PoPE: A Populationbased Approach to Model the Spatial Structure of Astronomical Systems
Abstract
We present a novel populationbased Bayesian inference approach to model the average and population variance of the spatial distribution of a set of observables from ensemble analysis of low signaltonoiseratio measurements. The method consists of (1) inferring the average profile using Gaussian processes and (2) computing the covariance of the profile observables given a set of independent variables. Our model is computationally efficient and capable of inferring average profiles of a large population size from noisy measurements, without stacking data or parameterizing the shape of the mean profile. We demonstrate the performance of our method using dark matter, gas, and stellar profiles extracted from hydrodynamical cosmological simulations of galaxy formation. POPULATION PROFILE ESTIMATOR is publicly available in a GitHub repository. Our new method should be useful for measuring the spatial distribution and internal structure of a variety of astrophysical systems using large astronomical surveys.
 Publication:

The Astronomical Journal
 Pub Date:
 January 2021
 DOI:
 10.3847/15383881/abc630
 arXiv:
 arXiv:2006.16408
 Bibcode:
 2021AJ....161...30F
 Keywords:

 Astronomy data modeling;
 Astronomy data analysis;
 Spatial point processes;
 Astrostatistics;
 Open source software;
 1859;
 1858;
 1915;
 1882;
 1866;
 Astrophysics  Cosmology and Nongalactic Astrophysics;
 Astrophysics  Astrophysics of Galaxies;
 Astrophysics  Instrumentation and Methods for Astrophysics
 EPrint:
 14 pages, 9 figures, 2 tables, the code is publicly available at https://github.com/afarahi/PoPE