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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2406.03096 |
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| _version_ | 1866910474504765440 |
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| author | Bickendorf, Gerrit Drees, Manuel |
| author_facet | Bickendorf, Gerrit Drees, Manuel |
| contents | With this article we introduce recent, improved machine learning methods from computer vision to the problem of event classification in particle physics. Supersymmetric scalar top decays to top quarks and weak scale bino-like neutralinos, where the neutralinos decay via the $UDD$ operator to three quarks, are difficult to search for and therefore weakly constrained. The jet substructure of the boosted decay products can be used to differentiate signal from background events. We apply transformer-based computer vision models CoAtNet and MaxViT to images built from jet constituents and compare the classification performance to a more classical convolutional neural network (CNN). We find that results from computer vision translate well onto physics applications and both transformer-based models perform better than the CNN. By replacing the CNN with MaxViT we find an improvement of $S/\sqrt{B}$ by a factor of almost 2 for some neutralino masses. We show that combining this classifier with additional features results in a strong separation of background and signal. We also find that replacing a CNN with a MaxViT model in a simple mock analysis can push the 95% C.L. exclusion limit of stop masses by about $100$ GeV and $60$ GeV for neutralino masses of $100$ GeV and $500$ GeV. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03096 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning to see R-parity violating scalar top decays Bickendorf, Gerrit Drees, Manuel High Energy Physics - Phenomenology High Energy Physics - Experiment With this article we introduce recent, improved machine learning methods from computer vision to the problem of event classification in particle physics. Supersymmetric scalar top decays to top quarks and weak scale bino-like neutralinos, where the neutralinos decay via the $UDD$ operator to three quarks, are difficult to search for and therefore weakly constrained. The jet substructure of the boosted decay products can be used to differentiate signal from background events. We apply transformer-based computer vision models CoAtNet and MaxViT to images built from jet constituents and compare the classification performance to a more classical convolutional neural network (CNN). We find that results from computer vision translate well onto physics applications and both transformer-based models perform better than the CNN. By replacing the CNN with MaxViT we find an improvement of $S/\sqrt{B}$ by a factor of almost 2 for some neutralino masses. We show that combining this classifier with additional features results in a strong separation of background and signal. We also find that replacing a CNN with a MaxViT model in a simple mock analysis can push the 95% C.L. exclusion limit of stop masses by about $100$ GeV and $60$ GeV for neutralino masses of $100$ GeV and $500$ GeV. |
| title | Learning to see R-parity violating scalar top decays |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2406.03096 |