Abstract
Abstract We present a novel population-based Bayesian inference approach to model the average and population variance of the 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 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.
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