New insight on galaxy structure from GALPHAT I. Motivation, methodology, and benchmarks for Sersic models

TL;DR

GALPHAT is a new, fast, and accurate galaxy image analysis tool that provides full posterior distributions for structural parameters, validated through extensive benchmarks on simulated Sersic galaxies under various observational conditions.

Contribution

Introduction of GALPHAT, a novel galaxy decomposition tool that offers full posterior probability distributions and confidence intervals for galaxy structural parameters.

Findings

GALPHAT accurately recovers galaxy parameters across different S/N and structural values.

Parameter covariance increases with S/N and Sersic index, emphasizing the need for full posterior analysis.

Benchmark results demonstrate GALPHAT's effectiveness in diverse observational scenarios.

Abstract

We introduce a new galaxy image decomposition tool, GALPHAT (GALaxy PHotometric ATtributes), to provide full posterior probability distributions and reliable confidence intervals for all model parameters. GALPHAT is designed to yield a high speed and accurate likelihood computation, using grid interpolation and Fourier rotation. We benchmark this approach using an ensemble of simulated Sersic model galaxies over a wide range of observational conditions: the signal-to-noise ratio S/N, the ratio of galaxy size to the PSF and the image size, and errors in the assumed PSF; and a range of structural parameters: the half-light radius $r_e$ and the Sersic index $n$. We characterise the strength of parameter covariance in Sersic model, which increases with S/N and $n$, and the results strongly motivate the need for the full posterior probability distribution in galaxy morphology analyses and later inferences. The test results for simulated galaxies successfully demonstrate that, with a careful choice of Markov chain Monte Carlo algorithms and fast model image generation, GALPHAT is a powerful analysis tool for reliably inferring morphological parameters from a large ensemble of galaxies over a wide range of different observational conditions. (abridged)