Insights into Galaxy Evolution from Interpretable Sparse Feature Networks Article Swipe
Galaxy appearances reveal the physics of how they formed and evolved. Machine learning (ML) models can now exploit galaxies’ information-rich morphologies to predict physical properties directly from image cutouts. Learning the relationship between pixel-level features and galaxy properties is essential for building a physical understanding of galaxy evolution, but we are still unable to explicate the details of how deep neural networks represent image features. To address this lack of interpretability, we present a novel neural network architecture called a Sparse Feature Network (SFNet). SFNets produce interpretable features that can be linearly combined in order to estimate galaxy properties like optical emission line ratios or gas-phase metallicity. We find that SFNets do not sacrifice accuracy in order to gain interpretability, and that they perform comparably well to cutting-edge models on astronomical ML tasks. Our novel approach is valuable for finding physical patterns in large data sets and helping astronomers interpret ML results.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3847/1538-4357/adadec
- OA Status
- gold
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407556519
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4407556519Canonical identifier for this work in OpenAlex
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https://doi.org/10.3847/1538-4357/adadecDigital Object Identifier
- Title
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Insights into Galaxy Evolution from Interpretable Sparse Feature NetworksWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-13Full publication date if available
- Authors
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John F. WuList of authors in order
- Landing page
-
https://doi.org/10.3847/1538-4357/adadecPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://doi.org/10.3847/1538-4357/adadecDirect OA link when available
- Concepts
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Physics, Galaxy, Feature (linguistics), Astrophysics, Galaxy formation and evolution, Astronomy, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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44Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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