Transfer Learning Beyond the Standard Model
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911226171228160 |
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| author | Krishnaraj, Veena Bayer, Adrian E. Jespersen, Christian Kragh Melchior, Peter |
| author_facet | Krishnaraj, Veena Bayer, Adrian E. Jespersen, Christian Kragh Melchior, Peter |
| contents | Machine learning enables powerful cosmological inference but typically requires many high-fidelity simulations covering many cosmological models. Transfer learning offers a way to reduce the simulation cost by reusing knowledge across models. We show that pre-training on the standard model of cosmology, $Λ$CDM, and fine-tuning on various beyond-$Λ$CDM scenarios -- including massive neutrinos, modified gravity, and primordial non-Gaussianities -- can enable inference with significantly fewer beyond-$Λ$CDM simulations. However, we also show that negative transfer can occur when strong physical degeneracies exist between $Λ$CDM and beyond-$Λ$CDM parameters. We consider various transfer architectures, finding that including bottleneck structures provides the best performance. Our findings illustrate the opportunities and pitfalls of foundation-model approaches in physics: pre-training can accelerate inference, but may also hinder learning new physics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19168 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transfer Learning Beyond the Standard Model Krishnaraj, Veena Bayer, Adrian E. Jespersen, Christian Kragh Melchior, Peter Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Machine Learning Data Analysis, Statistics and Probability Machine learning enables powerful cosmological inference but typically requires many high-fidelity simulations covering many cosmological models. Transfer learning offers a way to reduce the simulation cost by reusing knowledge across models. We show that pre-training on the standard model of cosmology, $Λ$CDM, and fine-tuning on various beyond-$Λ$CDM scenarios -- including massive neutrinos, modified gravity, and primordial non-Gaussianities -- can enable inference with significantly fewer beyond-$Λ$CDM simulations. However, we also show that negative transfer can occur when strong physical degeneracies exist between $Λ$CDM and beyond-$Λ$CDM parameters. We consider various transfer architectures, finding that including bottleneck structures provides the best performance. Our findings illustrate the opportunities and pitfalls of foundation-model approaches in physics: pre-training can accelerate inference, but may also hinder learning new physics. |
| title | Transfer Learning Beyond the Standard Model |
| topic | Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Machine Learning Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2510.19168 |