An Evaluation of Representation Learning Methods in Particle Physics Foundation Models
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866909906129387520 |
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| author | Chen, Michael Kansal, Raghav Gandrakota, Abhijith Hao, Zichun Ngadiuba, Jennifer Spiropulu, Maria |
| author_facet | Chen, Michael Kansal, Raghav Gandrakota, Abhijith Hao, Zichun Ngadiuba, Jennifer Spiropulu, Maria |
| contents | We present a systematic evaluation of representation learning objectives for particle physics within a unified framework. Our study employs a shared transformer-based particle-cloud encoder with standardized preprocessing, matched sampling, and a consistent evaluation protocol on a jet classification dataset. We compare contrastive (supervised and self-supervised), masked particle modeling, and generative reconstruction objectives under a common training regimen. In addition, we introduce targeted supervised architectural modifications that achieve state-of-the-art performance on benchmark evaluations. This controlled comparison isolates the contributions of the learning objective, highlights their respective strengths and limitations, and provides reproducible baselines. We position this work as a reference point for the future development of foundation models in particle physics, enabling more transparent and robust progress across the community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12829 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Evaluation of Representation Learning Methods in Particle Physics Foundation Models Chen, Michael Kansal, Raghav Gandrakota, Abhijith Hao, Zichun Ngadiuba, Jennifer Spiropulu, Maria Machine Learning We present a systematic evaluation of representation learning objectives for particle physics within a unified framework. Our study employs a shared transformer-based particle-cloud encoder with standardized preprocessing, matched sampling, and a consistent evaluation protocol on a jet classification dataset. We compare contrastive (supervised and self-supervised), masked particle modeling, and generative reconstruction objectives under a common training regimen. In addition, we introduce targeted supervised architectural modifications that achieve state-of-the-art performance on benchmark evaluations. This controlled comparison isolates the contributions of the learning objective, highlights their respective strengths and limitations, and provides reproducible baselines. We position this work as a reference point for the future development of foundation models in particle physics, enabling more transparent and robust progress across the community. |
| title | An Evaluation of Representation Learning Methods in Particle Physics Foundation Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.12829 |