GaMPEN: A Machine Learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters

TL;DR

GaMPEN is a novel machine learning framework that estimates Bayesian posteriors of galaxy morphological parameters, providing accurate uncertainties and enabling automatic image cropping, applicable to large datasets without prior size knowledge.

Contribution

GaMPEN introduces the first ML framework for joint posterior estimation of multiple galaxy morphology parameters, integrating Monte Carlo Dropout and Spatial Transformer Networks.

Findings

Achieves typical errors of 0.1 in $L_B/L_T$, 0.17 arcsec in $R_e$, and 6.3×10^4 nJy in $F$

Predicted uncertainties are well-calibrated and accurate (<5% deviation)

Categorical labels are ≥97% accurate in high residual regions

Abstract

We introduce a novel machine learning framework for estimating the Bayesian posteriors of morphological parameters for arbitrarily large numbers of galaxies. The Galaxy Morphology Posterior Estimation Network (GaMPEN) estimates values and uncertainties for a galaxy's bulge-to-total light ratio ($L_B/L_T$), effective radius ($R_e$), and flux ($F$). To estimate posteriors, GaMPEN uses the Monte Carlo Dropout technique and incorporates the full covariance matrix between the output parameters in its loss function. GaMPEN also uses a Spatial Transformer Network (STN) to automatically crop input galaxy frames to an optimal size before determining their morphology. This will allow it to be applied to new data without prior knowledge of galaxy size. Training and testing GaMPEN on galaxies simulated to match $z < 0.25$ galaxies in Hyper Suprime-Cam Wide $g$-band images, we demonstrate that GaMPEN achieves typical errors of $0.1$ in $L_B/L_T$, $0.17$ arcsec ($\sim 7\%$) in $R_e$, and $6.3\times10^4$ nJy ($\sim 1\%$) in $F$. GaMPEN's predicted uncertainties are well-calibrated and accurate ($<5\%$ deviation) -- for regions of the parameter space with high residuals, GaMPEN correctly predicts correspondingly large uncertainties. We also demonstrate that we can apply categorical labels (i.e., classifications such as "highly bulge-dominated") to predictions in regions with high residuals and verify that those labels are $\gtrsim 97\%$ accurate. To the best of our knowledge, GaMPEN is the first machine learning framework for determining joint posterior distributions of multiple morphological parameters and is also the first application of an STN to optical imaging in astronomy.