MACK: Mismodeling Addressed with Contrastive Knowledge
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866912361796861952 |
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| author | Sheldon, Liam Rankin Rankin, Dylan Sheldon Harris, Philip |
| author_facet | Sheldon, Liam Rankin Rankin, Dylan Sheldon Harris, Philip |
| contents | The use of machine learning methods in high energy physics typically relies on large volumes of precise simulation for training. As machine learning models become more complex they can become increasingly sensitive to differences between this simulation and the real data collected by experiments. We present a generic methodology based on contrastive learning which is able to greatly mitigate this negative effect. Crucially, the method does not require prior knowledge of the specifics of the mismodeling. While we demonstrate the efficacy of this technique using the task of jet-tagging at the Large Hadron Collider, it is applicable to a wide array of different tasks both in and out of the field of high energy physics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13947 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MACK: Mismodeling Addressed with Contrastive Knowledge Sheldon, Liam Rankin Rankin, Dylan Sheldon Harris, Philip High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment The use of machine learning methods in high energy physics typically relies on large volumes of precise simulation for training. As machine learning models become more complex they can become increasingly sensitive to differences between this simulation and the real data collected by experiments. We present a generic methodology based on contrastive learning which is able to greatly mitigate this negative effect. Crucially, the method does not require prior knowledge of the specifics of the mismodeling. While we demonstrate the efficacy of this technique using the task of jet-tagging at the Large Hadron Collider, it is applicable to a wide array of different tasks both in and out of the field of high energy physics. |
| title | MACK: Mismodeling Addressed with Contrastive Knowledge |
| topic | High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2410.13947 |