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arXiv 2203.07353hep-phhep-ex
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利用机器学习提高未来强子末态对撞机的Di-Higgs灵敏度

Improving Di-Higgs Sensitivity at Future Colliders in Hadronic Final States with Machine Learning

Artur Apresyan, Daniel Diaz, Javier Duarte, Sanmay Ganguly, Raghav Kansal, Nan Lu, Cristina Mantilla Suarez, Samadrita Mukherjee, Cristían Peña, Brian Sheldon, Si Xie

摘要:

未来对撞机物理计划的核心目标之一是阐明电弱对称性破缺的起源,包括对希格斯扇区的精确测量。这包括对希格斯玻色子玻色子(H)对产生的详细研究,它可以揭示H自耦合。自从希格斯玻色子玻色子被发现以来,一场大规模的测量希格斯玻色子玻色子性质的运动已经开始,在完成这一计划的过程中出现了许多新的想法。一个这样的想法是使用希格斯玻色子的高度提升和合并的强子衰变($\mathrm{H}\to\mathrm{b}\bar{\mathrm{b}}$,$\mathrm{H}\to\mathrm{W}\mathrm{W}\to\mathrm{q}\bar{\mathrm{q}}\mathrm{q}\bar{\mathrm{q}}$)与机器学习方法来改善信号与背景的区分。在本白皮书中,我们支持使用这些模式来提高未来对撞机物理程序对希格斯玻色子对产生、希格斯自耦合和希格斯矢量玻色子耦合的灵敏度。我们展示了在强子模式下,未来圆形对撞机可能的潜在改进,特别是使用图形神经网络。

英文摘要:

One of the central goals of the physics program at the future colliders is to elucidate the origin of electroweak symmetry breaking, including precision measurements of the Higgs sector. This includes a detailed study of Higgs boson (H) pair production, which can reveal the H self-coupling. Since the discovery of the Higgs boson, a large campaign of measurements of the properties of the Higgs boson has begun and many new ideas have emerged during the completion of this program. One such idea is the use of highly boosted and merged hadronic decays of the Higgs boson ($\mathrm{H}\to\mathrm{b}\bar{\mathrm{b}}$, $\mathrm{H}\to\mathrm{W}\mathrm{W}\to\mathrm{q}\bar{\mathrm{q}}\mathrm{q}\bar{\mathrm{q}}$) with machine learning methods to improve the signal-to-background discrimination. In this white paper, we champion the use of these modes to boost the sensitivity of future collider physics programs to Higgs boson pair production, the Higgs self-coupling, and Higgs-vector boson couplings. We demonstrate the potential improvement possible at the Future Circular Collider in hadron mode, especially with the use of graph neural networks.

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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