UBC Theses and Dissertations
How to count clustered galaxies Wang, Yunting
We are still trying to understand how galaxies form and evolve. Galaxies bridge the large scales of the expanding Universe and the relatively small scales of stellar systems by tracing the cosmic evolution of matter in individual structures that undergo physical processes. A subset of galaxies, dusty star-forming galaxies (DSFGs), is key to providing insights into the underlying physics. DSFGs emit the bulk of their light at far-infrared to millimetre wavelengths. They are very actively star-forming and are more common in the early Universe, when the details of galaxy evolution remain unclear. With decades of observations, statistical analysis is important to understand the physics behind the whole population of galaxies. In this study, we investigate galaxy number counts, i.e.the number density of galaxies as a function of their flux density, at submillimetre wavelengths using the 'P(D)' fluctuation analysis method. This is a widely used statistical framework that probes the galaxy number counts to the faint end below the detection limit, which is achieved by analyzing the contribution of light from faint galaxies in the overall fluctuations in the map, i.e.the flux histogram. However, P(D) assumes a random spatial distribution of galaxies, whereas galaxies are actually clustered, tracing the cosmic structure. We study the effect of clustering in galaxy number counts using the SIDES (Simulated Infrared Dusty Extragalactic Sky) simulation, which has realistic clustering from the computation of dark matter halos where galaxies reside. We then simulate observed maps for Herschel-SPIRE (Spectral and Photometric Imaging REceiver) at 500𝝻m. We find that clustering biases galaxy number counts. We explore the relation between clustering strength and its bias in the flux histogram, and find that a simple form of correction function can effectively characterize the clustering bias, with its parameters fully determined by the known instrumental properties, together with the two-point correlation function in the map. We test the correction method on simulated maps and improve the resulting galaxy number counts significantly. Our method can be used to revise galaxy number counts in existing data and serve as a powerful tool for future surveys from far-infrared to radio wavelengths.
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