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arXiv 2406.03096hep-phhep-ex
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学习识别R宇称破坏的标量顶夸克衰变

Learning to see R-parity violating scalar top decays

Gerrit Bickendorf, Manuel Drees

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13 pages, 9 figures
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AI总结：

本文利用CoAtNet和MaxViT等transformer视觉模型对R宇称破坏标量顶夸克衰变进行事例分类，相较CNN显著提升信号显著性，并可将标量顶夸克质量排除极限提高约60-100 GeV。

摘要：

本文引入近期改进的计算机视觉机器学习方法，用于粒子物理中的事例分类问题。超对称标量顶夸克衰变为顶夸克和弱标度类bino中性微子，其中中性微子通过$UDD$算符衰变为三个夸克，这类过程难以搜寻，因此受到的实验约束较弱。可以利用 boosted 衰变产物的喷注子结构来区分信号与背景事例。我们将基于 transformer 的计算机视觉模型 CoAtNet 和 MaxViT 应用于由喷注组分构建的图像，并将其分类性能与更经典的卷积神经网络（CNN）进行比较。我们发现计算机视觉的结果能够很好地迁移到物理应用中，且两种基于 transformer 的模型均优于 CNN。通过用 MaxViT 替换 CNN，在某些中性微子质量下，$S/\sqrt{B}$ 提高了接近 2 倍。我们表明，将该分类器与额外特征结合可以实现背景与信号的强分离。我们还发现，在一个简单的模拟分析中用 MaxViT 模型替换 CNN，可以在中性微子质量为 $100$ GeV 和 $500$ GeV 时，将标量顶夸克质量的 95% C.L. 排除极限分别提高约 $100$ GeV 和 $60$ GeV。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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