PoPE: A population-based approach to model spatial structure of astronomical systems

TL;DR

PoPE introduces a Bayesian population-based method using Gaussian Processes to efficiently model the average and variance of spatial distributions in astronomical systems from noisy data, avoiding stacking or binning.

Contribution

It presents a novel, computationally efficient Bayesian approach for inferring spatial profiles and their variance in large astronomical populations from low SNR measurements.

Findings

Successfully applied to cosmological simulation data

Accurately recovers average profiles without data stacking

Provides a publicly available implementation in GitHub

Abstract

We present a novel population-based Bayesian inference approach to model the average and population variance of spatial distribution of a set of observables from ensemble analysis of low signal-to-noise ratio 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 and binning data nor 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 (PoPE) 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.