GaMPEN: A Machine-learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters Article Swipe
Aritra Ghosh
,
C. M. Urry
,
Amrit Rau
,
Laurence Perreault-Levasseur
,
Miles Cranmer
,
Kevin Schawinski
,
Dominic Stark
,
Chuan Tian
,
Ryan Ofman
,
Tonima Tasnim Ananna
,
Connor Auge
,
N. Cappelluti
,
D. B. Sanders
,
Ezequiel Treister
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3847/1538-4357/ac7f9e
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3847/1538-4357/ac7f9e
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 (∼7%) in R e , and 6.3 × 10 4 nJy (∼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 ≳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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- Language
- en
- Landing Page
- https://doi.org/10.3847/1538-4357/ac7f9e
- https://iopscience.iop.org/article/10.3847/1538-4357/ac7f9e/pdf
- OA Status
- gold
- Cited By
- 11
- References
- 71
- Related Works
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- OpenAlex ID
- https://openalex.org/W4293239474
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https://openalex.org/W4293239474Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3847/1538-4357/ac7f9eDigital Object Identifier
- Title
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GaMPEN: A Machine-learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological ParametersWork title
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
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2022-08-01Full publication date if available
- Authors
-
Aritra Ghosh, C. M. Urry, Amrit Rau, Laurence Perreault-Levasseur, Miles Cranmer, Kevin Schawinski, Dominic Stark, Chuan Tian, Ryan Ofman, Tonima Tasnim Ananna, Connor Auge, N. Cappelluti, D. B. Sanders, Ezequiel TreisterList of authors in order
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https://doi.org/10.3847/1538-4357/ac7f9ePublisher landing page
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https://iopscience.iop.org/article/10.3847/1538-4357/ac7f9e/pdfDirect link to full text PDF
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://iopscience.iop.org/article/10.3847/1538-4357/ac7f9e/pdfDirect OA link when available
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Physics, Galaxy, Astrophysics, Algorithm, Computer scienceTop concepts (fields/topics) attached by OpenAlex
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11Total citation count in OpenAlex
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2025: 3, 2024: 3, 2023: 5Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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