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Path properties of superprocesses Tribe, Roger 1989

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P A T H PROPERTIES OF S U P E R P R O C E S S E S Roger Tribe B. A.. (Mathematics) University of Cambridge  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY  in THE FACULTY OF GRADUATE STUDIES DEPARTMENT OF MATHEMATICS  We accept this thesis as conforming to the required standard  THE UNIVERSITY OF BRITISH COLUMBIA  June 1989  © Roger Tribe  In  presenting  this  thesis  degree at the University  in partial  and study. I further agree that  of this thesis for scholarly  department publication  or by  his or her representatives.  permission.  AA/VT^gAMrTtCS  The University of British Columbia Vancouver, Canada  Date  DE-6  (2788)  permission for extensive  purposes may be granted  of this thesis for financial gain  Department of  for an advanced  of British Columbia, I agree that the Library shall make it  freely available for reference copying  fulfilment of the requirements  by the head  It is understood  that  of my  copying or  shall not be allowed without my  written  Abstract  Superprocesses are measure valued diffusions that arise as high density limits of particle systems undergoing spatial motion and critical branching.The most closely studied superprocess is super Brownian motion where the underlying spatial motion is Brownian. In chapter 1 we describe the approximating particle systems, the nonstandard model for a superprocess and some known path properties of super Brownian motion. Super Brownian motion is effectively determined by its closed support. In chapter 2 we use the approximating particle systems to derive new path properties for the support process.  We find the  growth rate of the support for the process started at a point mass. We give a representation for the measure at a fixed time in terms of its support. We show that the support at a fixed time is nearly a totally disconnected set. Finally we calculate the Hausdorff dimension of the range of the process over random time sets. A superprocess can be characterised as the solution to a martingale problem and in chapter 3 we use this characterisation to study the properties of general superprocesses.  We investigate when the  real valued process given by the measure of a half space under a super symmetric stable process is a semimartingale. We give a description of the behaviour of a general superprocess and its support near extinction. Finally we consider the problem of recovering the spatial motion from a path of the superprocess.  ii  Table of Contents  Abstract  ii  Acknowledgement  iv  1  2  3  Construction  1  1.1  Introduction  1  1.2  Watanabe's Theorem  2  1.3  The nonstandard model  9  1.4  Super Brownian motion  12  T h e support process of super Brownian motion  16  2.1  The support process started at a point  16  2.2  Recovering the measure from the support  21  2.3  The connected components of the support  24  2.4  The range of the process over random time sets  32  T h e martingale problem characterisation  46  3.1  The measure of a half space  46  3.2  The death point  55  3.3  The support near extinction  3.4  Recovering the spatial motion  .  59 . 67  Bibliography  72  iii  Acknowledgement  I t gives me great pleasure to thank Ed Perkins for acting as my thesis supervisor. He provided a wide variety of interesting problems and constant encouragement throughout my degree.  Chapter 1  Construction  1.1  Introduction  Consider a population containing a large number of individuals. Each individual moves through space and produces offspring during its lifetime. The rules for the spatial motion and the number of offspring of any individual may depend on its location. To obtain a tractable mathematical model for the behaviour of such a population we make some assumptions. The future motion of each individual depends only on its present location. In particular the motion is independent of the behaviour of the rest of the population. At the end of its lifetime each individual produces a random (possibly zero) number of offspring independently of the other individuals. This model could be used to predict the dispersion of an asexually reproducing species. It also applies to the distribution of a rare gene in a diploid gene pool where the chance of two rare genes meeting and hence interacting can be ignored. Because of the assumption of no interaction between individuals many quantities of interest can be calculated. In Sawyer [21] the distribution and joint distribution of new individuals is calculated under a variety of initial distributions. This thesis studies a continuous limit of such a model in which the mean number of offspring is one. To capture the positions of the whole population we consider the state as a measure consisting of small point masses at the locations of each individual. Then as the population size increases and the- mean lifetime decreases we obtain a limiting measure valued process. The total mass represents the size of the population and the measure of a set A represents the number of individuals situated inside a. In some cases the limit process will have a density which can be thought of as a population density. In passing to the limit some features of the original model are lost. For convenience we shall take the lifetimes of the individuals to be of fixed length but the same limiting process is obtained if the lifetimes are exponentially distributed. The exact distribution of the number of offspring is lost and only the variance is preserved, so we shall take critical binary branching where the number of offspring is 0 or 2 each with probability 1/2.  1  This thesis investigates the limiting measure valued process as a mathematical object and many of the theorems describe properties that are of purely mathematical interest. However the estimates needed to prove these properties might be interpreted to give information about the dispersion of a population. For example in section 2.3 we show that the support of the limiting process where the spatial motion is Brownian is nearly a totally disconnected set. To prove this we break the measure into groups of closely related individuals and estimate the fraction of the poulation that occurs in groups that are isolated from the remaining population.  1.2  Watanabe's Theorem  We first give an informal description for a construction of a branching superprocess. We will describe a particle system called a binary branching Feller process which will depend on a parameter /x . The idea is that as we let n increase to infinity the particle system will converge in law to a superprocess. Fix a large integer fi. At time zero we position O(pi) particles in space. On the time interval [0, 1/fi) these particles move independently according to the law of a fixed Feller process. At time 1/fi , for each particle independently we toss a fair coin. If the coin lands tails the particle dies and vanishes. If the coin land heads the particle splits into two. On the interval [l//i,2//i) the particles that are still alive move independently according to the Feller process. At time 2/fi these particles again independently die or split into two. We continue this process for all time. Figure 1.2 shows the evolution for three generations (where we have drawn the particle motion as continuous paths for convenience). One way to keep track of all the particles is to attach mass l / / i to each particle and consider the state at any fixed time as a measure. This measure will be a finite sum of point masses of size l//z. As the branches grow so the measure evolves in time. It is this measure valued process that will approximate a superprocess. We wish to let fx —• co . Notice that the parameter /J has several roles. There are O(fi) initial particles. The mass of each particle is 1/fi so that taking n large and choosing the initial positions carefully we can let the measure at time zero approximate any finite measure. However l//x is also the time between each branching generation. We now investigate the approximating particle systems and in doing so we shall see that a branching rate of 1/fi should lead to a nontrivial limit. The number of particles descended from any one fixed particle is a Galton Watson process. We recall some results from branching processes (see Harris [11] p21-22.) Let (X(n,i)  2  : i,n € N) be I.I.D. random  space  time Figure 1.1: Binary Branching Feller Process. variables on some probability space (ft, T, P) taking values 0 or 2 each with probability 1/2 . Let Zo = 0 and Z = £ f 7 n  =  l  X(n,i). Then lim nP{Z > 0)  =  n  2  (1.1)  n—•oo  Yan^ P(Z„ > nx\Z > 0) n  =  exp(-2z) for all x > 0.  (1.2)  We use these results to analyse the measure at afixedtime t > 0. Look back in time a short distance a > 0 . Let I(t,a) be a list of those particles at time t — a that have descendants alive at time t. We consider only those branches between times t — a and t that end with a living particle at time t ( see figure 1.2 ). We see that the mass at time t comes in 'clusters' rooted at points in I(t,a). Each particle at time t — a has an equal and independent chance of having descendants alive at time t. So the number of clusters is a Binomial random variable with parameters (n,p) where n  =  p =  # of particles at time (t — a) = / i x mass at time (t — a) Prob (one particle having descendants time a later) = P(Z  afJ  3  > 0)  t-a t Figure 1.2: Decomposing the measure into clusters. Now equation (1.1) shows that for large ft, conditional on the mass at time t — a # of clusters «  Poisson with mean equal to (2/o)x mass at (t — a) .  The masses of particles in each cluster are independent random variables and equation (1.2) shows that for large (i mass of particles in a cluster w Exponential with mean equal to (a/2). Note that if the spatial motion is homogeneous then the exact shape of each cluster about its root is identically distributed. Thus the measure at time t is the superposition of an approximately Poisson number of clusters whose masses are independent and approximately exponentially distributed and which are rooted at points uniformly chosen according to the measure at time t — a. This description becomes more and more accurate as /i —• co and is the basis for many properties of superprocesses. As the parameter fi —* co, if we pick the positions of the initial particles so that the initial measures converge, we hope that the approximating particle systems will converge in law to a finite measure valued process. That this is so and the exact way in which the spatial motion meshes with the branching is the content of Watanabe's Theorem. In the remainder of this section we will develop the notation to state this Theorem.This is essentially taken from Dawson,Iscoe and Perkins [5] section 2. We will descibe a labelling system ( first used by Walsh [24] ) which will allow us to point to any branch on a binary branching Feller process. This labelling system is used extensively throughout this thesis. 4  Notation. E  =  £  =  b£  =  bounded Borel measurable functions / : E —• R .  Ci  =  continuous functions / : £ - » R with limits at infinity.  Co  =  continuous functions / : £ - » R vanishing at infinity.  M(E)  =  all measures on (E, S).  MF(E)  =  finite measures on  M\{E)  =  probability measures on  M£(E)  =  :x,€£,tf £NO).  =  f f(x)dm(x) for all m G M {E),f  m(/)  f+(x) =  locally compact separable metric space. Borel c-algebra on E.  (E,£).  E  (E,£).  F  G b£.  f(x)A0.  E&  =  EL) { A } where A is added as a discrete point.  N  =  {1,2,...}  No  =  {0,1,...}  For any metric space M we write D(M) for the space of right continuous paths with left limits mapping [0,oo) —• M with the Skorohod topology and C(M) for the space of continuous paths with the topology of uniform convergence on compacts. We give Mp(E), Mi(E) the topology of weak convergence (which is metrizable). Throughout this thesis C will denote a constant whose exact value is unimportant and may change from line to line. Distinguished constants will be denoted Ci,C2,... There are two underlying sources of randomness for a branching superprocess, a spatial motion and a branching mechanism. A s in the above description we shall take the spatial motion to be a Feller process and the branching mechanism to be critical binary branching. This will be sufficient for all the results of this thesis. We shall briefly describe more general branching superprocesses at the end of this section. Let ((Y : t > 0),(PQ : y G E)) be a Feller process with state space E defined on some probability T  space (QOJ-^O)- Thus (Pg : y G E) is a strong Markov family and its associated semigroup satisfies T  T  : C (E) -> C (E) for all t > 0 0  0  \\T,f — /|| —• 0 as tf —• 0 for all / G  5  C (E). 0  Define A : D(A) C C,(E) — C (E) by 0  Af(x)  =  D(A)  =  lim(T f(x)-f(x))/t t  (/ € C,(E) : lim(T f(x)  - f(x))/t  t  It follows that D(A) is dense in C\(E).  exists uniformly in x)  We may extend the Feller process to E& by setting P*(Y — t  A,Vi>0) =l . Let e be a coin tossing random variable defined on (Sl\,Ti,Pi)  taking the values 0 and 2 each with  probability one half. Let / = U e N ( N °  {0> ^}")- ^he elements of I will label the branches of the branching Feller process.  x  n  If /?= (/?o, ft ,...,#,•) e / we write \0\ = j for the length of the label 0 . If 0 is of length j then it will label a branch upto time (j +  . Write 0 ~ < if  < < < (\0\ + 1)/// so that 0 labels a branch  upto the first branching time after t. Let 0\i = (0o, • • •,/?«) for i < j .Call /? a descendant of 7 and write for 6ome i < |/?|. Let 0(0,7) = \0\ — inf(j : 0|j ^ 7|j) be the number of generations  0 y 7 if 7 =  back that 0 split from 7. Let fi = ( D ( £ A ) x {0,1}) , -F = product a-field . Writing u € fi as w = ( y , e ) 7  a  2  2  G„ = <r((Y ,e ) a  : \a\ < n). Fix n £ N and ( « i ) i N  a  which satisfies for any measurable A C D(En ),B a  a  , e ° )  M  =  0  ^  E ] j A  a  x B  a  )  w  i  s  h  t  o  find  a  |cr|=0  we define  probability P on (fi , ^2) 2  (  1  / ) € A"). JJ  ^(e  M  |or| = 0  € JJ A° x B°|G„)(u;)  al=n  e  °(yA  JJ  =  H=0  W  a e /  C {0,1} and all n > 0  a  t  P{w : (Y°,e°)\  •  e  6  P(u» : ( Y  a  2  G B")  (1.3)  |o| = 0  TJ P * "° ( y ( ( „ i ) / „ ) € j 4 | y ( „ / , 0 = Y ^ ^ w )  =  a  0  A  A  +  \a\=0  x  J]  (  Pl  e  6 5°)  (1.4)  |o|=n  By an adaption of the Kolmogorov extension Theorem there exists a unique probability measure P =  !f '' satisfying (1.3) and (1.4). It follows that i)i  >  P  P(Y so that each V  0  a  e A) = Po"°(XA(|a|+i)/„ e A)  has the law of the Feller process upto time (\a\ + l)/fi when it is frozen. Also from  (1.3),(1.4) ( c ° : a £ I) are I.I.D. copies of e and are independent of (Y  a  : a £ I). The e will indicate a  whether the particles split or die at the branching generations and this will be independent of the spatial motion. Finally from (1.3),(1.4) the random variables (Y G  n  a t  : |a| = n) are conditionally independent given  indicating that the particles move independently between branching times. 6  Let  n = = p(*-')..«  =  E& x n 2 product <7-field. s  x P  iti)i  (s>)i 2  '"  Then for u> = ((x^),-, (Y , e ) e/) particles will start at those z,- that are not equal to A. Define the 0  Q  a  death times for the branches as if a = A  0  0  min((i +  : e°'' = 0)  if this set is nonempty  (|a| +  otherwise  To each branch a £ / we associate a corresponding particle which moves along the branch until the death time. So the position of the particle on the branch a is given by N °={  for t < T°  ( Y"  t  for t > r  [A  Define a filtration where if j/fi <t<(j+  A? = a(Y , a  Also let  A * — \f oAt. For 1  t>  Pi,...  a  l)//i  e° : |a| < j) V f | a(Yf : |/?| = j,s<  «).  ,/3 £ I define the information in the branches /?!,...,/?„ by n  M  :«l=l,...,n).  Now we attach mass 1/fi to each particle and define a measure valued process TV" : [0,oo) —• Mp(E) by N?(A)  =  ( l / » x #(/V° 6 A : a ~ t) a~t  For f £ b£ v/e write  W)=  / f(x)dN?(x)  =  (l/riJ2f( t ) N Q  where we shall always take /(A) = 0. Then TV/* e .4, for all t and TV" £ D(M (E)) almost surely. F  We shall sometimes need the total mass descended from one branch. Define  o~«,a>-/:  If trip = (1/A05Z<LI  M£(E) then we extend (xj),<je to (^i) N by setting X* = A for i > K.  E  i6  for p( >)i.*». This ignores the order of the (x,-)j but note that the order does not affect  We write P "  r  m  the measure on ^ ( A ^ : t > 0) in which we are mainly interested. We shall need a strong Markov property . Let T = (j/n : j = 1,2,...). In Perkins [16] Proposition p  2.3. some shift operators are defined and a strong Markov property is proved for stopping times taking values in  . (The construction in Perkins [16] for super stable processes is slightly different but the  proposition applies here.) Theorem 1.1 (Watanabe [25].) Suppose p »(N»  G •)•-• Q (-)  C(Mp(E)) then Q  m  m  on D(M {E))  m  M  The law Q  G M£(E) —» m G MF(E)  weakly as \i —* co. Then  as ft — oo.  F  is supported on the subset of continuous paths. Writing Q , Xt for the coordinate process and  m  (1.5) again for the restriction to  for the canonical completed right continuous filtration  satisfies the following martingale problem  Xo = m W)  < Xt{f) = m(f) +  Zt(f) The family (Q  m  m  + Z (f) t  for all f G D(A)  martingale s.t. (Z(f))  is a continuous  : m G MF(E))  We reserve the symbol Q  X,(Af)ds  t  = /J  (1.6). X,(f )ds 2  is a strong Markov family.  for the law of the branching super Feller process on path space starting at  m. In Watanabe [25] the convergence of the finite dimensional distributions is proved as well as the continuity of the superprocess under some conditions on the semigroup T . Also the Laplace functional t  of the superprocess is identified as follows. For fixed / G D(A) let u be the unique strong solution of t  the evolution equation  {  du /dt = Au - u$/2 t  t  «o = /  Then E (exp(-X (f))) m  t  = exp(- J u dm). t  (1.7)  For convergence as distributions on D(M) and for the continuity of the paths of the superprocess in general see Roelly-Coppoletta [19] where it is shown that the law of a Markov process has Laplace functional given by (1.7) if and only if it satisfies the martingale problem (M).  From the construction we see that a superprocess inherits from the approximating particle systems the following 'branching' property. If m\,m2 € MF(E) and X},X^ are independent superprocesses started at m i . m j then the process X} + X? has law Q ^+ '. M  M  One can build branching superprocesses with more general branching mechanisms. If the number of offspring has mean zero and a finite variance <r(x) that depends continuously on the position of the parent x £ E then the convergence in Theorem 1.1 still holds and only the variance a of the offspring distribution enters into the limiting process. Fitzsimmons [10] considers infinite variance branching that depends measurably on position and also spatial motion given by a Borel right process. The correct Laplace functional is formally identified and a measure valued Borel right process that has this Laplace functional is constructed.  T h e nonstandard model  1.3  Why use nonstandard analysis to study a superprocess ? In taking the limit in Watanabe's Theorem we have lost the particle picture. The limiting process takes values in the space of measures. It no longer makes sense to talk of particles dying or having descendants. To do calculations using the intuition of the particle picture we must work with the approximating systems and use weak convergence arguments to obtain results about the superprocess.  The idea is to work in the nonstandard universe and to  construct a binary branching Feller process exactly as described in section 1.1 but with an infinite branching rate /i." This will give a process taking values in the nonstandard measures.Theorem 1.2 will -  show that we can derive from this nonstandard process a standard measure valued process in a very simple manner and that this standard process has the law of a superprocess. Now we can argue using particle calculations on the nonstandard model and transfer results to the superprocess . Many of the limiting arguments seem to be built into the model. Thus nonstandard analysis provides a tool to handle weak convergence arguments efficiently. The nonstandard model was introduced by Perkins and used successfully in Perkins [16],[17],[18] and Dawson, Iscoe and Perkins [5]. We now give an informal description of some definitions and results from nonstandard analysis that we hope motivate Theorem 1.2. Cutland [3] gives an introduction to nonstandard analysis for probabilists which is sufficient for our needs. We start with a superstructure V(S) . S will be large enough to contain the basic spaces for constructing the binary branching processes i.e. it will contain the reals ,the metric space E, various measure  9  spaces (fioi -^o) e.t.c. V(S) is the superstructure obtained by repeated use of the power set operation and is large enough to do any calculations with the binary branching processes . The nonstandard model will live in an extended superstructure V('S).  We assume the existence of an embedding * : V(S) —• V('S).  Every object in V(S) has an image under * and the embedding satisfies three properties. i. *R is a proper extension of R. This will imply the existence of infinitesimal elements of *R. We will write elements of *R as underscored characters £ , £ , . . . We identify the image of real numbers r 6 R with their images *r . ii. The transfer principle. This allows us to transfer true statements about objects in V(S) to true statements about their images under the embedding. It will imply, for instance, that *R is an ordered field. A £ V('S)  is called internal if A £ 'B for some B £ V(S). These are precisely the sets in V('S)  that we can describe using the transfer principle. We give one example. Suppose the underlying Feller process was a Poisson process of rate one. Then P (Y 0  = 2) = e-'t /2 for all t £K+ 2  t  The transfer principle now implies that 'P ('Y 0  = 2) = e- - f/2 for all t £ 'R+ 1  L  where we have identified the reals 2,e with the nonstandard reals *2,"e. iii. The saturation principle. This is needed, for instance, in the construction of Loeb measures but its statement would not be helpful here. We can consider the construction of the binary branching Feller processes as a map P : E% x N — Mi(fi) where ((*<),•  -> p( 0<.". Under the embedding we obtain a map 'P : ' ( £ ^ x N ) -+ 'M^Q,). So  if p £ *N,(xO, £  then  x  is an internal probability on  'A). We also have the embedding  of all the particle structure e.g. •N[ £ 'E for all */? £'I,t£ P  A  'R+  To avoid a notational nightmare we drop the * whenever the context makes clear we are talking about a nonstandard object. For instance we write N[ £ 'EA for all /? £ 'I,t £ 'R+ The transfer principle will allow us to do calculations with the nonstandard branching processes as easily as with their standard equivalents. 10  Call r £ ' R infinite if |r| > n, Vn € N. Otherwise it is called finite. Call r 6 ' R infinitesimal if |r| < 1/n, Vn £ N. For every finite r. £ *R there is a unique r £ R such that r — r is infinitesimal. This unique r is called the standard part of r . We write m * r. if JJ — r is 2  2  infinitesimal. Similarly for any metric space M we call mon(j/) = (a; 6 'M : 'd(x, *y) < 1/n, Vn £ N) the monad about y G A/. If x £ mon(y) we call x nearstandard, y the standard part of x and write y = stjv/(x). Let ns(*M) be the set of nearstandard points in 'M.  When the space we are working with is clear it  is common to write "x for the standard part of x . Indeed for r £ *R we shall write r for the standard part . Let (X, X, v) be an internal measure space i.e. A" is an internal set, X is an internal algebra of sets ( closed under *-finite unions) and v is a finitely additive internal measure on X. ( For example {'E,'£,N^)  and (*Q, \4, *P ") are both internal measure spaces.) Define a real valued set function "v m  on X by  •v{A) = {v(A)) for all A G X. m  Loeb showed that the finitely additive measure V has a tr-additive extension denoted by L(u) on the cr-algebra P(X) generated by X. Let L(X) be the completion of <r(X) under L{v). Then (X, L(X), L{v)) is a standard measure space called a Loeb space. If E is a complete separable metric space and u G 'Mp(E)  then there are two ways of obtaining a  standard finite measure; we may take the Loeb measure L{v) or if u is nearstandard we may take its standard part stM (E)(v) F  These are connected by the following result (see Lemma 2 in Anderson and  Rashid [2].) v G ns{'M (E)) F  if and only if L{y)(ns{*E) ) e  =0  and in this case  st (E){v)(A) MF  = L{u){st- \A)) E  for all Ae€.  (1.8)  Finally we have an elementary nonstandard criterion for convergence in a metric space M . Consider a sequence a(n) £ M as a map a : N —• M . We have an extension *a : *N —• "Af. Then a(n) —• a G M if and only if st ('a(n)) M  Now we state the main Theorem of this section.  = a for all fi G *N \ N.  (1.9)  Fix n £ *N \ N and let fi = n! ( this ensure that  Q CP).  11  Notation. We write (*fi,.F, P " ) for the Loeb space ('Vt,L('A), m  we shall often write P, E, 'P, 'E for P * , E », 'P », m  Theorem 1.2 Let m G Mj?(E)  m  m  and choose  is a unique (up to indistinguishability  L{'P »))  . Also when m„ is fixed  m  *£""" respectively. so that $tM (E){ n)  G 'M (E)  m  F  F  ) continuous Mp valued process X  t  =  on  m  -  P ») m  Then there such that  P " - a.s. m  X,(A)  for all i 6 n«C[0, oo)), A 6 S.  = L(N[)(sr (A)) l  (1.10)  Moreover P -(X  € C) = Q (C)  m  m  for all C G  B(C(M (E))). F  This is nearly immediate from Theorem 1.1 and (1.8),(1.9). For the proof see Dawson,Iscoe and Perkins [5] Theorem 2.3. We shall use the nonstandard model for super Brownian motion in R throughout chapter 2. In this d  case there are two very useful results connecting the nonstandard support of the process (Nt_: i > 0) to the support of the process (X  t  : t > 0). These are proved in Dawson, Iscoe and Perkins [5] Lemmas 4.8,  4.9. Lemma 1.3  a. For each nearstandard £ G *[0, co) such that t > 0 , with probability one S(X ) t  = st *(S(N£)). R  (1.11)  b. With probability one, for all nearstandard £,£ G *[0, oo) and y ~ t if0<e<t  1.4  andN? ^ A then °Nl £ S(X,).  (1.12)  Super Brownian motion  Super Brownian motion is the most intensively studied superprocess. We give a summary of those path properties that will be used in this thesis. We assume that the process is started at a finite measure. In dimension one the measure Xt has a density X(t,x)  which is continuous in (0,co) x R. We shall  not consider this case until section 3.1 and delay a careful statement of this result until Theorem 3.1 . In dimension d > 2 the measures X are singular with respect to Lebesgue measure for all t > 0. Thus t  even if the process starts with a smooth density it instantly becomes singular. This result is proved in Dawson and Hochberg [4] for a fixed time and is extended for all times in Perkins [18] in a remarkable way which describes the exact nature of the measure X . t  12  We explain this result now.  For any continuous onto increasing function <j> : [0,oo) —• [0,oo) define a function on the subsets of R by d  oo  <f>m(A) = lim  y^<£(diam(A))  inf  diam(Di)<«  , =  (1.13)  1  where the supremum is taken over all countable covers of A C K  d  using sets of diameter less than 6.  Then <pm(-) is a Borel measure called Hausdorff (^-measure. If <$>(x) = x  d  then <j>m(-) is a multiple of  d-dimensional Lebesgue measure. In general however, (f>m() is not a <r-finite measure. If 4>(x)/x —• oo d  as x —• 0 it gives a way of distinguishing between d-dimensional Lebesgue null sets. If cj>(x) = x then r  <j>m(-) is called Hausdorff r-measure and will give positive measure to smoothly embedded subsets of R  r  (e.g. for a smooth curve C , x m(C) l  = length(C) ). Define for Borel A € R  dim(A) = inf(r > 0 : x m(A) r  d  < oo)  Then dim(A) , the Hausdorff dimension of A, takes values in [0,d] . Note that for A of dimension r we have  i  x'm(A)  0  if s > r  = { e [0,co]  if s = r  oo  if s < r  Notation. For any Borel measure m we write 5(m) for the closed support of m.  Theorem 1.4 (Perkins [18]) Let <j>(x) = x log log (l/a;). Let X be super Brownian motion started 2  +  +  t  at m € Mf(R ) <l  in dimension d > 3. Then there exist constants 0 < Cj < c < oo depending only on d 2  such that with probability one <i>m(A D S(X ))  Cl  t  < X (A) t  < c 4>m(A n S(X,)) 2  So, upto a density bounded inside [ci,c ], the measure X 2  random closed set S(X ). t  Vt > 0, V Borel A  (1.14)  is a deterministic measure spread over a  t  This implies immediately that S(X ) t  has Hausdorff dimension 2 and hence is  Lebesgue null for all t > 0. In dimension 2 there is a less precise result which still implies singularity. Theorem 1.4 allows us to concentrate on the support process (S(X ) t  : t > 0) of super Brownian  motion. The following two Theorems (proved in Dawson, Iscoe and Perkins [5] Theorems 1.1,4.5 ) show that the support moves with finite speed and gives a modulus of continuity for that speed. Notation. For closed A C R and e > 0, let A = (x : d(x, A) < s). d  c  Theorem 1.5 Let h(t) - ^(log^" ) V 1). 1  13  a. For Q  — a.a.u  m  and each c  b. For each t > 0, for Q  m  >  2 ,  3  6(w,c) such that if 0  <  t —s  <  6 then  — a.a.u and each c > y/2, 36(u,c) such that if 0 < s < 6 then S(X )  C  t+s  s(x y ('\ h  t  This Theorem can be derived from a global and local modulus of continuity for the motion of the particles in the nonstandard model. The key is that we can control the motion of all the particles simultaneously. Theorem 1.6 Let h(t) = y/t(\og(t-i)  V 1).  a. For P » —a.a.u and each c > 2, 3 6(u,c) such that i/0 < t — s < 6 for nearstandard s,t £ *[0,oo), m  P~tandN[j:A,  then.\N[ - N[\ < ch(t-  s).  b. For each nearstandard t G *[0,co] , for P™* — a.a.u and each c > y/2 , 36(u,c) such thai if P~t  0<s<6,  then \N£ -Nl\<  + s, N[ ±A  +1  +L  ch(t-s).  Theorem 1.5 follows from Theorem 1.6 and equations (1.11),(1.12) on the support of the nonstandard model.(The local modulus of continuity is not stated in Dawson, Iscoe and Perkins but the proof is entirely similar and simpler than the global modulus.) From the proofs of these results we also note that Q(%) < P) <  <P) = 0 ( p  ( ( c 2 / 2 ) _ 2 )  ) as p -> 0  (1.15)  Super Brownian motion has a space-time-mass scaling property. For (3 > 0 define Kp : M^(R ) —• d  M F ( R ) as follows d  J f(x)Kpm(dx) Proposition 1.7 For m 6 Mp(R ) d  process ( / T K -i, X p  a  the law of the process (X : t > 0) under Q  • < > 0) under  1  pt  — J f((3x)m(dx) for all measurable f. t  m  equals the law of the  Q »" . pK  1  m  For a proof see Roelly-Coppoletta [19] Propositon 1.8. Exact asymptotics for the probability of super Brownian motion giving mass to small balls were proved in Dawson,Iscoe and Perkins [5]. Theorem 1.8  a. For d > 3 there exists a constant C3 G (0,00) depending only on d such that for  any 6 > 0 there exists to and \e - Q (X (B(x, 2  d  m  t  e)) > 0) - c / 3  pt{x, y)m{dy)\ < 6m(l) + e(c + 6) m(l) /2 2  d  3  14  2  for all e < e ,t 0  > 6,x G R , m G M (R ). d  /n particular  d  F  l r n i ( l / - ) Q ( X ( B ( x , ) ) > 0) = c d  2  p (x,y)m(dy)  m  e  t  £  3  t  and for any 6 > 0, K < oo the convergence is uniform overt > 6, x G R , m ( l ) < K. d  b. For d > 3 Mere exists a constant C4 G (0, 00) depending only on d such thai for all x G R , £ > d  0, t > e , m G 2  M (R ) d  F  c. There exists a constant C5 G (0,oo) depending on d such that for all  £,t,m,x  The proofs of parts a,b follow from the proof of Dawson,Iscoe and Perkins [5] Theorem 3.1 and part c follows from Evans and Perkins [8] Lemma 1.3 . Finally we state a result on the effect of changing the initial measure on the law of of the process (Evans and Perkins [8] Corollary 2.4). Theorem 1.9 For any m i , m  G M (R ) d  2  F  and s,t > 0 the laws Q (X mi  G •) and Q (X m3  t  s  G •) are  mutually absolutely continuous. When trying to prove almost sure results about Q (X m  our convenience.  15  t  G •) this will allow us to choose m and t > 0 at  Chapter 2  T h e s u p p o r t process o f super B r o w n i a n m o t i o n  2.1  T h e s u p p o r t process started at a point  For super Brownian motion started at a point mass the growth of the support is controlled by the local modulus of continuity Theorem 1.5b. We show that in this case there is a limit result for the rate o f growth. T h e o r e m 2.1 Lei g(t) = y/2t log^" ) and p(t) - inf{r : S(X )  C 5(0, r)}. Then  1  t  For Q  - a.a.u : lim  6a  44 =  1  First we reinterpret the classical results on Galton Watson processes in (1.1),(1.2) in terms of the nonstandard model. L e m m a 2.2 For nearstandard £, £ > 0 such that x,t > 0 and y ~ t a. /i-P"" *°(jV£(l) >0) « 2*l  1  b. P»~ °(N£(l)  > *|JV£(1) > 0) = -( */')  c. P"~ °(Nl{l)  > x\Nl ± A ) > 2XI" exp(-2z/t)  ls  ls  2  e  1  PROOF OF LEMMA 2.2. Parts a,b follow from (1.1),(1.2) and the transfer principle. For part c, f i x j G {0,1,... ,2"'}. Then a counting argument shows -P"~ (liN£(l)  = j, Nl^A)  lto  = j2~>' 'P ~ °(LiNl t  ti  ls  = j).  Sofor G{0 l//i,2//x ...,2'"/^} £  )  >  •P"~ (N^(l) 1So  > x_\NZ ? A)  =  2" 'P"" * (/V7'(1) > x, Nf ^ A) <  ,  0  2"*  =  £  rp> ~ ((*N£(i) i  = j)  l6o  >  nx'P^^iN^l)  =  xln'P^^iNlil) 16  >) £  > 0)yP"~ °(Nl(l) le  > x\N£(l)  > 0)  and the result follows from parts a,b.D PROOF OF THEOREM 2.1. The local modulus of continuity (Theorem 1.5b ) implies it is enough to show For Q ° - a.a.u : lim inf  >1  s  t-o  g(t)  ~  We use the nonstandard model for super Brownian motion taking x< = 0 , i = 1,..., fx so that the initial mass  equals 6Q. Fix 9 G Q , e G R such that e,0 € (0,1).Define A, = {  Recall that I(9 ,9 ) n  : s u p ( | < | : 0 ~ 6 ) < (1 - CM*")} n  W  lists the particles at time zero that have descendants alive at time 6 .  n  n  7 G 1(9",0") pick 7 ~ 6 such that 7 -< 7 and  ^ A.  n  P{A ) n  p{sup(\N^\: eI(9 ,9 ))<(l- )g(9 ))  < =  For each  n  n  n  £  7  E  -P(\Nl\<(l-e)g(6 )  TT  n  _7€/(»",»")  =  £?[(lW )*<'"'">] n  where J„ = Po(p3s»| > (1 - e)g(9 )) and Z(0 ,9 ) n  n  is the cardinality of I(9 ,0 ).  n  n  n  that Z(9 ,9 )  has a ""-binomial distribution B(n,p) with n = fi and /ip w 20".  Po(\Bi\ >x)>  C(d)x - e- '  n  n  d  2  x3 2  Lemma 2.2 shows Using the bound  for x > 1 we have  P(A ) n  <  °((l-/ p)")  =  exp(-2<r / )  <  exp  n  n  n  (-Cn - 0- ) d  2  cn  Borel Cantelli implies there exists N(u>) < 00 almost surely such that For all n > N(u), 30 ~ 9 such that |JVf. | > (1 - e)ff(0") n  Now fix w such that N(u) < 00 and outside a null set so that the support relations (1.11),(1.12) and the modulus of continuity for particles Theorem 1.6a holds. Choose n > N(u>) such that 0 < 6(u>,3). Find n  0 ~ 6 such that |JV^„| > (1 — e)g(0 ) .The modulus of continuity implies n  n  >  (l-e)g(0 ) n  - 3^"(1 - 9W\og((9»(l for 0  17  n+1  <i<9  n  >  g{6 ) ( l - z -  >  g(t) ( l - e -  (3/V2) ((1 -  n  (3/v/2) ((1 -  0)(1 + log(l - <?)/nlog(0)) ) 1/2  6)(1 + log(l - <?)/nlog(0)) ) 1/2  But -jVf G S(X ) by (1.11) so t  liminf wo 4 <4 ,(<) >- 1 - e - 3>/l - 0 Now take sequences e„ | 0, 9 T 1 to show that with probability one n  liminf44>l Finally we note that the set {u : liminf _o/ ( )/fl'(0 ^ 1} * Borel in C([0, oo), M f ) and so we may >  l  s  (  transfer the result to path space.• • Thus for small t the support is approximately contained in a ball of radius g(t). We may normalise the support so that it has radius one. We show in Corollary 2.5 that as t —• 0 this normalised support 'fills' the whole unit ball. The following Lemmas examine how fast holes appear in the support. Lemma 2.3 For d > 3 , 0 < r < l a. If k < (1 — r )/(d — 2) then with probability one 2  lim  sup d(x,S(X )/g(t)) t  *  <-  = 0.-  x£B(0,r)  0+  b. If k > (1 — r )/(d — 2) then with probability one 2  limsupt *  sup d(x, S(Xt)/g(t))  -  t—0+  a. Fix r € (0,1), ib < (1 - r )/(d7  PROOF,  <*+(i/2).  D e f i n  = oo.  *€B(0,r)  2). We look for holes inside S(X ) n B(0,rg(t)) t  of size  e Grid= ((t z):r = n - / , n = l , . . . 1  3  )  , z £ (t W/2y/d)z ,\x\ k+  d  <rg(tj)  We first show that for small t there are no holes centered at a point of the Grid. For ( n  -ly,3  , x ) G Grid  we have Q^(x n  (5(x,(l/4)n-( « 2  l / 3  : + 1  ) / ) ) = 0) 6  =  g"'  / 3 <  ° (x (5(n / x,(l/4)n-*/ )) = 0) 1  6  3  1  1/3 [l-Q (x (5(n / x,(l/4)n- / ))>0)]"  =  < o  1  1  18  6  f c  3  by first the space-time-mass scaling and then the branching property of super Brownian motion. From Theorem 1.8c Q ° (x {B{n l x,{\/A)n- l )) 6  l e  >u) > C{n- ^l {n ^x)  k 3  k  l  3  A 1).  l  Vl  Pick e such that k < (1 — (1 — e ) r ) / ( d — 2) and choose «o such that (l/4)n~ * _1  2  k  l x\ < ry/{2/i) log n we have for n> n  < e. Noting that  3  6  Q ° (x -i (5( :,(l/4)n-( 6  n  2,:+1  a  /3  0  )/ )) = 0)  <  6  [l-Cn-*( - )/ exp(-r log(n)/3(l-e))' d  2  3  2  ,1/3  expC-Cn^/ )-^- )/ -^^ -'))  <  3  2  3  1  Therefore  J2  Q °(X (B(x,(l/4)i W)) (  =  k+  t  (t,x)€Grid:(<n-  0)<J2  Cn ' (logn) exp(-Cn^- ^- ^ -^ ) kd 3  d  k  r3  1  3  1 / 3  This sums over n and Borel Cantelli implies 3N(u>) < co almost surely such that if (t, x) G Grid and t < N~  then X {B(x,  1/3  (l/4)t W))  >0  k+  t  (2.16)  Fix u such that N(u>) < co and off a null set so that the global modulus of continuity (Theorem 1.5a) holds.Now argue by contradiction. Suppose 3< < min(6(w,3), N' / ,2~ ) 1 3  that X,(B(x,t W))  and an x G B(0,rg(t)) such  20  = 0 .Pick n> N such that (n+1)" / < t < n " / . We use the global modulus  k+  1  3  1  3  of continuity for the support to show there must be a hole (of smaller radius) centered at a grid point.The modulus of continuity implies * „ - , / , (B(x, t  k  +  W _ 3/ ((n)" / - (n + l)" ' ))) = 0 1  3  1  3  l  But **+(l/2) _  3  /  l  (  (  n  ) - l / 3  _  (  n  +  l)"l/3)  >  >  „-(a*+l)/6 _  (l/2)n-(  (  2fc+1  3  n  )/  - 4 / 3  l  o  g  (  3  n  4 / S  )  )  l / 2  6  so that X„-  (B(x,(l/2)rz-( + )/ )) = 0 2 t  I / 3  But we can find x such that (n~^ ,xo) 3  0  1  6  G Grid and B(x , (l/4)n-( * V ) C B(x, ( l / 2 ) n - ( 2  0  Thus X . ,(fl(x (l/4)n-( *+ )/ )) = 0 a  B  I/  0)  19  1  6  +1  6  2l+1  )/ ). 6  which contradicts equation 2.16 . So for small t , for all x G 5(0, rg(t)) we have t~ -^l ^d{x, k  2  S(X )) t  < 1.  Thus limsupi- (21og(i- )) / t  1  1  sup  2  *->0+  d(x,S(Xt)/g(t))  < 1.  x€B(0,r)  But k < (1 — r )/(d — 2) was arbitrary and the result follows. 2  b. Let x have coordinates (rg(t),Q,...  ,0) for f < 1. Then using Theorem 1.8b  t  Q (X (B(xut'))>0)  <  Ct ^p(t  <  C  to  t  +  l  t ,rg(t)) 2t  - /(d-2)-(d/2) ^ '(l ( :  <  +  3 ,  +  - )1  1  If / > (1/2) + (1 — r )/(d — 2) this probability tends to zero as t -+ 0 so that along a fast enough sequence 2  t„ — 0 Borel Cantelli guarantees X (B(x ,(t )')) tn  tn  = 0 for large n. So if k > (1 - r )/(d - 2) 2  n  limsup i- (21og(<- )) / l  1  1  But  sup  2  t-»0+  d(x,S(X )/g(t))>l. t  *6B(0,r)  > (1 — r )/(d — 2) was arbitrary and the result follows. • 2  In dimension 1 and 2 we do not have estimates on the probability of charging small balls given by Theorem 1.8.The following Lemma gives such an estimate and hence an upper bound for the rate at which holes appear in the support in these dimensions .While this bound is certainly not best possible it will be sufficient to prove Corollary 2.5. a. For all x G R ,t  Lemma 2.4  Q °(X (B(x,e)) s  t  b. For  >0  d  = 0) < exp ( - 2 ( 2 x ) / d  = 1,2 and 0 < r < l if k < (1 - r )/d 2  lim t _  PROOF.  a.  x, = 0, i = 1,...  It  will  be  '  t~  d £  I  -(  d+2  ) / e x p ( - - ( | | x | | + ) )) 2  1  l  2  £  then with probability one  sup  k  2  d(x,S(X )/g(t)) t  = 0.  *€B(0,r)  0 +  enough  to  prove  this  for  the  nonstandard model with  I(t,t) lists the particles alive at time zero that have descendents alive at time t.  For each y G I(t,t) pick 7 ~ t such that y -< 7 and N? ^ A . Using (1.11) we have P(X (B(x,e)) t  = 0)  <  ?  =  E [(1 - /,)*<*•'>'  20  f| \76/(*,0  (N?tB(x,e)) /  where I = Po(B 6 B(x,e)). t  t  Z(t,t) has a *-binomial distribution B(fi,p) where up s» 2t  1  . So  p{x (B(^r)) = o) < t  =  exp(-2< 7 )  <  exp (-2(27r) / e r< >' e x p ( - r  -1  t  d  2  d+2  d  The bound is continuous in e so we may replace B(x,e) by  2  HlMI +  E) )) 2  B(x,e).  b. We follow the proof of Theorem 2.3a. replacing the equation 2.16 using the bound above by Q ° (* 6  n  1/3  (fl(x,(l/4)n-(  2i+1  ) / ) ) = o) < e x p ( - C n - « * 6  2  + 1  ) -( d  d + 2  )  + 2 r 3  )/ ) 6  The remainder of the proof carries through.• The Hausdorff metric on compact subsets of R is defined as follows. For K\,Ki d  nonempty compact  sets dH(Ki,K2) dn{Ki,9)  —  max( sup d(x,K2) A 1, sup d(x,Ki)  =  1  A 1)  Combining Theorem 2.1 and Lemmas 2.3,2.4 we obtain Corollary 2.5 If d > 1 then with probability one d (S(X )/g(t), H  2.2  t  5(0TT)) -> 0 as t - 0 + .  Recovering the measure from the support  For a fixed time t > 0, Theorem 1.4 can be improved as follows.  For d > 3 there is a constant c  6  depending only on the dimension so that with probability one X (A) t  = c <t>m(A n S(X )) 6  t  for all Borel A.  The proof, due to Perkins (private communication), uses the 0 — 1 law explained in Proposition 2.11. Thus for d > 3 and fixed t > 0 the measure Xt can be completely recovered from its support. We now give an alternate method for recovering X from its support. It is an analogue of a Theorem t  of Kingman on Brownian local time.Let l(t, x) be the local time of a Brownian motion B . Let Z(t, x) — t  {s < t : B, = x) . Recall that for a closed set A we write A for the set {x : d(x, A) < e}. We also write e  21  Leb(A) for the Lebesgue measure of a set A. In Kingman [13] it is shown that there exists a constant C7 such that for fixed x,t with probability one lim <T  Leb( Z(t, xY) =  1/2  c l{t,x). 7  Theorem 2.6 For d > 3,< > 0 and Borel A of finite Lebesgue measure, with probability one e - Leb(S(X y 2  n A) £ c X (A)  d  t  3  as e — 0  t  where c is the universal constant occuring in Theorem 1.8 a. 3  PROOF. Fix t> 0 and Borel A. Let K\ = e - Leb(S(X ) 2  D A). We shall show  d  t  limsup£((J<0 ) 2  lim E(K Y) c  t  <  c\E{{X {A)) )  =  c E(X (A)Y)  (2.17)  2  t  3  for all Y G I ( V  (2.18)  2  t  The result then follows for lim sup E((K  e  since t —* X (A) t  t  -  c X (A)) ) 2  3  t  =  l i m s u p ( £ ; ( ( ^ ) ) - 2c E{K[X {A))  <  0  +  2  3  i  c E((X (A)) )) 2  2  t  '  is continuous and hence X (A) t  L (\f J -6)-  G  2  R  s>0  T  Proof of (2.17). We shall prove this for the nonstandard model. E{{K' ) ) 2  t  = e~ 4  2d  J  Ax  P(X (B(x,e))>0,X (B(y,e))>0)dxdy. t  t  (2.19)  A  To calculate the probability that occurs as the integrand in (2.19) we use the following idea. Recall that the support moves with a modulus of continuity given by ch(t) where c > 2. We shall choose a suitable value of c later. Using the notation of Theorem 1.6 let G  a  = (u> : 6(u,c) > a) be the set where the  global modulus of continuity for particles holds for time intervals less than a . For a path in G« , the only particles that can enter B(x,c) at time t must lie in B(x, e+ ch(6)) at time t — 6. So if the distance between B(x,e)  and B(y,e) is at least 2ch(6) then on the set d  the events (X (B(x,c)) t  > 0) and (X (B(y,e)) t  , if we condition on the measure  > 0) are independent.  22  X -s, t  Fix a > e ,6 6 ( £ , a ) and x,y such that \x — y\ > 3c/i(i5).We write m\g for the measure m restricted 2  2  to B. Using Theorem 1.8a we can find Co such that for all £ < £o e - P(X (B(x,e)) 4  > 0,X {B(y,e))  2d  t  >  t  a  t  6  <  e - P ' - l e ( ' . ' + ' M < ) ) ( X ( 5 ( x £ ) ) > 0)P <-^ (»-'+°w>(X (B(y,£))  <  e - P *-'(X (B(x,  <  ^c Jp (x,z)X ^(dz)  4  x  2 d  s  x  i  4  2d  s  i  + 6X - (l)  t  3  <  s  t  t (  r  c Jp (x,z)X _ (dz)  + £(c +  l  t  t  s  6) Xl (l)/2^ 2  3  (  + e(c +  6  6) X _ (l)/2^ 2  3  Jp (y,z)X _ (dz)  2  6  > 0)  x  6  3  6  e)) > 0)P ^(X (B(y,e))  x  > 0)  B  1  x ( c Jp (y,z)X . (dz)- 6X - (l)  So for e <  0,G \o-(A - ))  s  t  2  s  + C(6 + e)(l +  6  X _ (l)) 4  6  £o  e~ J 4  J P(X (B(x,e))  2d  > 0,X (B(y, £)) > 0, G )dxdy  t  t  a  AxAn(\x-y\>3ch(S)) <  c E(X (A)) 2  2  + C(6 + e)\A\ (l + E(X _ (l))) 2  6  o (^ _ a) -  Similarly using Theorem 1.8b and conditioning on e~ J 4  JP(X (B(x,e))  2d  (2.20)  4  r  (  t  >0,X (B(y,e))  t  > 0,G )dxdy  t  a  A x A n (\x - y\ < Zch(6)) <  e~ J 4  JP(X (B(x,e))  2d  > 0,G )dxdy  t  'Ax  a  Ar\(\x-y\<3ch(c )) 2  +£ ~ E( 4  J  2d  JP <-'*(X >(B(x,e))>0)P <->(X (B(y,e))>0)dxdy) x  x  c  c3  A x A n (|x - y\ < 3c/i(<5)) <  C m ( l ) m £ - ( £ l o g ( l / £ ) ) + C|yl|(/ (6)) i;(X _ (l)). 2  d  d  d  (2.21)  2  l  t3  Combining (2.20) and (2.21) we see that limsup£((/<7) iGj < c\E{X (A)) 2  + CS  2  t—o  where C depends only on m, d,t,A. However 6 < a was arbitrary and so limsup E((K') la,) 2  c E(X (A)). 2  3  <  To remove the restriction to G we note that P(G ) —> 1 as o | 0 and so it is certainly  enough to show s u p  a  E((K') ) 4  £>0  £8  J  4d  < oo. J J  Jp([[X (B(x ,e))>0)dx ...dx ) t  AxAxAxA \ > 3c/i(£ ) : i^j) 1  (\xi -  a  =  1  2  X  j  23  i  1  4  AxAxAxA  ^  '  , = 1  {\Xi-Xj\>$ch{t*):ii:j)  < C\E{{J  ,{ z)X _ {dz))^+Ct - ^6  Pc  ai  t  id  A  0  using equation (1.15) . Fixing c so that c — 4 > Ad — 8 this expression is uniformly bounded in e. The 2  other regions of AxAxAxA  give smaller contributions as in the derivation of (2.21).  Proof of (2.18). Fix 6 > 0 and C G ?t-6- Using Theorem 1.8a lim£(I *7|^-<) c—0 c  =  \im£ - lc  [ J  2 d  e-0  =  c I 3  Q '- (X (B(x,£))>0)dx x  (  A  / / JA JR"  c  6  V6{x,y)Xt-t{dy)dx  By the uniform integrability of the K\ \imE(l K< ) c  t  =  E(c l  = Since s u p  2.3  E((K') ) 4  e > 0  f  3 c  v (y,A)X . (dy)) 6  t 6  c E(l X (A)). 3  c  t  < oo we may extend to all Y G L (\J 2  6>0  T -s). t  O  T h e connected components of the support  The arguments that lead to the upper bound on the Hausdorff measure of the support use covers of the support that have a Cantor set like structure. Don Dawson asked the following question: For fixed t > 0 , is S(X ) t  a totally disconnected set ?  We now prove the following partial answer. Theorem 2.7 Let Comp(x) denote the connected component of S(X ) t  all m G MF(R ) D  and t > 0, with probability one Comp(x) = {x} for X — a.a.x. t  Notation. For t. a. 6 G T" . 8 ~ t let  7~l+2. .7>-/J 3  24  containing x. If d > 3 then for  Z^(a) is the mass of the 'cluster' of particles descended from  that are alive at time t + a . The 2  following lemma shows that there is a good chance ( independent of a ) that these particles have not spread more than a distance 0(a) from their common root. For m 6 Mp(R ) d  such that s < M ( / i ) m  m  F  choose  6  'Mp(K ) d  -  Lemma 2.8 For nearstandard a, 0_€ *[0,co) such that a = "a > 0,0 = °0_ > 0 we Aaue P "(W^(a,i)  = 0\Z (a)>0)  m  £"  p  {Z (a)\(W {a,0)  m  0  = 0)|Z"(a) > 0)  0  =  p(0)  =  r(0)a  (2.22) 2  (2.23)  where if 0 € R, 0 > 0 Men p(0) > 0, r(0) > 0 and QW°( (B(O,0y)  = 0)  =  exp(p(0)-l)  (2.24)  £?d/»)«-(X (fl(0 fl))I(X (S(0,fl) ) = 0))  =  r(0)exp(p(0)-l)  (2.25)  Xl  e  1  )  1  PROOF. This is essentially due to scaling (Proposition 1.7.) Fix nearstandard a , £ such that a,6 > 0. For 0 such that /?| 7^ A define 0  p(a,S.)  =  'P "(W (a,0)  r(a,S)  =  'E *(Z (a)I(W (a,l)  m  =  p  m  p  O\Z^(a)>O) =  0  The values of p(a,0), r(a,0) do not depend on the choice of  O)\Z (a)>O) p  or /?.Take x, = 0 for i = 1,..., [/ia /2] 2  and Xj = A otherwise , so that s < ( m ) = (a /2)6o- Then 2  mp  /J  Q(I/2)«O(  X I ( S  =  P »{Na'(st- (B(O,a0) ))  =  P '(N (B(0,aB) )  a  2  o  a 3  m  1  m  e  e?  [^ /2] 3  1=1  = 0) = O)  = 0)  = 0, almost surely for any r. So  e  n  c  c  Jfl  = ^"(  o)  =  g(° / ^ (X (fl(0,a^)  Q( /2)«o( (B(0,fl) ) = 0) m  QY)  0  =  using scaling and the fact that Xi(dB(0,r)) 1  (  (w '(fi>£)=°)) r  25  . JJ  -P "(W -(a,0) m  = O)  x  1=1 > JJ ('P «(W '(a.,£) m  = 0\Z (a)  x  > Q)'P >{Z (a)  Xi  m  •=i  > 0) + 'P "(Z (a.)  Xi  m  Xi  = 0))  t^ /2]l 3  (l + =  (p(a,l)-iyP -(Z \a)>0)) m  x  exp(°p(a,£)-l)  since [/ia /2]*P "(Z (a) > 0) ~ 1 from Lemma 2.2a. So °p(a,£) is constant in a and (2.22) and (2.24) 2  m  j;,  follow taking p(6) =  °p(a,6).  Similarly E °'  (x (B(o, emx^Bio,  2  6  ey) = o))  1  =  E ' °> ( a - X , ( 5 ( 0 , af?))I(X »(B(0,a0) ) = 0))  =  ( a " ) ' l'E( £  A  6  2  2  c  a  /  a  Irt'M  \  a  fl  Xi  i=l  /W/2]  = ( )° E a_2  t^ /2]  Z (*W  2  ^ ( ' l ^ ) = 0)) i  ; = 1  [/.a /2] 3  ^(^*(a)l^(a,£) = 0)-P( i  f| W >(a,e) x  = 0)  •=i [^ /2) 3  a- exp(p('?))-l)r[ 2  W ( a ) I ( W * ' ( f l . f i ) = 0)|Z*'(fl)>0) r  •=i  x*P(Z (a) > 0 ) / * P ( W ' ( a , £ ) = 0)] Ii  r  a- exp(p('?)) - 1))° (>a /2]-P(Z"(a) > 0)r(a,£)/(l - (1 - p ( a , £ ) ) ' P ( ^ ( a ) > 0))) 2  2  a- exp(p(0))-l)rr(a,£) 2  So ( a ) -2  °r(a,£) is independent of a  and (2.23) , (2.25) follow taking r(6) = ( a ) -2  °r(a,£) .  Finally Q °/ (A:i(R ) = 0) = exp(-l) so that p(6), r(6) > 0 will follow if we can show {  2  d  Q ' (Xi(B(0,0) ) So  3  e  = 0,X (R ) d  1  # 0) > 0  But since the support of the process moves with finite speed , for small enough s we have Q {x,(B{o,ey) So/2  = o,x,(R ) # o) > o d  and Theorem 1.9 implies (2.26) holds.• Notation.  26  (2.26)  For a G (0, co) define Q  a  = {(y,r,S)  € Q x Q x Q : r,6 > 0 , \y\ < r - 6 , \y\ + r + 6 < a} d  For a G [0,co), m G Aff(R ) define Ann(m,a) C R by d  Ann(m,a) =  d  (^J | x 6 R : m(z : r — 6 < \z — x — y\ < r + 6 ) | (y,M)eQ d  a  For a € *[0,co), m G * M ( R ) define Ann(m,a) C 'R d  by  d  F  Ann(m, a) =  | x G *R : m(z : r — <5 < |z — x — y\ < r + <5)| d  (y,r,«)eQ« Ann(m,a) is denned so that for x G Ann(m,a) there is a mass free annulus of positive standard rational thickness that disconnects x from J9(x, a) . c  Note that if x G Ann(X ,a) then Comp(x) C 5(x,a) . Thus t  oo x G j^l Ann(X<,n )  Comp(x) C {x}  -1  n=l  For t,a,9 G T", /? ~ t ,N[ ^ A define ^ ( a , £ ) = p"  1  £  I(|/V7  + f t a  _ iVf | < 2 a £ )  7~l+£ ,7> 0 2  i  Note that if a,9_,t G T ' , a > 0, 0 < 9 < 1/2, /? ~ £ are such that A^f_  £ A, Wl±-^(a,0) =  4  VV'-  - i a  o3  ( a i £ ) = 0 then there is a particle free annulus surrounding Nf  N[(z:  5 a £ / 4 < \z - N^_^\ < 7a 9/4) = 0  \N[-Nl_^\<a0 We may shift the annulus slightly to be centered at a rational and have positive rational thickness s o that N[ G Ann(Ar/\a). The following lemma shows that on average a positive fraction of the initial particles will lie inside Ann(W ",&). t  L e m m a 2.9 For nearstandard a,t  G  with 0 < a < 1, 2 a / < t < co, d > 3 if stM(m^) 1  3  M p ( R ) Men Mere exists a constant p > 0 depending only on d such that d  m  E  u  j  ^ T i ^ G A n n ( / V £ , f l ) , A £ ± A) j  27  > />m(R ) d  — m  G  PROOF. The remark following the definition of V ( a , £ ) shows that 7  E  32 W  (^  Ann(/V^, ), TV # A )  e  7  £  7~t  >  £ I//" 32 ( L 7~l  =  E  1  1  ]T  *  N  ^ (  a  A  -  ) i ( ^ (  w n  a  >  '  1 1  "-  £ ) =  = 0, V  ^ (  a  , £ )  /  :  £[  ]T  " ^ ( a , £ ) = 0)  0)  =  \7~i-a ' =  7 l i  (2.27)  • £ ( ^ ( . a ) I ( ^ ( , £ ) = 0 ) | A _ O * - P ( ^ ( a , £ ) = OM _ O a  \7~i-a  a  1  a  3  the random variables V(g.,6_) and ^ ( f i t ) I ( W ( f l , £ ) = 0) are ""-independent.  since conditional on At-^  7  7  Now using the *-Markov property (see Perkins [16] Proposition 2.3. )  "P (v (fl,fi) = o)M 1 _ 4 0 n,  _  . . p 1-M.  =  Q*.-. (X (B(-,2a<?)) = 0) |  i - * ( j V > ( B ( - , 2 a £ ) ) = 0) 3  3  a3  7  •< t-o» v  where in the second step we used the continuity in 0 of Q (X,(B(x,2a9))  = 0) and the fact that  m  st (N^_ M  - n~ 6 y  X - 2.  ) =  1  3  N  t  a  Also  -75 ( ^ ( a ) I ( W " ( a , £ ) =  0)H t _ f t 0  =  "2? ( / ^ ( a ) I ( ^ ( a , £ ) = 0 ) | A £  =  • [•£(^(ft)I(W (fl,fi) =  =  2r(0)I(Ar _ , # A )  n,  f t a  # A) I(A£ , ^ A ) £  0|Z ( ) > 0)/i-P 7  A  (z^Ol) >  0|A^_  A A  # A ) ]  7  £  using Lemma 2.8 and Lemma 2.2. Substituting into equation 2.27 we get  W  E\^32  € A n n ^ . o ) , TV ^ A ) ] 7  7~1  >  2r(0)£  Z^"  1  £  * A)Q^.-  2 a * ) ) = 0)  3  7~i =  2r{0)E  Q '- (X ,(B(x,2a0)) x  =  3  a  28  0)dX _ ,(x) t  a  "AT  7  I(iV7_ , a  * A)  >  2r{9)E  ( l - c (2a9) - (2ira (l d  2  + 49 ))~ '  2  2  4  .J  d  2  exp(-(* - y) (2a (l + 40 ))- )dX _ ,(y)) 2  2  2  dX _ ,(x)  1  (  o  t  a  using the estimate on hitting balls in Theorem 1.8b. Lemma 2.10 gives an estimate on the expectation of this double integral and leads directly to  E ^  >  £  I(A£ € Ann(/V/\a), JV? # A) j  2 r ( e ) m ( R ) ( l - C T - ( l + 4e ) - / (2 / - m(R ))) d  d  2  2  1  d  2  d  2  d  r  Now take 9 > 0 small enough so that the right hand side is strictly positive. • Lemma 2.10 IfQ < a < l,t > a ' ,d> l  E  m  3  3,m € M ( R ) then d  F  | y J e x p ( - ( * - y) /2a)dX  dX  2  t  < am(R )(2 ' + m(R )) d  t  d  2  d  PROOF. Let pt(x) be the Brownian transition density with associated semigroup P . We have for positive t  measurable f,g (see Dynkin [6] Theorem 1.1)  E  [X (f)X (g)]  m  t  = JP f{x)dm(x)  t  JP g{x)dm{x) + j dm{x) J* P _,{P.f  t  t  By approximating positive measurable h(x,y) by functions of the form E [J  J h(x,y)dX (x)dX (y)]  m  t  t  =  Jdm( )  YA=I  2  t  2  x  Zl  x  t  x  w  e  2  have  z) 2  - Zi)p,(y  Jdz jdz p,{y  z )M i> z ) 2  -  2  2  (m(R )) sup E \h{B], B )] + m(R ) f sup E [h(Bl, B )] d  2  2  d  2l,23  where B},B  f>( )9i( )  Jdm(z ) Jdx Jdy h(x,y)p (x - )p (y -  Zl  + Jdm{x) J^ds Jdyp,{x-y) <  P,g){x)ds  t  JO  are independent Brownian motions satrting at zi,z .Foi  (2.28)  2  2i,22  h(x,y) = exp(—(x — y) /2a) we 2  2  have sup E \h{B],B ,)) = /exp(-i /2a)p ,(x)da; < {2a/a + 2s) . Substituting into equation (2.28) and using the bounds on a and t gives the result. • 2  2  d/2  2  Proof of Theorem 2.7 We prove the result first for the nonstandard model with x^ = 0 for i = 1,. . . / i , xt = A for i > \x so that if  = fi'  1  £,-6*,I(zi ? ) A  29  t  h  e  n  P "{X m  G •) = Q °(X S  G •)•  F  r  o  m  Lemma 2.9 we have for nearstandard 0 , ( 6 ^ such that 0 < a < 1, 2 a / < t < co 1  0<p  < E I (i-  =  P(-\N^  G Ann(N[, a), N? ± A)  £* (*L  E  P  e  Ann(A^,<i)|A£ ^  A)I(A? ^ A)  P {Ni G Ann(A^,a)|A£ ^ A )  = where  W  1  3  ^ A) is the Loeb measure induced by  *P(-\N"£ ^ A) . Since Ann(N£,a)  decreases as a  decreases we have for any 7 ~ t 00  P(Nl G f | Ann(A^, n " ) ^ ^ A) > p > 0  (2.29)  1  n=l  We now use a zero-one law to show this probability is in fact 1. Notation. Fix 7 ~ £ . For a,u G T' , 1  < £ define  W**  =  V(JV> - A V ^ : a i < / ' - V ( / ? 7 ) < £ ) V (  w  =  yw -ij,  t  r  V  t  M  A r  I  7 )  t -A 7- =Ii<l<ii) r  i  n  n =  Wo+  f|W„-i n  The following two results are due to Ed Perkins ( personal communication.) Proposition  2.11 For A G H +,  Proposition  2.12 IfO<a<  0  2~  P{A\N£ 4/d  ^ A)  = 0 or 1.  P(-|A£ ^ A) almost surely , for r small enough  then  d ({N[ : ^-V(/?, ) > (2r)«}, A ? ) > r T  For {y,r,6) G Q , u,v£ a  r  y,rA«,«i(7)  =  , u < £ define  {u:\Nl-NZ-y\$(r-6,r OO  r  W  =  OO  Ufl  6)foT l\psl.u<Li- o-(p, )<v}  +  1  a  1  OO  U  fl  rv,rAi-, -'(T)e7(o t  +  i = l n = l (s/,r,«)€Q -i i = t + l n  If w G HnLii^i  P(n )\N?_ 7  A n n ( A , n ) then w G T(7) and so equation 2.29 and the zero-one law imply 7  _1  i  ± A) = 1. Let A(7) = {w : for small r , d{{N[ : LI~ CT(P, ) > ( 2 r ) ° } , A£) > r} 1  7  30  so that Proposition 2.12 says P(A(y)\N?  E ( n" £ 1  / A) = 1. Then  r( ) n A( ), A?  I(u» g  E* ( F  7  7  * r( ) n A(7)|/V^ # A)I(A£  W  =  ^ (v'  =  p( ^r( )nA( )|/v ^A)  =  1  # A)  T  # A)  7  w  7  7  i  0  From the global modulus of continuity for particles, with probability one all the particles move only an infinitessimal distance in an infinitessimal time. So equation (1.11) and the above imply we can pick a single P null set  such that if u> £ N we have simultaneously  For all nearstandard £ < £ and /? ~ 1, Aff ^ A we have iVf w A f  (2.31)  st(5(ATi)) = S(X«)  (2.32)  Now fix ui g N, 7 ~ t such that N£ ^ A, w 6 T(7) p| A(y) . We claim oo  °L N  oo w  e  0  1/2  - 1  _ 1  )  u  r^A;- .*-^)  n  1  (»,<-,«)e<3„-i j=*+i  < fc and -1  d({N[ Pick n so that n  (^,n  oo  n n=1  0  A n n  n= l  To show this find k so that  Find r such that ( 2 r )  fl  G  : fi~ a(P,y) l  > (2r ) / }, A ? ) > r 1  2  0  0  (2.33)  < ro and find (y, r, 5) £ Q „ - i so that oo  "6  fl r  Vir>4j  31  - -,( ) lit  7  (2.34)  For 0 ~ t such that Li- o-(P,y)  > (2r )  1  Wl-iq-y\^(r-6 r  equation (2.33) ensures that \N[ - AT | > n'  1/2  7  0  1  and so  j-i  ^-1(7)  + S).  t  For P ~ t such that 0 < °(/i (r(/?, 7 ) ) < fc -1  -1  equation (2.34) and the definition of T  yri  ensure |Arf - A^ - y\ & (r - 6, r + 6). 7  For P ~ < such that fi~ a(P,y) w 0 equation (2.31) ensures Aff ss N . So 1  7  G • R : r - 6 < \z - A ? - y\ < r + d  {2  n {jV* : 0 ~ t} = 0  Equation (2.32) now gives { 2 € R : r - 6 < \z - "AT - y\ < r + s} D S(-Y«) = 0 d  7  Since we were free to pick n arbitrarily large this proves the claim. Now equation (2.30) and the claim give OO  7~1  n=l  so that for P ° — a.a.u 6  00  X (R  d  t  \ p| AnnpT,.n" )) = 0  (2.35)  1  n=l  It is possible to show that the map m — • m(R \ H ^ L j A n n ( m , n ) ) is Borel measurable. So we can d  -1  apply Theorem 1.9 and conclude that for any m G Mp{R )  equation (2.35) holds for Q  d  2.4  m  — a.a.w.O  T h e range of the process over random time sets  In Perkins [17] it is shown that with probability one the support process (S(X ) t  : t > 0) has right  continuous paths with left limits in the Hausdorff topology on compact sets. If we write S(X )t  lim,jt S(X,) then S(X )~ t  D S(X ) t  and \J S(X )t>0  \ S(X )  t  t  for  is shown to be almost surely a countable  set of points. We deduce that if A is a Borel subset of (0, 00) then (J  t€A  S(X )  is almost surely a Borel  t  subset of R since for any to > 0 d  [j S(X )\ teAn[t ,oo) t  0  (J t£An[t ,oo) 0  S(X )c\JS(Xt)-\S(X ). «>o t  t  Note also that Hausdorff dimensions are unaffected by the addition of countably many points . In Dawson,Iscoe and Perkins [5] , if d > 4, suitable Hausdorff measure functions are found for the set U <t<bS(Xt) a  from which it follows that the Hausdorff dimension is 4. We now find the dimension  32  of \J £ t  A  S(X )  for possibly random time sets A. This is an analogue of a Theorem of Kauffman for  t  Brownian motion. In Kauffman [12] it is shown that if Bt is a two dimensional Brownian motion then with probability one dim(B : t £ A) = 2dim(,4) for all Borel A C [0,oo) t  and the result is true for Brownian motion in dimensions d > 2. Notation. For R,K£  [0,oo) define r = inf{i > 0 : X (R ) d  k  t  <r = inf{< > 0 : X (B(0, k  t  = k) k) ) > 0} c  Theorem 2.13 For d > 4 and any initial measure m £ Mp(R ), d  dim ( |J S(X ) t  1 = 2 + 2dim( 4) for all nonempty Borel A C (0, r ) J  PROOF. Lower bound. Let Tk — k A  dim ^ y  S{X )^j  (2.36)  0  A r - i A ov We will show that if m £ MF(R )  is of compact  d  t  support, k £N satisfies m(R ) £ (Jb ,ife) and e > 0 then for Q d  with probability one  _1  m  — a.a.u  > 2 + 2dim(^) - 2e for all nonempty Borel A C {k~ ,T )  (2 37)  l  t  k  If m £ M f ( R ) is of compact support then almost surely the total mass remains bounded and the d  support remains bounded so that Tk ] T < oo as k —•co and dim(j4 l~l (k~ ,Tk)) T dim(A n (0, r )). l  0  So we may take sequences k  0  | c o , e „ { 0 to conclude that if m £ Mp(R )  n  then the lower bound on the dimension of Ut£AS(X )  in equation (2.36) holds Q  m  t  initial measures m £ M (R ) d  F  is of compact support  d  almost surely. For  we can argue by decomposing m into countably many finite measures with  compact support and use the branching property. Fix m £ Mp{R )  of compact support, k £ {2  d  : n — 1,2,...} satisfying m(R ) £ (k~ ,k)  n  e £ (0,1).Define a time grid as follows. Let  = j2~ , n  any fixed sample path we say 7" charges B(x,a) large n we do not expect many balls B(x,2~ ) n  l  and  T" = {tj : j = 1,2...}, If = [t?,t? ).  Along  d  +1  if there exists s £ J " such that X,(B(x,a))  > 0. For  to be charged repeatedly by many of the Jj"'s. The  following Lemma shows that there is not much mass in such balls . L e m m a 2.14 For Q  m  — a.a.u and sufficiently large n  X <x: { t  Y l(lf chargesB(x,2- )) /?»c[*-',*] n  n  33  >2) nc  i < 2-( J  d+5  >  n  (2.38)  for all t 6 T " n[0,ifc] 3  +6  For any fixed ball of radius a , if it is charged by the measure X we do not expect it to be charged much t  more than a . In Perkins [16] (proof of Theorem 4.5) the following very precise result is shown. There 2  exist constants c ,Ci > 0 such that for Q  — a.a.u and sufficiently large n  m  a  X {x : X (B(x,2- ))  > c log(n)2- "} < c /n  n  t  2  t  fl  Define a space grid as follows . Let G£ = d~ ^ 2~ Z 1  3 of the balls {B(x ,2- ) d  m  2  n  for all t E [k~ ,k]  (2.39)  l  0 73(0, k + 1). Note that any point is in at most  d  : x G G£).  n  t  Lemma 2.15 For Q  2  k  {  — a.a.u and sufficiently large n sup X (B(x,  2"")) < 2^ ^ X ^(B(x,2"("- ))) d+  t  n  (2.40)  1  t  for all t G T  n[0,ifc],x G G£.  3n  We delay the proofs of Lemmas 2.14 , 2.15 . The strategy of the proof is as follows . Given a cover C of \J £ t  S(X )  A  we will construct a cover C of A x [0, l/27fc]. Since A x [0, l/2Jfc] has dimension at least  t  A  1 + dim(yl) (see Falconer [9] Corollary 5.10 ) this will lead to a lower bound on how efficiently we can cover U( 4  S(X ). t  6y  Fix u and n < oo so that equations (2.38), (2.39) ,2.40 hold for n > n and so that z 2~ °2 d  0  l/4fc and E > c j / n n  \J  teA  S(X ) t  n  2d+w  0  no  2  < l/4fc. Fix Borel A C (k~ ,Tk) l  C 73(0, k). Choose a cover of \J  <  of dimension a. Since A C [0, cr ) we have k  S(X )  i€A  t  C = {Bt = 73(x,,2-"') : i = 1,2...} with x,- e G^^m  > n + 2.  (2.41)  0  We assume all the balls in C are distinct . Split the cover into two parts C\ = {73, • i E Ii},C  2  = {73, :  * € h} where  S -) <ni  = | * :  2  £  I(7/ '(n  charges 73(x, 2"("-- ))) > 2<"'- > >  2)  ieh  if  B{x 2-< -»)  i e h  if  73(x,',2 (  2  _  S -)  C  ni  it  ni_1  2  fni  3  ^)n5( ._2) ^ 0 C  n  For each i G 7 find z such that 73(s,-, 2-( ' )) C 73(zi,2-(" )) and less than 2 ( ' ) of the intervals n,  2  7  2(  n i  -2)  j_!  g cf  _1  i_2  t  )fc  j  c h a r g e  (.2-(n.-2)  S zi  )  34  n  _2  f  Now we form the cover C of A x [0,1/2Jfc] . Consider the balls in C one by one in order of decreasing A  2  radius. For a ball B(xi,2~ )  G C there are less than 4.2(  ni  ni_2  2  )  of the intervals lf  £  ni  C [k~\k] that  charge J3(XJ,2~"<). For each J "' that does charge S(x,-,2 ) choose a rectangle in  x R based  _ni  2  above 7 "' and of height c„ log(n,- — l ) 2 ( ' ) so that the base of the rectangle lies on top of any 2  _ 2  n  - 1  previous rectangle above I "' ( or the x-axis if there are none ). Repeating this procedure for each ball 2  Bi G Ci gives a collection of rectangles which we call CA- Note that each B(xi,2~"') 4_2("i-2>  gave rise to at most  rectangles of diameter less than (1 + c )log(n,- - l)2- ( ' >. 2  n  _1  a  We now check that C covers A x [0,1/2k). Fix t G A and let {" = sup{<? : t] < t}. Let J„ = {i G A  h : tli = n) so that h = Un>n +2 nJ  0  Y  <  2^ >  <  2(" ) 3 X - ( (J  B( ,2-("-V))  <  2<> V Z X  YI  d+A  +4  n  X (B( ,2-^- >) Xj  d  t  d+  n  (by equation (2.40))  lS  ian  3n  Xj  I x :  d  i3n  I(/ "" 2(  2)  charges S(x,2-("" ))) >2("" ) 2  2  £  J <"- >C[Jt-i,Jfc] 3  <  2  (d+4)n d -(d 3  2  3  )( -2)  + 5  n  (by equation 2.38)  So  YMBi) = Y Ew^-'2-")) n>n +2jeJ„  «'€/i  0  < Since t < T - i we know that X (R ) d  t  t  z2 d  2- °  2d+10  < 1/4*.  n  Let C = U ( „ > „ 2 ) {  > l/k.  a :  X (B{x,2~ ))  :  n  0 +  t  t  Equation (2.39) now gives 3/4fc  < <  Xt(S(X )\Ct)' t  Y  X (Bi)I(B gCt,X (B )>0) t  i  t  i  ie/iU/a  l/4k+^2Xt{B )l(B gC ,X (B )>0)  <  i  i  e/  3  35  i  t  t  i  > c log(n)2- "} 2  a  If B(x 2- ) ni  it  £ C choose y< € B(x t  2~ ) n Cf. Then ni  it  1/2*  <  ^^( ^'- " 5  <  ^  2  ( n i  c (ni - l ) 2  "  1 )  I  X  S  t  2 ( n i  6  )) ( '( ')>0,5,gC )  " I([P ,t 1 )  , i  2 n i  + 2~ ) charges B.)  (2.42)  7ni  The right hand side of equation (2.42) is the total height of the rectangles in C  that lie above t. So  A  indeed C covers A x [0,1/2*]. A  Now suppose that dim([_L S(X )) €j4  < 2j < 4. Then we can find a sequence of covers { C } m  t  m  of the  form 2.41 satisfying diam(C ) —» 0 and m  Y Then diam(C^*)  |B,f = ^ 2 " 7  2 7 n  ' < 1 for all m  0 and £  < ^4.2<  l#r  +£  <  n i  - > ( ( l + c )(nj a  fl  i )  2  -  2  ( « ' -  1  ) ) T +  £  Constant for all m.  So 1 + a < dim(A x [0,1/2*]) < 7 + e Therefore 27 > 2 + 2a - 2e and dim( [ J S(X,)) > 2 + 2a - 2e and the lower bound is finished. Upper bound. The upper bound is a straightforward application of the global modulus of continuity for the motion of the particles in the nonstandard model for super Brownian motion. It will be enough to show that if m € Mjp(R ) and r n ( R ) < * then for Q d  d i m ( y S{X )) t  m  d  — a.a.u.  < 2 + 2dim(,4) for all Borel A C (0, r A * ) . k  t£A  Fix rrif, G * M £ ( * R ) so that stjv/ (m ) = m and fix * G N so that m(l) < *. Define for £, e_ G {j/f* • j G d  F  /J  'N} Hi, £) = {7 ~ t • 30 ~ t + £, 0 y 7, N?  36  +L  ? A}  Z(i,L) = \I(LL)\ Conditional on Ay Z(t,£_) has a *-Binomial distribution under *P with parameters fiNf(l) *P" °(N^(l)  and p = L  > 0). From Lemma 2.2 °(/ip£_) = 2 / £ whenever £ is nearstandard . Let Poisson(n) be a  ll  random variable having the Poisson distribution with mean n under Po. P (3i? € [0,/fc A r ] such that Z(t],2~ ) n  k  <  £ P [Z{t],2- ) «;e[o,t]  > k2^ \r  <  jfc2 P (Poisson(/fc2 ) > jfe C" >)  <  Jfe2 exp(-ifc2 (2-e / ))  n  k  n+1  (n+2  > i?)  n+2  n  > Jfc2 ))  +2  0  2  n  n+1  1  2  which sums over n. Thus for ui off a single null set we may find n (w) < oo so that Z(t", 2 ) < fc2(" ) _ n  +2  0  for all t" £ [0, k A T*] and n > no, so that the global modulus of continuity for particles (Theorem 1.6a) holds with 2~ ° < 6(u,3) and so that equation (1.12) holds. n  Fix u so that no(w) < oo. Fix A C (0,r  A k) of dimension a. Given £ > 0 we can find a cover  k  C = {I?; : i = 1,2 ...}  of A so that diam(C) < £ and \p "'( '+ ) < 1 Q  e  2  i  Using the modulus of continuity we have  U  e UU e  g  (J  S(/V .  7€/(<;;_ ,2—o  ,3M2- "'- )) (  7  i  (2.43)  1)  J i  1  The right hand side of (2.43) forms a cover Cs of UttAS(X ) of diameter less than 6h(2e) satisfying t  ^2 ] \  2a+2+3c  <  B  Bec  ^jfc2"'+ |5 /n72- '/ | n  2  2  2o+2+3e  N  •'  s  <  Constant  Choosing a sequence of covers of decreasing diameter gives dim((J.5(X )) < 2 + 2dim(,4) f o r P - a . a . w . t£A t  It is possible to show that the set |w : for sufficiently large n, if 7"  C  [0,k A r ] then k  37  (Jte/"  ^( <) w  c  a  n  D  e  covered  by k2  balls with rational centers and radius 3/i(2-( ~ ))}  n+1  n  is Borel in C([0, oo), Mp(K )).  1  So by transfering to path space at the correct point in the proof we can  d  show the lower bound holds Q  m  almost surely. •  Notation. Define GMC  - |w : | A f - N[ I < Zh(t -  n  for all nearstandard  s,t,0~t  satisfying 0 < t - s < 2 , N[ £ A | _ n  Then from (1.15) P(GMC^)  -+ 0 geometrically fast.  Proof of lemma 2.15. It is enough to prove the lemma for the nonstandard model. P ( 3t G T  3 n  D [0, Jk], x e GI s.t.  sup  X,(B(x,  2"")) > 2^ ^ X (B(x, +  2"( - ))))  n  n  t  1  «e[<,<+2-»»]  \  <  y"p(GMC „n{  Y\  sup  3  t T'-n(0,t]x€G 6  ;  V  J  X,(5(x,2-"))>2(  d+4  )"X (5(x,2-("- ))})) 1  t  .e[f,«+2-»»]  )  + P(GMCiJ  (2.44)  Now sup  P(GMC „n( 3  V  < 'P I  X,(B(x,2-")) > 2 (  d+4  ) ^(5(x,2-( -^))) ) n  n  «€[t,<+2-»»)  /i" Y  sup  7  „  # >  W+.  1  V£€[0,2-»-]  y  € B^^-^"- )))  A  1  T +  > 2(  d+4  )"/i- ^I(Af 1  7  e  B(x,2-( -V))\ n  7~<  Let v =  /i-'E^W  € -B(x, 2~(" ))). Then using the *-Markov property at time t the process  £~«+, Wt^ A,TV T  /  -1  7  G ^ ( x ^ - ^ - ) ) ) : £ > 0} has the same law as {A^(l) : s > 0} under P" 1  . But P"(  sup  N?(l) > 2  (d+4  ) A "(l)) n  r  0  »€[0,2-»»]  <  P"(supX,(R ) > 2 (  _  2-( + )"  d  «>0  d  d+4  ) X (R )) n  d  0  4  since X , ( R ) is a continuous martingale. Substituting into (2.44) and noting the summation is over less d  than (k + l) + 2^ >" terms , Borel Cantelli gives the result. • d  1  d+3  38  Lemma 2.16 For s £ T  0 [0,fc],p £ N there exists a constant C = C(p,k,m,d)  3n+6  such that for all  " > (5 + log (p))/e a  E"  X,(x:  I(X  „(B(x,cn2- ))>2n  t ?  2 n  )>2  n e  )  < Cn  p ( d + 1 )  2  - n t p  .  t e[fc- ,ifc] 3n  l  PROOF. Fix n and write B for £ ( x , c n 2 ") . C will be a constant depending on p,k,rn,d x  but indepen-  dent of n whose value may change from line to line.  X,(x:  <  l{Xt {B )>2- )>2 )  Y, tj-et*-',*]  E X,(x  :  2n  r  nt  x  I ( * t » ( S * ) > 2~ ) > 2" - )  Y,  3  2n  £  2  t "6[« + 2- »+',«:] a  +E  :>  X,{x:  l(X , (B )>2- )>2 - )\ 2n  t n  n€  (2.45)  2  x  < °e[ifc- ,»-2- »+ ] 3  1  3  1  We will bound the first term on the right hand side of (2.45). It is similar and slightly easier to show the second term has the same bound. E  X,(x  :  I ( * t } » ( £ « ) > 2~ ) > 2" - )  Yl  2n  £  2  * "e[»+2- »+ ,it] 3  3  i  / <  E  E t»  31  y <  C2-  n K t?( *) x  >2  _ 2 n  ) > 2 " (  £  3)p  Jp  \t^-i^\>^~  minj,,i  3n  +  E  ncp  E  i  \J  « > " < . . . l ? » 6 | . + 3 - 3 » + l,*| mink|«3n -t3.|> -3n+i :  B  «?»e|.+3-»'»+i,i.) >=i  n  :  f[KX . (B ) t3 ?  x  >  2- )dX,(x) 2n  L «'=1  2  <  C2-  n t p  2  £ \yf X >n{B ).  Y  2 n p  <? <...i3n [.  E  n  e  +  3-3n + l  l  h  X  t t  x  ..X ,»(B )dX (x) t  x  (2.46)  s  l  The following lemma gives an upper bound for such expectations. Lemma 2.17 If fc < s < -1  E  < t < . . . < t < k then 2  p  X (B{x,a))...X (B{x,a))dX,(x) u  tp  < Ca [(r„ - ! „ _ ! ) . . . (i pd  2  where C depends on k, d, m,p but not a or the ti's  39  tOC*! -  S)} -"' 1  2  Proof of Lemma 2.17 . The required expectation is the standard part of _  ^-  xv-  _  _^  £ E  ( p + 1 )  -  or,,_ ~t,_i  £  E  1  n ^ - ^ ' i ^ ) «=i  E  or,~ti  p  n w-^--i<o)  1  l  L l =  'Eim?;  (2.47)  - N?\ <  • = K(« -«,-i) p  o,_,)=i  cT(a :/3,oi p  where ir(a : 7 1 , . . . , 7 ) = |ar| — inf(ji : a\j ^ 7,|j Vi = 1,..., n) is the number of generations back that a n  branched off from any of the branches 7 1 , . . . , -y„. Recall Y(t) is a *-Brownian motion under 'Po- If i G {n(tp — tp-i), • • • ,pt ] then p  'E[i(\K  E  ~ -\^ ) i - V ° i N  r  a  «,-x)]  (7(a,:/3,Oi,...,or _i)=t p  '  <  p'P (\Y(iii-i)\  < a)  0  if i ^ /itp  (/xm"(-R ) - 1)*P (|Y(« + t )\ < a) d  0  P  if i = /it,  So  ^  E  E  ^ [ K K ' - i V f i ^ ) ^ , ,  «,.,)]  • = M(<F-'I.-I) »(o,:»9,aI,...,aF_i)=i  <  c(m(RV(«+<pr  <  Ca^-tp.j) -^ . 1  d/a  +"V  E  1  (»> )" _1  d/2  2  Substituting into (2.47) and using induction over p gives the result Completion of proof of Lemma 2.16. Using Lemma 2.17 and the bound in equation (2.46) we have  E  X.(x:  I(^  (B.) > 2~ ) > 2 — ) 2n  < f  2  * "£{*+2- + ,Jfc] 3  <  C  n ^ -  d  3n  1  - ^ "  E < "<...t »6I«+3 3  3  " j 1  <  Cn'ty*-'-'*'  <  Cn  ( p + 1 ) d  k  ["  +  i  -t%Wu-»)) -  1 d,a  -an+1  .*)  ik '-  dti ^  dt ... 2  f  k  2~'" . p  40  dt [(t - <p_0 . . . (t - <!)(*! p  p  3  s)] -"' 1  2  Notation. Define for j,n G N, x G *R the events A?(x)  =  { 3 ~ (j  =  [3y ~ (j - 1)2" " s . t . ^ _  A](x)  =  {3s G // ,7 ~ s such that "A7 G 5(2,12/i(2- "))}  75;(x)  =  T  -  l)2-  2 n  s.t. /V _ _ „ G £ ( 7  (  1)2  2  3  9M2- ")),/V 2  £ >  j 2  - n( ) 3  7  _ „ G 5( 9/i(2- ")), yV - „( ) > 2" "} 2  1 ) 2  > 0)  3  £>  2  i2  n  a  T  2  {X - n(5(x,12ft(2- ")))>2- "} 2  i2  2  3  Note that up to a null set A](x)DGMC  2n  B?(z)nGMC  2n  C  A?(x)  (2.48)  C  Bfix)  (2.49)  Lemma 2.16 gives a bound on t2  3 n  E  (2.50) ^  i=Jfc-'2 » 3  whereas to prove Lemma 2.14 we wish to bound *2  3 n  (2.51)  By restricting to GMC  2n  equations (2.48), (2.49) will allow us to replace A^J{x),B^(x)  by AJ{x),  B?(x).  We will show that each time A"(x) occurs there is a good chance that B"(x) occurs and use the following Lemma to convert our bound on (2.50) into a bound on (2.51). Lemma 2.18 On a probability space ( f l , ( • ? ; ) z ' ) let A ,B ,n r  p  je  +  n  n  = 1,... ,N be events satisfying for  some q G [0,1] i. Aj,Bj  efj.  ii. Bj C Aj. in. P ( S | J _ ) > J  Then  ;  1  9^1^-0.  (  TV  TV  \  32*Ai ^n,32 Bi < a\?o < Po(B(n,q) < a) P - a.s. i=i «=i / where B(n,q) has a Binomial distribution under P with parameters n,q. 41 l  0  P R O O F . Define r = 0, r,- = inf(m > T j _ ! : u G A ) for j — 1,..., N. Let 0  = 1^, Yj = Ia,.We claim  m  E\Y l{Tj  > E{l(Tj  < oo)\T _ ]  Tj  Tj  To prove this pick C G  x  < co)|^ _J for j = 1,..., AT.  q  (2.52)  Ti  -Note that  {rj = n } =  and this union is disjoint. If x G {0, l } "  |J  yi„n {(*!,..., *„_!) = *)}  satisfies £ x < — 3~I  -1  {(-^l, • • • ,-^n-i) = z} Q { j-i — ^}  tnen  T  for somefc= 1,..., n — l , s o that c n { ( x , , . . . , * „ _ ! ) = x} = c n {(Xi,...,*„_!) = x} n{r,-^ = fc} G ^ „ _  I;  Then / y i(Tj < oo)dP  =  Ti  J2 I  Jc  n  =  Y  »  d  p  Jcn{T }  1  i=n  =E E/ =E E  /  *w  > ?E E / n =  l  l 6 ( M )  .-.-'Cn((X ,...,X . )= } 1  =  1J  =  1J I(TJ < co)dP /c  *T,K>j  n  1  I  < 00)dP  which proves the claim. We now check by induction on n that for n = 1,..., N , a = 0,..., n  P ^E  Y  'i ^ ' " < a  j ^ Po(B(n,q)  r  < a)  The case n — 1 is immediate from (2.52). Assume equation (2.53) for n = 1,..., fc. k+1  P(52Y <a,T <oo\r ) Ti  k+l  Ti  42  (2.53)  =  !  ( E ^ = ) ( r> y  a  P  = 0,^+i < oo|J- J + I ( E ^ < a -  Y  +l  l)P(Tk i  T  +  < oo\F ) Tk  i=i <  (1 - ? ) I ( E Y i=i  = a) + I ( £ y . < a - 1) i=i  Ti  P(r  Tj  t + 1  < oo^J  k  < (a - ?  ) l £ ^ < a) + ? I ( E ^ < a - 1) j I( Tk < oo)  since {r < oo} C {r i k  < oo}. So taking conditional expectations given To and using the induction  k+  hypothesis k+l P(Y, Tj<a,n+i<oo\T ) i=i Y  0  <  (l-q)Po{B(k,q)<a)  =  P (B(k+ 0  +  qP {B(k,q)<a-l) 0  l,q) < a)  completing the induction. Finally  • ff  N  N  P(J2 A,> >J2 B,^ \ o) i=i i=i l  n  a :F  l  =  P(r < o o , < a \ F i=i  <  F ( r < o o , E ^ < a|^ ) i=i P (B(n,q) < a).  <  n  n  0  )  0  0  Proof of Lemma 2.14. Fix n G N and nearstandard t G {j/n : j G °N},/3 ~ £ such that /vf ^ A . Let j'"(t) = sup{j : j2~ k~ 2 ,..., l  2n  n  < t}. We will apply Lemma 2.18 to the events A"(Nf),  BJ{N ), P  j =  k2 , j ± ] (t) + 1 and the internal filtration 2n  2n  = A -*~ j2  V  : s. < t) j = k-^ ",.. .,k2 , 2  j ± j (t) + 1  2n  2n  under the internal probability 'P. Conditions i. and ii. of the Lemma is immediate and we claim that condition iii. holds , namely that there exists g G *R satisfying q = e x p d  such that for j ^ j (t)  -2  2n  •P(BJ*(/Vf )\QU) > I'PiA^Nl)^)  (2.54)  Before proving (2.54) we complete the proof of Lemma 2.14. For t G T " + fl [0, k] 3  *2  E  X {x: t  2  E  l(^(x))>2 }l(GMC2n) nc  ;=it-'2 " 3  43  + 1  6  *2  <  E X {x:  3  £  t  l(B?(x))  > 2 <' } n  (2.55)  2  *2 "  *2 "  3  +£  3  E  £  l(A?{x))>2*",  ;=Jt-12a»  I(B W)<2 n  Lemma 2.16 gives the upper bound Cnf( h- / d+1  f l t / 2  ;  j = i - i 2  }I(GMC „) 2  »  3  for the first term in (2.55). The value of the second  ncp 2  term in (2.55) is less than the standard part of *2  Jfc2  3  3  E  ^ f ) ) ^ '  -  1  E  I(^(A f))<2 r  ,GMC  n f / 2  2 n >  7Vf  But by Lemma 2.18 /  *2  *2  3  E  I(^ (7Vf))>2" r ,  r  )  Wj (i)+i 3n  <  3  E  1  l{B»(N?))<2 *l\GMC  \Q .  n  n  2n  k  jVj' "(i)+i 3  •P (y(2 - ,o) < 2 / ) n £  1  n e  2  0  So (using (2.48)(2.49)) the second term of (2.55) is bounded by P~ <  J2' ( ? * P  l  N  A  )* o(y(2 p  m(R )(l - e " ) " " d  2  n t  - , ) < 2 / ) 1  n E  2  9  for large n  2  So X {x  E  :  t  c h a r  S  e s  > 2 <} > 2^ >  B(x,2""))  n  d+h  for some t £ T  n [0,*]}  +  P(GMC< )  3n+6  /?"C[t-»,t]  Jfc2  3  <  E 2( teT "+ n[o,*] s  <  ) " £ ; X {x:  d+  n  C  I(i"W) >  E  t  s  2 2< V 3n+6  rf+5  [ (<*+i)P2- P/2 nt  n  +  (  1  _  e  -2  )  2  "/'l  +  2" }I(GMC ) £  2 n  P  r  G  M  C  C  n  2n  )  for large n.Taking p — 2 ( d + 9)/e , Borel Cantelli gives the desired result. I t remains to prove (2.54).Fix n,j,t,0~  t such that t & [(j - l)2~ ,  j2~ ].  2n  2n  For j ~ (j -  define .  A?(y,x)  =  2*7(7,*)  =  {N^_ ^eB(x,9h(2- )),N - (y)>0} 2n  1)2  {^_  1 ) 2  j2  - »GB(£,9/ (2l  3  44  2 n  3n  )) ^ - n( )>2!  2  3  7  2 n  }  l)n2~  2n  Then  W  U  =  A  ?b>ti  A  7~(;-«)2- " 3  =  s;( ,£)  (J  7  Fix 5 = (71,... ,7r) where - ~ (j — l ) 2 " , r > 1. Consider the set -2  7l  r  Asia)  = n^'^.aO n t= l  fl  W?.*))  (- )  e  2  ,«(/-l)3-'* 7^5  As S ranges over nonempty subsets of {7 : 7 ~ (j — 1)2 "} the sets As(x) -2  form a disjoint partition of  A^x). 'P (^B"(Nf)\Q"_ ,  As(N )j p  l  >  'P (/V; - n(7i) >  >  'P ( 7 V - . ( 7 i ) > 2 - " | / V . - 3 . ( ) > 0) l(A (N[))I(/%_ -3„  2  2- \g^ A (N[)) 2n  ly  2  s  2  ja  a  J  2  7 l  s  1)2  ^  7  l  + ^ ( 7 ^ 2 - 3 . ( / ? | - - i ) 2 - » - ) > 2- |/V -2— ( % - i ) - * 0 > o./vf # A) 2n  0  J  2  x I(^ (7Vf))I(/?| _ 0  s  1 ) 2  -3„=7 ) 1  Lemma 2.2 b. c. gives •P(7V -3»(7i) > 2- "|7V - »(7i) > 0) « e" 2  2  i2  j2  3  •P(7v - -3»(/?|(j_i)2-a» ) > 2 - " | 7 V - . - 3 „ ( % _ 2 - 3 „ ) > 0,/vf # A) 2  J  = So with £ «  e  2  J  •P(7V -3.(/3| _ i2  (j  2  1 )  - 3 . ) > 2- "|7V/ _ „ ± A) « 2  1)2  2  a  2e-  2  - 2  'P{B?(Nl)\g?_ As{Nl))  >  lt  Since this is true for all sets As(Nf)  56  of the form (2.56) we have  and the proof is complete.•  45  ql(A (N[)). s  )  Chapter 3  The martingale problem characterisation  3.1  T h e measure of a half space  The martingale problem satisfied by a superprocess gives a semimartingale decomposition for X (f) t  where / is in the domain of the generator A of the underlying spatial motion, namely Mf)  = "»(/)+  (M(f))  =  t  f X,(Af)ds Jo  + M (f)  (3.57)  t  X,{f )ds  f Jo  2  We look for a similar decomposition of X (f) for general bounded measurable / . Perkins has shown t  (private communication) that if the semigroup of the underlying process satisfies a continuity condition, for instance 3C,/3i,/3 > 0 such that for all 0 < 6 < u, f G bS 2  \\T f v+i  - T f\\ < C\\f\\  (i/-* * V 1)  then with probability one the processes t —• X (f)  (3.58)  5  y  for bounded measurable / are all continuous on  t  (0,oo). The proof shows that if /„ G D(A) are uniformly bounded and converge pointwise to / then almost surely the paths X (f ) t  " i ( / n ) —*  (f)  m  converge to X (f)  n  t  uniformly on compact subintervals of (0,co). Also  by dominated convergence and £(sup(M (/„) - M,(/ )) ) < 4E( [ X,((f t<T Jo 2  t  T  m  - f ) )ds) 2  n  m  - 0  (3.59)  as rn,n —• oo again by dominated convergence. So along a subsequence n' the martingales  M (f ') t  n  converge almost surely and uniformly on compacts to a continuous martingale M (f) . So under the t  hypothesis (3.58) ,with probability one the processes f* X,(Af )ds n  have a subsequence which converges  uniformly on compacts in (0,co) to a continuous limit. We examine the case where X is a super symmetric a— stable process (so that hypothesis (3.58) is t  satisfied with 0\ —  = 1) and / is the indicator of a halfspace . Theorem 3.2 shows that X (f) fails to t  46  be a semimartingale i f 1 < a < 2. We will need the existence of a density for X  t  in dimension 1 when  1 < a < 2. We state the necessary results as a Theorem. T h e o r e m 3.1 Let m £ Mp(R)  have a continuous density u(x).Let a £ (1,2] and X  be a one dimen-  t  sional super symmetric a—stable process starting at m defined on a probabilty space (Q,F,P).  Then X  which is continuous on [0,co) x R. There is a space-time white noise W  has a density X(t,x)  tiX  r  t  defined  P) such that for all f £ C ° ° ( R ) of compact support  on an enlargement of(Q,J , X (f)  t  = m(f)+  f X,(Af)ds+ Jo  I f y/X(s,x)f(x)dW Jo JR  SiX  Vt > 0  (3.60)  For fixed x £ R,t > 0 X(t,x)=T u(x)+  (3.61)  I j -,(y,x)^X(s,y)dW, Jo JR  t  Pt  iy  where Pt{x,y) is the a—stable transition density. If u(x) is bounded and uniformly Holder continuous then there exist j > 0 and C depending only on m and a such that E((X(t,x)  - X(s,x)) ) 2  < C(t - y S  (3.62)  for allt,s>0  The existence of a jointly continuous density satisfying (3.60) is proved in Konno and Shiga [14] Theorem 1.4. Equation (3.61) is established during the proof in Konno and Shiga (although they consider more general initial measures and thus work on [<o,oo) for to > 0 , it is easy to extend (3.61) to [0, oo) for initial measures that have a continuous density.) The proof uses moment estimates of the type in (3.62) but since we can't point to exactly what we need we give a proof. P R O O F O F (3.62) .  From (3.61)  X(t,x)-X(s,x)  =  (T -T,)u(x) t  + J  Q  J(Pt-r(x, y) - P.-r{x, y)WX{r,  Find C,0 £ (0,1] such that \u(x) - u(y)\ < C\x - yf \\(T -T,)u\\ t  for all x,y £ R. Then  <  \\(T . -I)u\\  <  CE (\Y - f)  <  C{t-sf  47  t s  0  t  la  s  y)dW ,  r y  The stable density satisfies p i ( x ) < C ( | 2 : | ~ £(( j f  (1+a  ) A 1) and the scaling equation p (x) =  _ (x,yWX(r,y)dW , ) ) 2  Pt  r  t~ p (t- ^ x). 1/a  t  t y  =  J* mT (p _ (x  <  \\u\\ /  r  /  J0 /0  C  p ( )dxdr 2  x  Joo  pt — s  <  a  - -))dr  2  r  1  1  r- l dr l a  Jo  =  C(t-s)^ -^' a  a  Similarly E(( f J,-(t-,y/3 t  P t  < C(t - s ) s - (  <  < C{t - s ) ( ° - ) / . 1  2  -('-• )  Finally \\p -p,\\  ( _ - p,_ )^/x(^)dW , ) ) r  r  r y  2 a  I/3  0 + 1  ) / ° for 0 < s < t so  E(( f Jo  (p _ (x, y) - p,_ (x,  Cm(l) /'  (t -  t  <  r  y))y/xJr~^dW ) ) 2  r  r>y  s) r- ( V dr 2  2  a+1  a  C(t-s)WW"\  o Theorem 3.2 Let m G Mp and X  t  be a super symmetric  stable process of index a.  Let H be the  indicator of a halfspace .Define (j>(a) = 2a/(a + 1) if a > 1 Then for any T > 0 we have the following X (H) t  where M  t  is a continuous L  2  decomposition.  = m(H) + V +M t  forO<t  t  <T  = / * X,(H)ds  martingale satisfying (M)  t  If 0 < a < 1 and m has a bounded density then X (H) t  and V is continuous on (0,T]. t  is a semimariingale  and V has integrable t  variation on [0,T]. If 1 < a < 2 and m has a bounded density then V has integrable <j>(a) variation on [0,T\. If in t  addition the density is uniformly Holder continuous and satisfies u(0, x) > 0 for some x on the boundary of the halfspace then with probability one V< has strictly positive <j>(a) variation on [0,T] and hence X fails to be a semimariingale.  48  t  The proof uses the following well known Green's function representation for X {4>) ,<f> G bS. t  X {4) = m{T <j>) + f  I T -,4>(x)dZ,,  t  t  t  (3.63)  t  JO JE  where T is the semigroup of the underlying motion and Z t  is an orthogonal martingale measure  t>x  satisfying (f I f{s,x)dZ, ) Jo JE  = f X,{f {s ))ds JO  (3.64)  2  tX  r  for any measurable f(s,x) such that E{JQ X,{f {s,-))ds) 2  < oo, Vi. For the theory of stochastic inte-  gration with respect to martingale measures see Walsh [24].Briefly, equation (3.63) may be derived from the martingale problem as follows. Rewrite (M) as X (f)  = m(f)+  t  f X,{Af)ds Jo  + f f f(x)dZ,, . JO JE x  Considering functions of the form ft(x) = ^5i(x)/i,(i) and then passing to the limit we have Xt(ft) =  «»(/o) +  for sufficiently smooth f,{x).  /'  Jo  X.(Af.  (3.65)  + df,/ds)ds + f f f.(x)dZ.,, Jo JE  Fixing t > 0 and checking that f,(x) — T -,4>{x) is smooth enough to t  apply (3.65) we immediately obtain (3.63) for nice <j>. Extension to all <f> G b£ is straightforward. PROOF OF PROPOSITION  3.2. It is enough to consider the case d = 1 and H = I(x > 0). We start  with the Green's function representation. X {H)  = m(T H)  t  If we set M = f* f HdZ, t  t  + f f Jo JR  T .,HdZ, t  iX  then M is a continuous L martingale satisfying (M) = / * X,(H)ds. 2  iX  t  t  0  The  decomposition follows by setting V = m((T - I)H) + f j {T -, Jo JR t  t  t  I)HdZ, . iX  Now m((T, - I)H) = m{{T - I){H)l{x  > 0)) - m((7 - T ){H)l{x  t  t  < 0))  is the difference of two decreasing processes and so of bounded variation. It remains to check the variation of W := t  f I (T ..-I)HdZ . Jo JR t  (3.66)  tit  An upper bound for the expected value of the size of an increment of W can be obtained using the t  isometry for Z,  x  (equation 3.64 ) . We delay the calculations and state the result as a Lemma. 49  L e m m a 3.3 If m has a bounded density then there is a constant C depending only on T,a,m such that / o r 0 <s <t <T E((W  2 ^^ I ( < - s ) - W,) ) < C  t  (t-s)  ifa> 1  ( a + 1 ) / a  ifa<\  2  Since we are interested in a continuous version of Wt i t is enough to check the variation over one sequence of decreasing nested partitions .Let A = T/n and Sj = jA . I f 1 < a then  E^plW^-W.^*^  <  YmW^-W,^) ))^' 2  2  < CT. So W and hence V has integrable <f>(a) variation on [0,T]. Similarly i f a < 1 then V has integrable t  t  t  variation on [0, T\. We now assume that u(0, x) is bounded, uniformly Holder continuous and satisfies u(0,0) > 0. I f 1 < a < 2 then X has a jointly continuous density u(t,x) and t  / / f(s,x)dZ, = Jo JR  [ f Jo JR  iX  f(s,xWu(s,x)dW ,  s x  where W, is a space-time white noise (see Theorem 3.1). tX  We split an increment of W into three parts as follows. Fix n and let tj = j/n . t  m  i + l  -W  t i  .= f ' [ Jo JR  (T -.-T ..)HdZ. ti+l  +  ti  lT  £ J (T . -I){H)y/^^dW +l  R  ti+l t  tttl  + j*'** J ( <>+>T  R  - I)(H)(V^)-  = • Cj + Cj + We wish to show that W has strictly positive <j>(a) variation . We will first show that  \^ ^> is small and a  t  does not contribute to the variation. Then noting that Q is T measurable , we will show that conditional ti  on Tt} , €j has a mean zero Normal distribution with variance more than Cn- X (B(0,n- / )). 1  1 a  tj  since  X has a density u(t,x) bounded away from zero at t = x = 0, this variance will be of the order of t  n  -(o+i)/or  a  n  d t  h  e  i  n c r  e e n t |£,- + C j | *  E(\ V\ ) 2  (o)  m  < E ( £  +  1  will be of the order of n  J (Tt -. R  i+1  _ 1  .  -7)(//)(\AM2  50  y/u(t x)) dsdx) 2  jt  (T, _. - I) (H)[E(u(s,x)  -  2  R  <  Cn-i  i+1  •A,  JR  —  (Tt -, j+l  uitj.zWdadz  I) (H)dsdx 2  where 7 > 0 from Lemma 3.1 which uses the Holder continuity of u(0, x). Using the bound (T — I)H(x) < r  Cr\x\~  a  A 1 ( see equation 3.71) we have / •/i,  (T -,-I) (H)dsdx JR 2  tj+1  <  Cn"  <  Cn~(  1  / WO a + 1  dx+  / n- |x|" 2adx Jn-i/o 2  ) . / o  So [nT]-l  75  E  >=0  \*\"  <  a )  E (^(vj))*  (a)/2  j =0 n[T]-l  C E  <  (n-( >/"n-Y(°:>/  2  0+1  (3.67) = Cn~ /(° >. Conditional on T% , €j has a Normal mean zero distribution with variance 7 a  + 1  i  (T, _, - 7) 77ds 2  i+1  Let Y be a symmetric a-stable process under PQ . T  = P (Yi > Ixl/r /") 1  \(T -I)H(x)\  0  r  >  W  i >2  1/a  )I(|x|<(2r) / ) 1  a  So f (T ^,-I) Hds 3+l  >  2  tj+  ^  (P (Y >2 > )) l(\x\<(n)- '")ds 1  0  C n- I(|a:| < n "  =  1  1/a  2  a  2  1  1  )  where C = (P (Yi > 2 / ° ) / 2 . 1  2  2  0  Let N have a Normal mean zero variance one distribution under Po-  Q(|f;l*  (o)  > Kn- \?tj) l  Ci K l* W-W°)- )/x .(B{Q,n- l )))  >  Po(N  >  (1/5)1 \x (B(0, n" /")) >  2  >  l  i  0  1  l  t  1  ti  51  C - K l^n- l l  2  2  l a  a  using P (N  2  0  > 1) > 1/5. Since Cj is T Q(Vj  measurable  %i  + C i l * ( o ) > Kn- ^)  > (1/10)1 [XtjWOtn- 1'"))  1  >  C  _1 3  K *( 5n- ] . 3/  0  1/o  The density u(i,x) is jointly continuous and u(0,0) > 0 so given e > Owe may find no > 1,K > 0,i > 0 0  so that for all n > no Q(X (B(0,n- ' )  < C^Ka'^n- !"  l a  for some 0 < t < t ) < e.  1  t  0  Then for n > no [nT]-l  Q( E  k;+0l* >'co<o/20) (o)  [nT]-l  >  Q( E  I(ki+OI*  ( t t )  >W«)>"W20)  j=o /[nT]-l  >  Q I  E  1(^(5(0,n" /")) > C^J^n- ' ) 1  1 0  > nt  0  /[nT]-l  [nT]-l  E j=o >  I(k>+0l*  >«o/n)<n<o/20  ( a )  ( l - e ) - P ( S ( n t o , l / 1 0 ) < nto/20) 0  where B has a Binomial distribution under Po, using Lemma 2.18. So for large n Q( E  li c  +  <i l *  ^ «o<o/20) > 1 - 2e.  ( 0 )  3=0  But from (3.67) for large n n[T]-l  <?( E  h\ > HQ)  «o«o/40) < £ .  j=0  Now Minkowski's inequality and Fatou's Lemma give n|T]-i  <3( E i=o  l^'i+i ~  Since e was arbitrary it follows that the  > «o<o/80 infinitely often ) > 1 - 3e.  <j>(a) variation of W over [0,T] is strictly positive. • t  52  0  PROOF O F L E M M A 3.3  . From (3.66)  E[(W -W,) ] 2  t  =  £?  =  J  (T,_ - l)HdZ ,  mT ((Ttr  +  r x  P  - I)H) )dr  r  + J* mT (((T .  2  r  JVt-r " T,- )H r  dZ )  2  r<x  - T,- )H) )dr.  (3.68)  2  t r  r  For fixed x > 0 (T -T )H(x) r+6  r  =  P (Y  =  Po(y €[ /(r + 6) /° x/r / ])  <  Kx/r /") - fx/(r + «) )|pi(x/(r + /5) / ).  0  > - x ) - P (Y  r+6  0  >  r  1  1  -x) 1  Z  t t  )  1  1/a  1  0  C will be a constant depending only on T, a, m whose value may change from line to line.Using the bound pi(x) < C ( | x | - (  1+a  ) A 1) we have for r > 6 \(T -T )H(x)\ r+6  <  r  C(6\x\- A6\x\r-W ) a  <  C6(\x\-°  a  Ar- )  (3.69)  1  for r < 6 \(T -T )H(x)\ r+s  r  <  P (yi€[|*|/* ,oo))  <  C6\x\~  1/O  0  Al  a  (3.70)  and for r > 0  \(T -i)H(x)\ r  <  p (netkl/»- ,oo))  <  Cr\x\—Al  1/o  0  (3.71)  Find a constant K so that the densities of the measures mT  r  are bounded by K for all r > 0. From  (3.69), for 0 < r < s - (r - s) T,. ) )H  mT ((T,. r  <  ( [K(s  2(t - s)  2  \ <  C(t-  2  r  r  2  s)  .  r(-ryl" - r)~  2  fl  /  dx + K  J0  \ \x\~ dx + mT (\x\ > 1) 2a  r  7( -r)i/» a  l + is-r)^'^-  if a  2  I l+log+^s-r)- )  #1/2  if a = 1/2  1  53  J  From ( 3.70), for s - (t - s) < r < s mT ((T _ r  t  T.- ) )H 2  r  T  At—) " 1  <  2CK  dx + 2CK(t-s)  2a  J(t->y/  (< _ ) ( 2 A ( l / a ) ) s  <  \x\- dx  2  Jo  i f  a  +  2C(t-s) mT (\x\>l) 2  r  a  j.  1  /  2  C {i-s) (l  if a = 1 / 2  + \og ((t-s)- )  2  +  1  So mT ((T,_ -  Jo <  r  C(t - s)  / Jo  '  dr + C(t - s)  \ (t-s) \  f,-(t /•'-(<-•) [  1+ ( _ )(l/a)-2 5  r  2  '  J(t~,)  ' (t- ) (l 2  j.-(t-,)  <  2  r  fit-,) («-')  2  •r  +C  T,- ) )Hdr  r  s  +  log ((t-s)-i)) +  ifa<l  2  (t - ) ( « + ) / « 1  s  if a > 1  Similar arguments give an upper bound of no larger order for the first term in (3.68). • Remarks. i. For any m £ Mp(R )  similar arguments show that if 0 < S < T, 0 < a < 1 then V has integrable  d  t  variation on [S, T}. ii. I f 1 < a < 2 then the instantaneous propagation of the support (see (3.78) ) implies that V will t  have strictly positive <f>(a) variation on [0,T] for any T > 0. I f a = 2 and m ^ 0 then there is positive probability that for some s > 0 the measure X, will have a uniformly Holder continuous bounded density that is strictly positive at some point on the boundary of the halfspace. Thus for any XQ ^ 0 the process Xt fails to be a semimartingale. iii. Sugitani [23] shows that for super Brownian motion in dimension one the local time process Y(t, x) =  X(s, x)ds is differentiable in x and that i f m is atomless the derivative D Y(t, x  x) is jointly  continuous in t,x almost surely. We can easily identify the drift term Vt in the decomposition of X (H) t  as {\/2)D Y(t,x) x  .  Take m 6 A f f ( R ) atomless and of compact support. Define / ( x ) = ((x — a) V 0 ) . We may find 2  a  / „ G D(A) so that / „ | f {x) a  and Af„ —• I ( x > a) bounded pointwise. We have enough domination  54  (e.g. E(sup  t<T  < oo ) to take limits in the martingale problem and obtain  X (f%)) t  X (f ) t  a  =  m ( / ) + / X,(l(x<a))ds Jo  =  rn(f )+  0  a  f Ja  Y(t,x)dx  +  M (f ) t  a  (3.72)  + M (f ) t  a  We wish to differentiate (3.72) twice with respect to a and again we have enough domination. Thus for a fixed t 2X ((x t  - a) V 0) = 2m((x - a) V 0) + Y(t, a) + M (2(x - a) V 0)  (3.73)  t  Now continuity of both sides in t gives (3.73) for all t. Repeating the argument and using the continuity of D Y(t, x  x) gives X (l(x t  3.2  < a)) = m ( I ( x < a)) + (l/2)D Y(t,a) x  + M (l(x t  < a)).  T h e death point  We use the characterisation of a Superprocess Xt as a solution to a martingale problem (equation 3.57) to study the sample path behaviour near the time of death. Set £ = i n f > 0 : -Xt(l) — 0} where we write 1 for the constant function with value one. I f m 6 Mp then f < oo almost surely. Define C= t  ( Jo  l/X,{\)ds.  In Konno-Shiga [14] Theorem 2.1 it is shown that with probability one C is a homeomorphism between t  [0,£) and [0,oo). Let D : [0,oo) —• [0,£) be the continuous strictly increasing inverse to C . Shiga [22] (  t  uses D as a time change together with a renormalisation to convert a class of measure valued processes t  into a class of probability valued processes. The Superprocesses studied here do not seem to fall directly into his context. However the time change will still be useful. By stretching out the interval [0,£) into [0, oo) we can use the behaviour at infinity of the time changed process to give infomation about X before death. For t G [0,oo) define Y  =  X  Y  =  Y /Y,(l)  t  t  Dt  t  Gt = ^T>,  55  t  Note that {Y : t > 0} is a probability valued process.We derive the martingale problem for Y . For t  t  / € D(A) rD,  Y {f) t  =  rn(f)+  j ' X,(Af)ds Jo  =  m(f)+  ff.iA^Y.i^ds Jo 10  +  M (f) Dl  + Ntif)  where, since D is a continuous time change , Ntif) is a continuous Q local martingale satisfying t  t  (N(f))t = =  ( 'x,(f )ds 2  Jo  f Jo  Y,(f)Y,(l)d.  In particular Y (l) t  (N(l))  =  t  (N(f),N(l))  =  =  t  m(l) + A> (l) (  f{Y,{l)) ds 2  Jo  fY,(f)Y,(l)ds  Jo  Applying Ito's formula and noting that Y«(l) > 0 for all t > 0 we have Yt{f) = mif) + fY.iAf)ds Jo where N if) t  + N if) t  (3.74)  is a continuous Gt local martingale satisfying (N(f)) = t  fY.if )-iY,(f)) ds 2  Jo  2  The martingale problem for Y is frustratingly close to that for the probability valued diffusion known t  as the Fleming-Viot process ( where the drift term in (3.74) would be replaced by fY,iAf)ds  ). In  Konno-Shiga [14] this 'connection' between the martingale problems is used to derive the existence of a continuous density for the Fleming-Viot process in dimension 1 from that for super Brownian motion . The following result shows that as t —* £ , what mass that remains is concentrated near a single point. Theorem 3.4 For m € M  F  there exists an E valued random variable F such that with probability one  Xt/Xtil)->6  ast — £  F  56  (3.75)  where the convergence is weak convergence of measures. The law of F given the history of the total mass process Ti = a(X (l) t  : t > 0) satisfies E (f(F)\H)=l/m(l)  (3.76)  f T fdm. JE  m  6  Remarks. i. Equation (3.76) implies that the law of F can be constructed as follows. Position a particle in E at random according to the measure m ( ) / m ( l ) . Let the particle move according to the underlying spatial motion but independently to the process. Stop the particle at time £. The final position of the particle will have law F. ii. The law of £ is given by P(£ <t) = e x p ( - 2 m ( l ) / t ) PROOF. First assume E is compact. Take / £ D(A). fY.{Af)ds Jo  <  \\Af\\ Jo  fr {\)ds  =  \\Af\\ f X ,(\)d8 Jo \\Af\\D < \\AfU.  t  D  =  t  So Nt(f)  > -m(f) - \\Af\\Z.  For any continuous local martingale (M ;t > 0) , with probability one either M converges to a t  t  finite limit or l i m s u p M , = — l i m i n f M = oo (see Rogers Williams [20] Corollary IV.34.13 ). So N (f) (  t  converges as t —+ oo to a finite limit. Also Y (Af)dr  \f. So f* Y,(Af)ds  < \\Af\\(D  r  t  - D,) — 0 as s,t — oo.  converges as t —+ oo.Thus Y (f) converges a.s. to a finite limit which we call Voo(/)t  Since C(E) is separable and D(A) is dense in C(E) we may pick {<£„}„ C D(A) dense in C(E). Off a null set N we have Y (<t> ) —* Voo^n),Vn. Fix w £ N. Then by approximation Y (f) converges to a t  n  t  finite limit Yoo(/) for all / £ C(E). Also / —+ Yco(/) is a positive linear functional with Voo(l) = 1 and thus arises from a probability which we call Yoo. For / £ D(A) A?(/)-  fY.{f )-{Y {f)?ds 2  t  Jo  is a continuous local martingale. Since N (f),Y,(f ),Y,(f) 2  t  all converge to finite limits this local martin-  gale must converge requiring Yoo(/ ) = (Yoo(/)) a.s. So the probability 2  2  57  is concentrated on a level  set o f f . But E is a metric space so that C(E) and hence {4> }n separate points and this forces Yco = 8p n  a.s. for some F. We have been unable t o deduce the law of F directly from the martingale problem but it comes immediately from the particle picture. Take the nonstandard model with sijKpfffl,,) = m. Let  Q be the  — fi~ ^ r  internal algebra generated by the total mass process  satisfying  6  Xi  {N (l) : t > 0} and t  a(G) the standard <7-algebra generated by Q. Note that NA['E) is Q measurable for all t so that 7i is a sub  cr -  algebra of cr(G). Let £„ = inf(t  > 0 : N£(l)  < 1/n). For any / € C(E), n > m ( l ) -  1  •E(N?J-f)/N? (l)\G) n  Now £ = 'P(N^  n  =  n'E(n->  £  =  n/x- £  'P(Nl  1  'f(N&\G) ?  A\G)T f(7\o) u  ^ A\G) is independent of j, so -E{N* Cf)/N{ (l)\G) m  =  n  n ^ ^ ^ E ^ / f x , ) ) i  =  n/m(l)/  ^/(x^m^x)^-  =  l/m(l) /  1  E  f~(  E  ^(AT  7 f  n  ^A|a))  n  7>„/(fm„.  So f5(X.  (f)/X  (l)\<r(£)) = l / m ( l ) / T  /dm  0  using Albevario et al. [1] Proposition 3.2.12.Now °£„ f £ as n —• oo so that E{f{F)\H)  = l/m(l) / T /dm.  (3.77)  £  When E is only locally compact we can extend the semigroup T t o E U {oo} the one point comt  pactification of E by taking T (co, {oo}) = l , T ( x , {co}) = 0 for all x G E,t > 0. Working with this t  t  new Feller process on E U {oo} the above argument gives the existence of a death point F taking values in E U {co} and satisfying (3.75) and (3.76). Since P(£ < co) = 1 the characterisation of the law of F (3.77) ensures P(F € E) = 1. • Example. Let Xt be a super Poisson process. Define 7 i = inf(< > 0 : X ({0,..., t  58  k — 1}) = 0). In  Perkins [17] Corollory 3.1 it is shown that T t £ and k  S, = {k,k+  1,...} for Lebesgue a.a.t in [T ,T ),k k  G Z + , P - a.s. m  k+1  Theorem 3.4 shows that only finitely many of the T 's are distinct. Indeed k  0 = To < Ti < . . . < 7> < 7>+i = T  F+2  = . . . = /; P  m  - a.s.  There is positive probability for any combination of equalities amoung To,T\,...  3.3  ,T . F  T h e support near extinction  The closed support of a superprocess X at a fixed time has been studied in Perkins [17] and Evans and t  Perkins [8] . If the spatial motion is a Levy process on R with Levy measure p. then in Evans and d  Perkins [8] Theorem 5.1 it is shown that for all t > 0 |J S(n* t=i  k  * X ) C S(X ) t  t  P  m  - a.s.  where //** is the k'th fold convolution of (i with itself.For a super symmetric stable process this implies  S(X ) t  = 0 or R  P  d  m  - a.s. Vr > 0.  (3.78)  Similar results for certain Feller processes are obtained . Consider a Markov jump process with bounded generator A so that  Af(x) = p f M * , dy)(f(y) - f(x)) JE with p > 0 and \x a probability kernel such that x —* f it(x,dy)f(y)  (3.79) G Co(E) for all / G Co(E).  Then  for t > 0  [J^S^J  ...jXt(dx ) i(x dx )...Li(x ,-)j 1  t  u  2  k  C S(X ) P t  m  - a.s.  (3.80)  We shall show that (3.78),(3.80) are far from being sample path properties and that near the time of death there will be exceptional times at which the support is concentrated arbitrarily close to the death point. We start by examining the case where the spatial motion is a Markov jump process as described above.  Note that Af is well defined by (3.79) for any bounded measurable / . A monotone class  argument shows that for any bounded measurable / the process X (f) t  usual semimartingale decomposition. 59  is continuous and satisfies the  Theorem 3.5 For all £ > 0 , with probability one there exist distinct t  j £ such that  n  S(X jCB(F,e). t  PROOF. Take ACE  and let / = l(x G A). We shall use the time changed process Y (f) t  as in section  3.2. Let B be an independent Brownian motion defined if necessary on an extension of the original t  probability space. Define  B= t  / ( y , ( / ) ( i - y . ( / ) ) ) - i ( n ( / ) ^ o ) d 7 v , ( / ) + / ' i ( y , ( / ) = o)dS. t  1 / 2  Jo  Jo  so that Bt is a Brownian motion and Yt(f) = m(f) + f Y,(Af)ds Jo  + /'(y.(/)(l Jo  Y,{f))yl dB,. 2  If Y t ( / ) = 0 or 1 then Y is supported on A or A respectively. So we look for times at which Y ( / ) ( 1 — c  t  t  y ( / ) ) becomes zero. Fix iV G N and define Z (f)  — y v + « ( / ) ( l - Yff (f)).  t  Zt(f)  Z (f)+  =  f(l-2Y ,(f))(Y (f)(l-Y ,(f))y' dB 2  0  N+  Jo  + ( (l-2Y .(f))Y ,(Af)dsJo N+  =  By Ito's formula we have  +t  N+s  N+  f Jo  N+  Zo(f) + A/3. - Z,{f))ds + [\z.(f)(l Jo Jo  s  Y ,(f)(l-Y (f))ds N+  -  N+t  *Z {f))) ! dB. l  2  t  where P.  =  B  =  t  (l-2Y ,(f))Y,(Af) N+  /  sgn(l -  JN  2Y,(f))dB,  so that B is another Brownian motion. Since the function t  — 4a:)) / satisfies the Yamada-Watanabe 1  2  criterion (see Rogers and Williams [20] Theorem V.40.1 ) we have a unique solution on the same probability space to the stochastic differential equation X = Z (f) t  - X )ds  + f((l/8) Jo  0  + f \X,{\ Jo  s  AX,)\ l dB . l 2  s  Lemma 3.6 shows that X takes values in [0,1/4] and zero is a recurrent point . Define t  T  N  = inf(< > 0 : Y j v t ( l ) > ( l / 8 p / | | ) ) +  which is a GN+t stopping time. For s <TN | A | = | ( l - 2 y  w  +  i  ( / ) ) y  60  A  f  +  . ( y l / ) | < l / 8 .  So by a comparison Theorem for one dimensional diffusions ( see Rogers and Williams [20] Theorem V.43.1 ) we have Zt{f)  for t<T  < X  t  P  m  N  - a.s.  (We have applied the comparison Theorem up to a stopping time .The changes needed in the proof of Theorem V.43.1 are easy. ) Since zero is recurrent for X Since Y,(l)  must hit zero infinitely often as t —*• oo.  , on the set {Tyv = oo}, Z (f)  t  t  —• 0 as s —• oo , P(T/v = oo) t 1 as N —• oo. So with probability one there exist t  n  Given e > 0 let (A ) m  m  mo  = 0 or 1.  tn  (3.81)  be a countable collection of open balls of radius e/2 that cover E . Fix w so that  m  (3.81) holds simultaneously for all f then Yt(A )  T oo so that Y (f)  = I ( z 6 A ). m  Find mo(w) so that F(u) €'A ( y mo  —+ 1 . So there exist i „ f oo so that Y i ( I ( x 0 A )) n  5(X  0 ( n  u  Since Y —• 8f t  = 0 and  mo  ) = S ( Y J C B ( F , e) for all n. t  • Lemma 3.6 Let Bt be a Brownian  motion defined on a probability space (Q.,T,P).  Let Xf  be the  unique solution to the stochastic differential equation dX X Then P(3n such that X  t  t  =  ((l/8)-X )dt  0  =  * € [0,1/4]  + \X,(l-4Xt)\ dB  (3.82)  1,2  i  t  > 0 for all t > n) = 0 .  PROOF. Equation (3.82) is pathwise exact so we may find a pathwise unique solution on any space and any two solutions have the same law. Let P  z  be the law of X  on path space. Then the laws  x  (P ) x  x  form a strong Markov family. We write x for the coordinate function on path space. t  Let Y be the unique solution on ( Q , ^ , P) to the S.D.E. 7  t  dXt  =  X  =  0  ((l/S)-X )/\0dt- \Xt(l-4X )\ dB 1/2  t  r  i  t  0  By uniqueness Y = 0, Wt > 0. So by a comparison Theorem (see Rogers and Williams [20] Theorem t  V.43.1 ) X  x  > 0, V< P  m  — a.s. We may treat the boundary x — 1/4 similarly and conclude P*(z, €[0,1/4], V r > 0 ) = l 61  It is enough to show that there exist <o ° that s  P  1/4  ( 3 0 < t < t , x = 0) = c > 0 0  for then by the strong Markov property P (30  < t < to, x = 0) > c for all x G [0,1/4] and setting  x  A  t  = (3t G (n<o,(n+ 1)< ], x = 0) we have P (A \A ...  , ^ „ _ i ) > c and P (A  x  n  0  (3.83)  t  n  t  x  u  n  i.o.) = 1 which  implies the result. On the interval [6, (1/4) — 6] where 0 < 6 < 1/8 will be chosen later , we can construct a weak solution to (3.82) using a scale and time change of Brownian motion in a standard manner (see Rogers and Williams V.44 .) We shall then examine the behaviour near the endpoints seperately. Set s(x) = f  (u(l - 4u))- du for x G [6, (1/4) - 6] 1/4  so that s(x) is a strictly increasing C function taking [5,(1/4) — 6] —• [a,b]. Set h(x) — s'(x)x(\ — Ax) 2  and g(x) = /i(s (x)) . Then g is a continuous function on [a, 6] bounded away from zero by a constant -1  K. Let B be a Brownian motion on (fl, T, P) started at s(x) G [a, b] . Set T = inf(t > 0 : B = y) and t  y  A = f g(B )- du Jo  for t < f  2  t  u  a  A fj.  Let jt be the continuous strictly increasing inverse to At- Then Yt = B  lt  g(Y )dB t  t  solves the S.D.E. dY = t  for some Brownian motion B and Z = s ( Y t ) is a weak solution to (3.82) up till the time - 1  t  t  t  inf(t > 0 : Z G {&, (1/4) - 6 » . Now for t > 0 t  0  P(f  a  < f» < t ) > 0 0  so if T — inf (/ > 0 : x = y) then y  t  P*(Ti < T  ) - « < # <o) > 0 Vx G (5, (1/4) - 6). 2  ( 1 / 4  (3.84)  For the behaviour near x = 1/4 we need only that we can find to,6 > 0 so that ^  1 / 4  (x  < 0  < (1/4) - 26) > 0  (3.85)  and this follows since X = 1/4 is not a solution to (3.82). t  For the behaviour near x = 0 we use another comparison . Let Y = (1 + cos(2B<))/8 where B is a t  t  Brownian motion started at xo and 6 = (1 + cos(2xo))/8. Ito's formula gives  /V«(1-4Y.))  Y = 6 + 2 f\(l/S)-Y,)ds+ t  Jo  Jo 62  1/2  <W,  where W is another Brownian motion. Let Xt be a solution on the same space to the equation (3.82) t  with x = 1/4 and W the Brownian motion. Then the comparison Theorem shows X < Y up till the t  t  t  time T = inf(i >0:X /\Y t  = 1/8) = inf(i > 0 : Y = 1/8).  t  t  But the construction of Y implies that there exists t t  such that with positive probability , Y = 0 for  0  t  some i < to < T. So P\To  <T  < t ) > 0.  1/8  (3.86)  0  Equation (3.84),(3.85),(3.86) together imply (3.83). • Example. We examine the simplest nontrivial superprocess. Let E = {a, b] and the underlying spatial motion be a Markov chain leaving each state at rate one. Then if we write Xt(a),X (b)  for  t  Xt({a}),Xt({b})  the martingale problem reduces to a pair of linked stochastic differential equations. X (a)  =  X (a)+  f\x,(b)-X,(a))ds+ Jo  f {X,{a)fl dB , Jo  (3.87)  X (b)  =  X (b)+  f\x,(a)-X,(b))ds+ Jo  l\x.(b)yl dB\ Jo  (3.88)  t  t  where Bf,Bf  0  0  2  a  2  are independent Brownian motions. So we consider the superprocess as a diffusion on R . 2  Let D = ((x,0) : x > l/2)U((0,y) : y > 1/2) . We will show that with probability one  (X (a),X (b)) t  t  never hits D.Define D  =  Rr  =  r  ((*,()):*> (1/2)+ r) ((x,y):y>x-(l/2)-r)  It will be enough to show P((X (a),X (b)) t  6 D for some t > 0) = 0 for all r > 0.  t  T  The properties of the a-dimensional Bessel process ( see Rogers and Williams [20] V.48 ) show that if Zt satisfies dZ = adi + (Z ) dB  (3.89)  l,2  t  then for a > 1/2 , P(Z  t  t  t  > 0,V< > 0) = 1 and a = 1/2 is critical.  By comparing (3.88) to the  S.D.E. (3.89) solved on the same space with respect to B\ we see that X (b) t  So = inf(t > 0 : {X (a),X (b)) t  G R ). Define  t  0  T  =  inf(i>5„_i  n  =  inf(< >T  S  n  n  :(Xt(a),X (b))€R° ) t  /2  :(X (a),X (6))Gl ) t  63  t  0  > 0 up till the time  X (b) t  1/2  X (a) t  1/2  R  Figure 3.3: Typical sample path of  (X {a),X (b)). t  t  Then by the strong Markov property and the same comparison argument X (b) > 0 for T t  n  < t < S„.  By continuity of paths T„ | oo and the result is proved. The finite lifetime of the process implies that the diffusion converges to (0,0) and Theorem 35 shows that it approaches the origin in a particular manner . There exists R(ui) > 0 such that inside 2?((0,0), R) the diffusion will not hit one axis and will hit the other axis at an infinite number of points that accumulate at (0,0). We would like to extend Theorem 3.5 to superprocesses with more general spatial motion. We take one step in this direction by showing that the semimartingale decomposition for X (H) t  in section 3.1  allows us to extend Theorem 3.5 to super symmetric stable processes of index a < 1/2. Note also that the result is true for super Brownian motion (see Liu [15] where it is shown that the diameter of the support of super Brownian motion converges to zero at extinction ). L e m m a 3.7 Let Xt be a one dimensional super symmetric stable process of index a started at m 6 M F ( R ) . IfO<a,0< PROOF.  1/2 then the patht —• X (\x\~ ) p  t  is continuous on (0,oo).  We use a stopping time argument similar to that in Perkins [16] Proposition 4.4. Take the  nonstandard model with  « m. Find a,/3 so that 0 < max(a,/3) < /? < a < 1/2. Write B  n  B(0,2~") and rB for (rx : x e B). Fix integer M > 1 and define T  n  = inf(t e F f l [1/M,oo) : 64  N (B ) > 2.2-""). L  n  for  Let t? = j2~ *,/;  = [t?, t ? ) . Then  n  +1  P(T„e/;)  =  P ( T „ ei?,N (2B )>  +  P(T £l»,^  +  P ( T „ £ z;,^- £  tUi  2-"?)  n  £  n  I(^g5„,^ I(yV  1  (3.90)  iA)<2^)  + i i  N {B )2- P)  G B„, |7V7 - yV?„| > 2" ) >  n  n  7 n  Tn  V  n  Denote the terms on the right hand side of (3.90) as 1,11 and III.For t" > 1/m /  <  P(iV  < ; + i  (2B )>2-^) n  2 'E(X (2B ))  <  n  t7+i  <  2m(l)M-  1 / o  n  2-  -^.  n ( 1  T„ is a At stopping time so by the strong Markov property  II  = E[l(T er?)-p \^  £  NTjw  n  W ^ » , «  1  - T .  M  r A ) < W / 2 )  7~«; -T.(«) +1  <  P(T„ € /;)exp(-2"( -^/4) 5  using Perkins [16] Lemma 4.1.a. Similarly  III <  E\l(T eI?)P" " \nT  ( u  Y  1  n  l  ( \  N  k - T ^ - ^ \ > ^ )  n  ) > N  T  n  (  u  )  ( B  n  ) - 2 ^ ) \  P(T-neI?)2.P (\Y -n*\>2- )  <  n  0  <  CP(T  2  e / ?)2-"(*- ) a  n  ;  using Perkins [16] Lemma 4.2.a. Summing (3.90) over 7" C [1/M, M] we have P(T„ <E [l/M,M])  < C ( 2 - " - - ' ) + exp(-2"( -^) + 2 (1  5  5  5  n(i  - >) a  which sums over n. So for large n , for all t 6 [1/M, M] < 2.2~ K  X ((-2- ,2- )) n  n  n  t  Thus X (\x\-P) t  is uniformly bounded for t G [1/M,M] and (X (|a:|  sequence in C [ l / M , M], P  t  m  — a.s. So X (\x\~P) t  _/3  A n) : n = 1,2,...) is a Cauchy  is continuous on [ l / M . M ] for any M. • 65  Remark. In Perkins [16] Theorem 6.5 it shown that if a < 1 then there exist constants 0 < eg < c < 9  oo such that for any m G Mp(R) , setting 4> {x) =  x<Mog log +  a  A  =  t  l/x  +  (z : HmsupXt(S(a;,a))^ (a) ajo  G [cg,c ])  _1  0  9  then X (A )  = 0 , V i > 0 ,P  c  t  t  -a.s.  m  We call a point in A a point of density for Xt- For a < 1 it follows from the facts that t  i. A« is Lebesgue null for all t > 0 (Perkins [16] ) ii. The laws of super symmetric stable processes on <r(X, : s > to > 0) are equivalent under translation of initial measures (Evans and Perkins [8]) that for any fixed x , P(x is a point of density at some t > 0) = 0 . This also follows ( for a < 1/2 ) from Lemma 3.7 for if 0 G A< then A"t(|x| ) = oo. Contrast this with the fact that equation (3.78) -a  implies that for a fixed t > 0 the points of density are dense in R. Proposition 3.8 If X  t  is a super symmetric stable process of index a < 1/2 tn dimension one started  at a finite measure then the conclusions of Theorem 3.5 still hold. PROOF. Fix an open ball B = (a, 6) of finite radius in R. From Theorem 3.2 and the following remark we have the decomposition X {B)  = X (B)  t  where (M(B))  t  = /' X,(B)ds  + Vt + M (B) t  and V has finite variation on [S,T] for any 0 < S < T < oo. Define  {  t  limh_o+(V <+j — Vt)/h  if this limit exists  0  otherwise  r  l  We will find an upper bound on at infinity. Let g(x) = s u p  0  <>0  Note that for any 6 > 0 , T IB(X) is a C°° function vanishing (  |AT<Is(x)|. Scaling arguments show there exists C such that g(x) <  C(\x - a\~ + \x - b\- ). For fixed 0 < s < t a  a  \X (T I ) t  =  |j  s  B  - X,(T l )  X (AT l )dr\ r  - M {T l )  6 B  t B  t  <J 66  6 B  X (g)dr. r  +  M,(T l )\ 6 B  Letting 6 | 0 \V -V,\  = \X (B) - X,(B) - M (B) + M,(B)\  t  t  t  <J  X (g)dr. r  X (g) is continuous and bounded by Lemma 3.7 so V is absolutely continuous and \v \ < X (g) for a.a.r Q r  t  r  n  r  a.s. Now we follow the proof of Theorem 3.5. Recall that t= and we set Y (B) = X ,(B)/X (l) t  D  Z = Z+ t  0  I Jo  l/X (l)dr  and Z {B) = Y (B)(l  Dt  t  - Y (B)).  t  t  [\(l-2Y (B))v ,-Z )ds+ Jo N+s  DN+  (3.91)  r  Then Z (B)  satisfies  t  j\z {B){\-AZ (B))yi dB . 2  s  s  Jo  s  s  Now the comparison argument of Theorem 3.5 will work provided we can show < 1/8 for a.a.s) — 1 as yV  P(\VD .I N+  But from (3.91) we have \t-s\ < \D -D,\max < < (l/Xr(l)) t  a.s. From Lemma 3.7 X (g) Dr  3.4  (Dt  r  oo  (3.92)  so that |vn | < X (g)  Dt)  r  Dr  for a.a.r.  Qm  —*• 0 as r —• oo and (3.92) follows. •  Recovering the spatial motion  How much can you tell about the underlying spatial motion from a single path of a superprocess? In the following result we use an arbitrarily short piece of the path but recover only partial information. i  Lemma 3.9 Let X% be a superprocess started at m E Mp(E)  with spatial motion a Feller process with  generator A . For f € D(A) satisfying m(|/|) = 0 and Var{X {f))  = 0(t ) 2  t  as t -  0  (3.93)  there is a sequence tj [ 0 such that n  {l/n)Y,tJ Xt V)*™W)-  (3-94)  1  i  P R O O F . Set Z = (l/t)X (/).Then t  t  E(Zt)  =  (l/t)(m(/)+ f  E{X,{Af))ds  Jo =  (1/0/ mT.{Af)da-> Jo  67  m{Af)  as < 0  (3.95)  Hypothesis (3.93) ensures that Var(Z<) remains bounded so it remains only to show that we can pick tj I 0 fast enough that the Z ' s are nearly uncorrelated. Using a product moment formula (Dynkin [6] <y  Theorem 1.1 ) we have for s < t Cov(Z„Z ) t  Note that mT (|/|)  =  (l/st)  f dm f JE JO  T {T -rU)T-rU))dr  <  (l/0||/||mT.(|/|).  r  t  —• m ( | / | ) = 0 as s —• 0. Now pick to > 0 arbitrarily small and t„ inductively so that  a  (3.96)  Cov(Z ,Z J<2-\ -»l m  tn  t  Then £(((l/n)][>.-m(A/)) ) i=i 2  =  ( 1 / n ) JT E((Z . 2  t  + (1/n ) £  - m(Af)f)  Cov(Z ,,  2  t  Z) tj  n  +(l/n )YE(Z 2  ti  - m(Af))E(Z  -  tj  m(Af))  (3.93),(3.95),(3.96) ensure that all three terms go to zero. • Remark.  Since ,  = ( 1 / t ) / ' mT (T _ (f))dr Jo  (l/t )Var(X {f))  2  2  t  2  r  r  a sufficient condition for hypothesis (3.93) t o hold is that /  2  ( l / t ) m T ( / ) = (1/t) f mT (A(f ))dr Jo 2  2  t  I f this condition holds then we may take t  r  n  <  (l/t)mT (f ) 2  t  € D(A) for then < p(/ )||m(l). 2  = 2~" in (3.94).  As an example we take the underlying motion to be a pure j u m p Levy process on the line (Y : t > 0). t  Hence £(exp(-»0Y«)) = exp(t / {e JTl  iBx  - 1 - (i0x/(l +  where fi, the Levy measure , gives finite mass to (—a,a)  c  x )))n{dx)) 2  for any a > 0. We show that from any initial  segment of a path of the super Levy process Xt started at 6o we can recover the Levy measure /z. Fix a > 0 and let / = I(a,oo) Then although / 0 D(A) i t can be shown that ( T , / ( 0 ) - / ( 0 ) ) / t = P(Y € (a, oo))/t - /i(a, oo) as t - 0. t  ' 68  (3.97)  r  Find t so that P(\Y,\ > a/2) < 2sfi([-a/2,a/2] )  for all s < t -  c  0  Var(X (f))  =  t  0  fT (T _ f)(0)dr 2  T  Jo  r  <  [ P(\Y \>a/2) Jo  <  2 i([-a/2  +  r  f  (P(Y - <E(a/2,oo)) dr 2  t  r  a/2] ) j (r + (t - r) )dr = 0(t ) Jo e  >  2  (3.98)  2  The proof of Lemma 3.9 shows that the bounds (3.97),(3.98) together will imply n  := ( l / n ) £ V ' X - y ( / ) ^ ,1(0,00)  S  n  2  and along a subsequence (njt)* therefore S  —* /i(a, oo) almost surely . From a countable number of  nk  intervals all bounded away from zero we can recover the entire measure fx in this way.  Corollary 3.10 Suppose Ai,A  are generators of two conservative Feller processes on E and that there  2  exists f e C(E) satisfying f,f  £ D(A ) D D(A )  2  X  2  and Aif(x)  ^ A f(x) 2  for some x £ E . Let Pi be  the law of the superprocess with spatial motion generated by Ai and started at 8  X  .Then P\ and P are 2  singular. P R O O F . For conservative Feller processes we have Ail = 0 so that replacing / by / — f(x) £ D(Ai) we may assume that f(x) = 0 . The proof of Lemma 3.9 and the following remark shows that we can find an explicit subsequence (nk)k such that Pi lives on the measurable subset Q = {« :(l/ )E ' 2-'(/) 2  Since (l/n*)  w  if( )  A  n t  x  x  as k  co)  2 W -J(/) will have a subsequence that converges almost surely to A f(x) j  2  2  , under P  2  we have P (fti) = O.D 2  We return to the example of section 3.3 to show that it may not be possible to recover the entire underlying motion. Let E = {a,b},Q = D([0,oo),M (E)),X (u) t  F  = u{t),T  = <T{X, : s > 0). Let P ^  0  ^ be the law  on CI of the superprocess with spatial motion a Markov chain on E with generator Af(a) = —Af{b) = rf(b)  — f(a) and started at ao6 + 6 6j .Thus the 'particles' jump from a to b at rate one and from b to a  0  a at rate r.  Proposition 3.11 For r i , r > 0 there exist ao,6o > 0 ch that p^ ") and Pr°°' °^ are not singular su  2  measures. 69  0,6  b  P R O O F . We write X (a),X (b) t  {  for X ({a}),X ({b}).  t  t  Define  t  inf(0 < t < £ : Xt{a)/X (l)  < 1/2)  t  if this set is non-empty otherwise  +00  We will find M > 0 so that if ft = {T = + o o , £ < M u p , < X,(l) )8  0  e  < M] then P ^ ' ^ ^ ) > 0 and 0  4  then give an explicit Radon-Nikodym derivative for  From the characterisation of the law of the death point ( equation (3.76)) we have Pr]'*\F  = a) > 0.  So P£' Hx (a)/X (l)-+l)>0. l  t  Pick n so that if  = inf(t > 0 : X (l)  t  < 1/n) then  t  P^HXtW/Xtil)  > 1/2 for all  < t < 0 > 0.  Then by the strong markov property if F(x,y) is the distribution function of the pair  (X (a),X (b)) in  in  under Pr]' ^ then 1  0< r r Jo Jo So we may pick a ,b 0  so that p ° ' ( T ' (  0  0  lo)  r |  surely we may fiad M so that  P  ( 0o, r I  =  P^ \T  = +cc)aT:r, y.  V  +00) > 0 and since { < 00 and s u p ,  * (fi ) > o)  <{  X (l) t  < 00 almost  0.  0  Let R = T A inf(t > 0 : X , ( l ) > Af) A inf(t > M : X , ( l ) > 0) where we let inf(0) = 00. Under  P  i(o ,*o) 0  J M*(b)  =  X  4 A  rtAR  '  (rxX.(6)-X.(o))d«  0  «(6)-6 - /  (^.(oJ-rxX.t*))^  0  Jo are martingales satisfying <A/*(a))« = ft** X.(a)ds,  (X (b)) R  t  =/ '  A f i  0  X,(b)ds and (M (a), R  Define  V t  Then  =  /V2 Jo  - r ) X . ( ) / X , ( ) d M , ( a ) + '/Vi Jo  =  5(Z ) = exp(Z« - (1/2)<Z),)  fi  1  0  a  "  r )dM, (b) R  2  t  »tA.R  (Z), = / Jo  (r, - r ) ((X. (6)/X.(a)) + X.(6))ds. 2  2  2  70  M (b)) R  i  = 0.  For t < T , X,(b)/X,(a)  < 1 so that (Z)  < M ( r - r ^ ) for all t . This ensures that % is a uniformly 2  t  2  2  integrable martingale ( see Elliot [7] Theorem 13.27 ) . Define Q by dQ/dP^ '  = n  a ha)  TO  Then (see Rogers  and Williams [20] Theorem IV.38.4 ) under Q ft A H  (a)-{M (a),Z)  R t  R  t  =  X (a)-°-o-  / Jo  tAR  J are martingales with the same brackets processes as M (a), R  T  R  which must agree w i t h P ^  Thus Q\  no  =  0 , B O )  . But Q  0  2  -  X,(a))ds  tAR  ' o  M (b). R  (X,(a)-r X,(b))ds 2  This characterises the law of Q on  - { R = oo} so if A C ft then A = An { R = oo} e FR • 0  P r' |n„.D (  f  (r X,(b)  bo)  r  71  Bibliography  [1] Albevario.S.,Fenstad.J.E.,Hoegh-Krohn.R. and Lindstrom.T. (1986) Nonstandard  Methods in  Stochastic Analysis and Mathematical Physics, Academic Press. [2] Anderson.R.M. and Rashid.S. (1978) A nonstandard characterization of weak Proc.Amer.Math.Soc.  convergence,  69, 327-332.  [3] Cutland.N.(1983) Nonstandard measure theory and its applications, Bull. London Math. Soc. 15, 529-589. [4] Dawson.D.A. and Hochberg.K.J. (1979) The carrying dimension of a stochastic measure diffusion, Ann. Probab. 7, 693-703. [5] Dawson.D.A., Iscoe.I. and Perkins.E. (1988). Super-Brownian motion: path properties and hitting probabilities. To appear in  Prob.Th.Rel.Fields.  [6] Dynkin.E.B. (1988) Representation for functionals of superprocesses by multiple stochastic integrals, with applications to self-intersection local times.To appear in Asterisque. [7] Elliot.R.J. (1982) Stochastic Calculus and Applications  , Springer,Berlin.  [8] Evans.S.N. and Perkins.E. (1989) Absolute continuity results for superprocesses with some applications.Preprint. [9] Falconer.K.J. (1985) The Geometry of Fractal Sets, Cambridge tracts in mathematics 85, Cambridge University press. [10] Fitzsimmons.P.J. (1988) Construction and regularity of measure valued branching processes.Israel J.Math. [11] Harris.T.E. (1963) The Theory of Branching Processes, Springer-Verlag, New York. [12] Kauffman.R. (1969) Une propriete metrique du mouvement brownien. C.R.Acad.Sci.Paris. 727-728.  72  268,  [13] Kingman.J.F.C. (1973) An intrinsic description of local time. J.London.Math.Soc.fS.StT.2,725-731. [14] Konno.N. and Shiga.T. (1988) Stochastic differential equations for some measure valued diffusions. To appear in Prob. Th.Rel. Fields. [15] Liu.L. (1988) Processes before extinction and comparison of measures by counting atoms. Ph.D. Thesis . University of Rochester. [16] Perkins.E. (1988). A space-time property of a class of measure-valued branching diffusions. To appear in Trans. Amer. Math. Soc. [17] Perkins.E. (1988) Polar sets and multiple points for Super-Brownian motion. To appear in Ann.Prob. [18] Perkins.E. (1988). The Hausdorff measure of the closed support of super Brownian motion.To appear in Annates de I'Instilute Henri  Poincare.  [19] Roelly-Coppoletta.S. (1986) A criterion of convergence of measure-valued processes: application to measure branching processes, Stochastics 17,43-65. [20] Rogers.L.C.G. and Williams. D. (1987) Diffusions,Markov  Processes , and Martingales ,Volume 2:  ltd Calculus. Wiley. [21] Sawyer.S. Branching diffusion processes in population genetics. Preprint. [22] Shiga.T. (1988) A stochastic equation based on a Poisson system for a class of measure valued diffusion processes. Preprint. [23] Sugitani.S. (1988) Some properties for the measure valued branching diffusion process. Preprint. [24] Walsh.J.B. (1986) An introduction to stochastic partial differential equations.Lecture notes in Mathematicsll80  (Springer Verlag) 265-439.  [25] Watanabe.S. (1968) A limit theorem of branching processes and continuous state branching processes. J.Math.Kyoto.  Univ.%, 141-169.  73  

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