Contrastive Learning for Robust Representations of Neutrino Data
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908373868347392 |
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| author | Wilkinson, Alex Radev, Radi Alonso-Monsalve, Saul |
| author_facet | Wilkinson, Alex Radev, Radi Alonso-Monsalve, Saul |
| contents | In neutrino physics, analyses often depend on large simulated datasets, making it essential for models to generalise effectively to real-world detector data. Contrastive learning, a well-established technique in deep learning, offers a promising solution to this challenge. By applying controlled data augmentations to simulated data, contrastive learning enables the extraction of robust and transferable features. This improves the ability of models trained on simulations to adapt to real experimental data distributions. In this paper, we investigate the application of contrastive learning methods in the context of neutrino physics. Through a combination of empirical evaluations and theoretical insights, we demonstrate how contrastive learning enhances model performance and adaptability. Additionally, we compare it to other domain adaptation techniques, highlighting the unique advantages of contrastive learning for this field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07724 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Contrastive Learning for Robust Representations of Neutrino Data Wilkinson, Alex Radev, Radi Alonso-Monsalve, Saul High Energy Physics - Experiment In neutrino physics, analyses often depend on large simulated datasets, making it essential for models to generalise effectively to real-world detector data. Contrastive learning, a well-established technique in deep learning, offers a promising solution to this challenge. By applying controlled data augmentations to simulated data, contrastive learning enables the extraction of robust and transferable features. This improves the ability of models trained on simulations to adapt to real experimental data distributions. In this paper, we investigate the application of contrastive learning methods in the context of neutrino physics. Through a combination of empirical evaluations and theoretical insights, we demonstrate how contrastive learning enhances model performance and adaptability. Additionally, we compare it to other domain adaptation techniques, highlighting the unique advantages of contrastive learning for this field. |
| title | Contrastive Learning for Robust Representations of Neutrino Data |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2502.07724 |