Learnable wavelet neural networks for cosmological inference Article Swipe
Christian Pedersen
,
Michael Eickenberg
,
Shirley Ho
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.14362
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.14362
Convolutional neural networks (CNNs) have been shown to both extract more information than the traditional two-point statistics from cosmological fields, and marginalise over astrophysical effects extremely well. However, CNNs require large amounts of training data, which is potentially problematic in the domain of expensive cosmological simulations, and it is difficult to interpret the network. In this work we apply the learnable scattering transform, a kind of convolutional neural network that uses trainable wavelets as filters, to the problem of cosmological inference and marginalisation over astrophysical effects. We present two models based on the scattering transform, one constructed for performance, and one constructed for interpretability, and perform a comparison with a CNN. We find that scattering architectures are able to outperform a CNN, significantly in the case of small training data samples. Additionally we present a lightweight scattering network that is highly interpretable.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2307.14362
- https://arxiv.org/pdf/2307.14362
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385373655
All OpenAlex metadata
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https://openalex.org/W4385373655Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2307.14362Digital Object Identifier
- Title
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Learnable wavelet neural networks for cosmological inferenceWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-24Full publication date if available
- Authors
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Christian Pedersen, Michael Eickenberg, Shirley HoList of authors in order
- Landing page
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https://arxiv.org/abs/2307.14362Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2307.14362Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2307.14362Direct OA link when available
- Concepts
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Interpretability, Convolutional neural network, Inference, Artificial intelligence, Wavelet, Scattering, Artificial neural network, Pattern recognition (psychology), Computer science, Domain (mathematical analysis), Machine learning, Physics, Mathematics, Mathematical analysis, OpticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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