1. GaMPEN: A Machine-learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
- Author
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Aritra Ghosh, C. Megan Urry, Amrit Rau, Laurence Perreault-Levasseur, Miles Cranmer, Kevin Schawinski, Dominic Stark, Chuan Tian, Ryan Ofman, Tonima Tasnim Ananna, Connor Auge, Nico Cappelluti, David B. Sanders, and Ezequiel Treister
- Subjects
Space and Planetary Science ,Astrophysics of Galaxies (astro-ph.GA) ,FOS: Physical sciences ,Astronomy and Astrophysics ,Astrophysics::Cosmology and Extragalactic Astrophysics ,Astrophysics - Instrumentation and Methods for Astrophysics ,Instrumentation and Methods for Astrophysics (astro-ph.IM) ,Astrophysics - Astrophysics of Galaxies - 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 ($, Accepted for publication in The Astrophysical Journal. We welcome comments and constructive criticism. Digital assets will be available at http://gampen.ghosharitra.com
- Published
- 2022