Browsing by Author "Perreault-Levasseur, Laurence"

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    GaMPEN: A Machine-learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
    (2022) Ghosh, Aritra; Urry, C. Megan; Rau, Amrit; Perreault-Levasseur, Laurence; Cranmer, Miles; Schawinski, Kevin; Stark, Dominic; Tian, Chuan; Ofman, Ryan; Ananna, Tonima Tasnim; Auge, Connor; Cappelluti, Nico; Sanders, David B.; Treister, Ezequiel
    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 (similar to 7%) in R ( e ), and 6.3 x 10(4) nJy (similar to 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 greater than or similar to 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.
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    Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey
    (Wiley, 2023) Ghosh, Aritra; Urry, C. Megan; Mishra, Aayush; Perreault-Levasseur, Laurence; Natarajan, Priyamvada; Sanders, David B.; Nagai, Daisuke; Tian, Chuan; Cappelluti, Nico; Kartaltepe, Jeyhan S.; Powell, Meredith C.; Rau, Amrit; Treister, Ezequiel
    We use the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters and associated uncertainties for & SIM;8 million galaxies in the Hyper Suprime-Cam Wide survey with z & LE; 0.75 and m & LE; 23. GaMPEN is a machine-learning framework that estimates Bayesian posteriors for a galaxy's bulge-to-total light ratio (L ( B )/L ( T )), effective radius (R ( e )), and flux (F). By first training on simulations of galaxies and then applying transfer learning using real data, we trained GaMPEN with <1% of our data set. This two-step process will be critical for applying machine-learning algorithms to future large imaging surveys, such as the Rubin-Legacy Survey of Space and Time, the Nancy Grace Roman Space Telescope, and Euclid. By comparing our results to those obtained using light profile fitting, we demonstrate that GaMPEN's predicted posterior distributions are well calibrated (& LSIM;5% deviation) and accurate. This represents a significant improvement over light profile fitting algorithms, which underestimate uncertainties by as much as & SIM;60%. For an overlapping subsample, we also compare the derived morphological parameters with values in two external catalogs and find that the results agree within the limits of uncertainties predicted by GaMPEN. This step also permits us to define an empirical relationship between the Sersic index and L ( B )/L ( T ) that can be used to convert between these two parameters. The catalog presented here represents a significant improvement in size (& SIM;10x), depth (& SIM;4 mag), and uncertainty quantification over previous state-of-the-art bulge+disk decomposition catalogs. With this work, we also release GaMPEN's source code and trained models, which can be adapted to other data sets.