  33  f02iv  66  kOiii  99  m5iii  Table 5.2: Conversion table between stellar type and number. 0  B  A  Giants and Supergiants M a i n Sequence  -3.6  0.0  0.7  F  G  K  M  -0.3  0.2  0.6  -0.6  1.4  1.8  2.0  2.8  Table 5.3: L o g a r i t h m i c star counts [# / 10 pc ] by stellar type and luminos4  3  ity class from data published i n A l l e n (1973) [1]. galaxies of different types as shown i n Table 4.5. E a c h histogram b i n i n F i g . 5.2 includes galaxies of the same type i n tegrated over a l l redshifts (the redshift ranges from 0 to 1.2, w i t h step 5z — 0.01. There are three pronounced peaks which correspond to SO and Sc galaxies, and starburst galaxies. A s explained later, the fact to have many  CHAPTER  5.  QUASARS  IN THE UNMSl  0  B  A  Giants and Supergiants M a i n Sequence  1.0  0.0  0.64  103  CATALOG.  F  G  K  M  1.9  3  2.9  2.1  2.2  2.8  2.9  2.6  Table 5.4: Logarithmic star counts [# / 10 pc ] for candidates found i n the 4  3  L M T survey. NUM.  TYPE  NUM.  TYPE  NUM.  TYPE  1  Bulge  6  Sc  11  SB 5  2  E  7  SB 1  12  SB 6  3  SO  8  SB 2  13  UVhot  4  Sa  9  SB 3  5  Sb  10  SB 4  Table 5.5: Conversion table between galaxy type and number. spirals is reasonable i f compared w i t h other works on galactic types [6]. It is useful to check the galaxy distribution w i t h that found i n the C f A l - 2 Redshift Surveys [50]. The C f A l sample includes 2397 galaxies (ellipticals, SO, Sa, Sb, Sc, Sd and S m spirals, Irregular galaxies) distributed over 2.7 steradians; the C f A 2 survey has 1862 galaxies over 0.42 steradians.  Table  5.6 shows the galaxy percentage distributed according to the morphological type for C f A l - 2 and L M T surveys.  Discrepancies between the C f A l  and  C f A 2 surveys are due to the fact they sample different structures: the morphological m i x varies w i t h local density [19].  For the U N M S survey, the  fraction of Sc galaxies does not include Sd spirals.  CHAPTER  5.  QUASARS  1500  IN THE UNMSl  ~i  1 r-  _l  I  CATALOG.  104  1000  o o  o  500  _l  I  I  l_  I  [_  10 galactic type  Figure 5.2: G a l a x y distribution by the morphological type. Considering only common galactic types, what appears evident i n each survey is the low fraction of ellipticals: at this point, this result seems reasonable. T h e major discrepancy is the smaller number of spiral candidates found  CHAPTER  5.  QUASARS  IN THE UNMSl  CfAl  CfA2  LMT  %  %  %  CATALOG.  105  4.1  bulge E  11.1  5.3  4.7  SO  25.3  34.4  11  Sa+sb  33.2  40.3  17.3  Sc+Sd  22.1  12.9  12.6  Sm+Im  25.3  5.1  SB1-6  38.6  UVhot  6.1  Table 5.6: Fraction of ellipticals (E), SO galaxies, Sa+Sb, Sc+Sd and S m spirals, Irregular (Im) galaxies, Starburst (SB1-6) and U V h o t galaxies i n the C f A l , C f A 2 and L M T galaxy samples. in the L M T sample: this spiral fraction is not i n fact completely consistent w i t h the luminosity function of Binggeli, Sandage and T a m m a n n [5] where, considering the galaxy distribution i n the local field, the fraction of spirals w i t h respect to ellipticals seems higher than that obtained using the method described i n §4. A m o n g galaxy candidates, there is a big number of starburts w i t h respect to normal galaxies. T h i s t h i r d result doesn't agree w i t h the luminosity function of Sanders & M i r a b e l ( F i g . 1) [2]: for bolometric luminosity lower than 1 O L , the luminosity function of normal galaxies is well above that of 1 1  0  starbusts; for higher luminosities, it seems there are more irregular galaxies than normal galaxies but the trend of starburst luminosity function is not  CHAPTER  5.  I ' ' 'I  I  QUASARS  1 1 1  I  1 1  1  I  IN THE UNMSl  '  1  1  I ' 1  CATALOG.  1  I  1  1 1  I  1  1  1  I  1  106  1 1  I  1  1 1  J|  STARBURSTS  BULGE I SPIRALS II 1000  1000 H  500  500 h  CO ECJ Cd  3  O  _L  0  0.2  0.4  ' I  1 1  0.6  1  I  1 1  0.8  1  I  1  1 1  1  I  1  1 1  0  1.2  I i i  iI 0.6  t i i  I i i i I  0.2  0.4  0.8  1  1.2  0.2  0.4 0.6 0.8 Redshift Z  1  1.2  J|  ELLIPTICALS 1000  1000  h  co  EU H CQ O  500 h  500 h  _L  0  0.2  _L 0.4 0.6 0.8 Redshift Z  1  1.2  0  Figure 5.3: D i s t r i b u t i o n of galaxy counts w i t h spectral type and redshift. well defined.  Since starbursts are characterized by a great stellar activity  and their spectrum show emission lines, it is possible that some stars were misidentified as starburts. F i g . 5.3 shows the galaxy distribution i n redshift according to the galactic  CHAPTER  5.  QUASARS  IN THE UNMSl  CATALOG.  107  type. Ellipticals are more concentrated at low redshifts: this is a selection effect due to the difficulty to observe non-emission like objects at high redshifts. Spirals are mainly found i n the range z = 0-0.6: this is consistent w i t h the fact that, though dwarf spheroidal ellipticals are the closest galaxies to the M i l k y Way, they are not as easily detected as spirals, the most luminous field galaxies close to our Galaxy. Starburst candidates are detected at a l l redshifts:  although they are  mainly concentrated i n the range z = 0 - 0.3, there are pronounced peaks at z ~ 0.5 and z ~ 0.9. M a n y of the features such as tails and ringlike structures visible i n starburst pictures can be convincingly explained i n terms of colliding a n d / o r merging galaxies [67]. T h e fact that galaxies need time to form and, first of all, to come so close to the point to interact, could explain the great number of starburts found at low redshift. A n o t h e r explanation for the peak i n the range z = 0 - 0.3 could be that some of these local starburts are misidentified stars.  5.2  Properties of quasar candidates.  5.2.1  The number - redshift distribution.  F i g . 5.4 shows the number of quasar candidates i n each redshift range 5z = 0.3, from z = 0.3 to z = 7.0. Most sources fall i n two regions: 0.5 - 1.5 and 3.5 - 5.5. It is useful to check this result w i t h other redshift distributions such as the Hewett & Burbidge catalog (7110 quasars) [35] and the Sloan Sky  CHAPTER  5.  QUASARS  IN THE UNMSl  CATALOG.  108  400  Redshift z  Figure 5.4: Redshift distribution of quasar candidates. Survey catalog (3814 quasars) [66] (see F i g . 5.5). In the first work, quasars are mostly concentrated i n the range 0 - 2 . 3 w i t h a t a i l extending up to z = 4. T h e second study shows many quasars i n the range 0.25 - 2 but there are still objects at z = 5. In this last case, the peak at z ~ 0.25 is caused by  CHAPTER  5.  QUASARS  IN THE UNMSl  CATALOG.  109  Figure 5.5: The redshift distribution for quasars i n the Hewett&Burbidge catalog (top) and i n the Sloan Sky Survey catalog (bottom).  CHAPTER  5.  QUASARS  IN THE UNMSl  CATALOG.  .  110  Seyfert galaxies. C o m p a r i n g a l l these distributions, two discrepancies can be noted: the L M T sample shows a d i r t h of detections for z ~ 2 and an unusual number of objects at redshift z > 4. T h e L y a emission line could explain b o t h these two effects. A t z ~ 2 the Lya is not visible i n the optical part and this, besides having an incomplete S E D , could contribute to the misclassification of some quasars as stars: this could be the reason behind the first discrepancy. T h e second effect could be explained i n the following way: the L y a emission line enters into the optical spectrum (e.i. it can be observed i n quasar templates at wavelenghts A > 4500 A ) at redshift higher than 3. If a galaxy spectrum or, more likely a stellar spectrum, shows a strong line but it misses other important flux measurements, this source can be misclassified as quasar. T h i s misclassification was i n part expected: the method is supposed to separate quasars from galaxies and stars w i t h an accuracy of 85%. Since a lot more than 15% quasars seem misidentified, it is then necessary to analyse more carefully the quasar sample i n order to catch unlikely quasar candidates.  5.2.2  The number - apparent V magnitude distribution.  Fig.  5.6 shows the object distribution according to the apparent V  magnitude: quasar candidates are peaked around magnitude 19, w i t h a long and thick t a i l extending to magnitude 12. Even though the brighest quasar (3C 273, h t t p : / / w w w . seds.org/~spider/spider/Misc/3c273.! has a visual brightness 12.8, it is unlikely that a l l the objects brighter than magnitude 16 are correctly classified.  CHAPTER  5.  QUASARS  IN THE UNMSl  CATALOG.  Ill  Figure 5.6: The V magnitude distribution for quasar candidates. The magnitude distribution is compared to that of Hewett & Burbidge (Fig. 5.7): both distributions show a pronounced peak at magnitude 19 and the fact our sample misses many faint objects at V > 20 could be due to the lower l i m i t i n g magnitude reached by the N O D O - L M T (21 mag) and also  CHAPTER  5. QUASARS IN THE UNMSl CATALOG.  1500 i  1  1  1  1  1  1  1  [  1  1  1  112  r  V (magnitudes)  Figure 5.7: T h e V magnitude distribution from the H e w e t t & B u r b i d g e catalog. to the smaller number of quasar candidates counted i n the U N M S l sample (30% of that of Hewett & Burbidge). T h e real discrepancy between these two distributions is the bigger number of bright sources i n the L M T sample: it is possible that many quasar candidates w i t h magnitude brighter than 16 are misclassified stars.  5.2.3  The number - spectral index a distribution.  T h e continuum part of a quasar spectrum can be modeled by a simple power law F index.  oc u , where F„ is the flux i n frequency and a is the spectral a  v  CHAPTER  5.  QUASARS  IN THE UNMSl  CATALOG.  113  To determine the continuum, one must exclude emission lines. T h i s is done i n steps.  First, the S E D is fitted w i t h a power law considering a l l  magnitudes. Second, a l l the points for which  \Pobs,v  -Pu \  >25F ,  Q  (5.1)  are rejected as possible lines. Here F  obs v  obStV  and 6F  obs>I/  are the observed flux  and its error at frequency u, a and (3 are the characteristic parameters of the best power law fit. The t h i r d and final step is to fit a second time the S E D w i t h the restricted set of magnitudes. F i g . 5.8 shows the object distribution according to the spectral index a. A prominent peak at a ~ -1 and a second peak for a ~ -2 are the most interesting characteristics. F i g . 5.9 shows spectral index distributions by Warren et a l . [77] and by Francis et al. [24]. T h e first study is a multicolour survey for high redshift quasars and it includes 144 sources; the second work uses quasars taken from the Large Bright Quasar Survey to create a high signal-to-noise composite spectrum (718 sources). B o t h of them show a peak around a ~ -0.5, a bit shifted from the peak at a ~ -1 observed i n this survey. T h e m a i n difference between the U N M S a distribution and those found i n these works is the second peak, centered at a ~ -2 and extending up to a ~ -5. Since typical values of a cited i n literature (§4.3.3) are i n the range [-2.5, 1], it is likely that some misclassified stars are included i n this second peak, but it is also possible that the elimination of some points (no more than 9 points according to the criterion used) i n spectra has contributed to make quasar S E D s steeper if the flux measurements eliminated are at the edges of the optical range.  CHAPTER  5.  QUASARS  IN THE UNMSl  CATALOG.  114  spectral index  Figure 5.8: Spectral index distribution for quasar candidates i n the U N M S l catalog.  5.3 Discussion. In §4 we found that it is possible to identify quasars w i t h a completeness of 85%. Yet, result diagrams, shown i n the previous section, are not fully  CHAPTER  5.  QUASARS  IN THE UNMSl  CATALOG.  115  Figure 5.9: The spectral index distribution by Warren et al. (top picture, upper line) and by Francis et a l . (bottom).  CHAPTER  5.  QUASARS  IN THE UNMSl  CATALOG.  116  consistent w i t h the results obtained from other surveys. Therefore, objects outside expected ranges of redshift [0.5, 4], magnitude [16, 23] and spectral index [-3, 1] were analysed more completely. To do that, three more diagrams were considered: object distribution i n the redshift - V magnitude plane ( F i g . 5.10), i n the redshift - spectral index plane ( F i g . 5.11) and i n the spectral index - V magnitude plane ( F i g . 5.12). F i g . 5.10 shows that, faint sources are equally distributed at high and low redshifts, but quasar candidates brighter than the 16th magnitude are mostly localized i n the redshift range 3-6. T h i s last result is not acceptable because those quasars would have to have exceptionally high luminosities. T h e second diagram ( F i g . 5.11) shows that sources are m a i n l y grouped around a ~ - 1 . Most of those having spectral index lower than -3, have redshift higher than 4. T h e y represent just a small group (78 sources) and neglecting them would have little effect. T h e next diagram (Fig. 5.12) shows the source distribution i n the spectral index - V magnitude plane: only few of the bright objects have unusual spectral indexes lower than -3. These considerations suggest that, i n order to reduce misclassification, criteria should be based on the first diagram ( F i g . 5.10). T h o u g h we wish to eliminate objects that satisfy one or more of the following criteria: • V mag < 16 and z > 3.5; • V mag < 16 and a < -3; • z > 3.5 and a < -3. T h e fact that we chose a = -3 as the m i n i m u m value for the spectral index  CHAPTER  5.  QUASARS  IN THE UNMSl  CATALOG.  117  Figure 5.10: Quasar candidates i n the redshift - V magnitude plane. instead of -2.5, cited i n §5.2.3, is justified by the desire to retain as good quasar candidates as possible. E a c h object has a m i n i m i m number of 30 flux measurements over a total of 39: i f an observed S E D shows the flux of an emission line and no flux i n the filters right beside it, the continuum part of  CHAPTER  5.  QUASARS  IN THE UNMSl  CATALOG.  Figure 5.11: Quasar candidates i n the redshift - spectral index plane, the spectrum would appear steeper (a < -2.5).  118  CHAPTER  2  5. QUASARS  0  IN THE UNMSl  -  2  -  4  CATALOG.  119  -6  spectral index  Figure 5.12: Quasar candidates i n the spectral index - V magnitude plane.  5.4  Quasar candidates.  Weeding out misclassified quasar candidates which satisfy at least one of the criteria mentioned above, the final list now includes 2294 objects, 6% of the entire group of sources analysed w i t h this method.  CHAPTER  5.  QUASARS  IN THE UNMSl  CATALOG.  120  According to the surface densities mentioned i n §4, the distribution of stars, galaxies and quasars should be respectively 49.5%, 49.5% and 1% of the object sample: it is clear that the number of quasars found is still higher than  400  Redshift z  Figure 5.13: T h e number - redshift distribution for the quasars.  "cleaned" set of  CHAPTER  5.  QUASARS  IN THE UNMSl  CATALOG.  121  Figure 5.14: T h e number - V magnitude distribution for the "cleaned" set of quasars. expected and it is likely this list still includes misclassified objects. Diagrams in F i g . 5.13, 5.14 and 5.15 show the distribution i n redshift, magnitude and spectral index using the new quasar candidate sample: these plots are now  CHAPTER  5.  QUASARS  0  IN THE UNMSl  CATALOG.  122  -2 spectral index  Figure 5.15: T h e number - a distribution for the "cleaned" set of quasars. more consistent w i t h the knowledge that we have about quasars and no other criteria seem possible to further reduce the quasar candidate list.  CHAPTER  5.5  5.  QUASARS  IN THE UNMSl  CATALOG.  123  Identification. A s another way to test the reliability of quasar candidates, the U N M S l  quasar catalog was cross-correlated w i t h Veron's database [75], which, recently updated after the first release of the 2dF quasar catalogue ( C r o o m et al.), now includes 23760 quasars, 608 B L L a c objects and 5751 active galaxies (of which 2765 are Seyfertls). In the region observed by the L M T Survey (12  h  <a<  1S , 32.5° < 5 < H  33.5°), Veron's catalog has 100 quasars among which 1 is i n common w i t h the quasar candidate catalog found i n this thesis. Since the unavailability of optical S E D s , the cross-correlation was done using the object position and visual brightness. Table 5.7 shows coordinates, V magnitude and redshift of this object.  VERON  UNMSl  NAME  R X J1730.0+3301  RA  17:30:4  17:30:4  DEC  33:01:02  33:01:05  V  17.9  16.43  z  0.632  0.62  Table 5.7: E q u a t o r i a l coordinates ( R A , D E C ) , apparent V magnitude and redshift z for the quasar common to Veron's catalog and U N M S l quasar catalog.  CHAPTER  6. SUMMARY  AND CONCLUSIONS.  124  CHAPTER 6 SUMMARY AND CONCLUSIONS. T h i s thesis has attempted to develop a method to identify quasars from stars and galaxies included i n the U N M S l catalog, compiled from observations taken i n 1996-1997 and 1999 w i t h the N O D O L i q u i d M i r r o r Telescope. T h e method proposed i n this thesis is based on a x  2  cedure combined w i t h a Bayesian approach. T h e x  2  m i n i m i z a t i o n pro-  parameter (eq. 4.12),  between the theoretical and observed S E D , estimates how well a S E D model fits the data. T h e odds ratio (eq. 4.15), from Bayes' theorem, includes other important considerations: it accounts for the number of models, which differs from category to category (131 stellar templates, 1573 galaxy templates and 1344 quasar templates), and our prior knowledge of the number of quasars to be expected i n 1 degree square at a particular magnitude (surface densities are generally obtained from other surveys). The method was first tested on templates: each was treated as test object, modified by adding noise, resampling the flux measurements i n a jackknife approach and analysed i n terms of x  2  and odds ratio. It was found that 1082  template-objects of 3048 are misclassified: using the notation of Table 4.7, type-I quasars are confused w i t h U V h o t , elliptical and spiral galaxies, type-II quasars are m a i n l y classified as starburst and late spiral galaxies, and G and F stellar types, t y p e - I l l quasars are mostly seen as F type stars and type-IV  CHAPTER  6. SUMMARY  AND CONCLUSIONS.  125  quasars as B type stars. A n important step was to realize how important the number of reliable magnitudes is on classifying objects correctly : i n the U N M S l catalog most of the sources have 10 - 20 niters and only 35% of the entire database have more than 35 flux measurements. If important spectral features are missed, it is easy to misclassify objects. T h e final test was to analyse method's results when more and more filters are randomly neglected. F r o m the distribution of stars, galaxies and quasars ( F i g . 4.16), two features appear: the number of stars increases and the number of quasars decreases when more and more filters are randomly neglected. However, the galaxy distribution is almost flat. T h i s situation and further studies described i n §4 leads us the conclusion that, i n order to classify galaxies properly, w i t h an accuracy of 85% or higher, 20 flux measurements or more are required. To classify stars and quasars, it is necessary to have 30 or more flux measurements. The method, applied to the U N M S l sources satisfying the above criterion and more separated by more than 3 arcsec, produced the results summarized in Table 6.1: Category  Object N U M .  STAR  25193  GALAXY  10791  QUASARS  3056  TOT.  39040  Table 6.1: D i s t r i b u t i o n of star, galaxy and quasar candidates. C o m p a r i n g surface density values cited i n §4 w i t h these results, there are  CHAPTER  6. SUMMARY  AND CONCLUSIONS.  126  more quasars than what are expected. Useful results are obtained from the quasar candidate distribution i n redshift, magnitude and spectral index: there are many objects brighter than magnitude 16, localized at redshift higher than 4, and w i t h the spectral index a lower than -3.5. Considering that bright objects cannot be too distant (otherwise they would emit an extremely large amount of energy) and that, generally, quasar spectra can be well fitted by a power law w i t h spectral index i n the range [2.5, 1], sources satisfying the following criteria are not considered reasonable quasar candidates: • V mag < 16 and z > 3.5; • V mag < 16 and a < -3; • z > 3.5 and a < -3. 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