Machine learning in the search for new fundamental physics
Machine learning in the search for new fundamental physics
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DOI：
10.1038/s42254-022-00455-1
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发表时间：
2021-12
影响因子：
38.5
通讯作者：
G. Karagiorgi;G. Kasieczka;S. Kravitz;B. Nachman;D. Shih
中科院分区：
文献类型：
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作者：
G. Karagiorgi;G. Kasieczka;S. Kravitz;B. Nachman;D. Shih
令人信服的实验证据表明，在粒子物理学的既定和经过测试的标准模型之外，存在着新的物理学。目前和即将进行的各种实验都在寻找新物理学的特征。尽管在这些实验中测试了各种各样的方法和理论模型，但它们都有一个共同点，即它们产生的复杂数据量非常大。这一数据挑战需要强大的统计方法。机器学习在高能粒子物理领域的应用已经有十多年了，但深度学习在2010年代初的兴起，在研究范围和雄心方面产生了质的转变。这些现代机器学习的发展是本评论的重点，它讨论了在陆地高能物理实验背景下的新物理搜索的方法和应用，包括大型强子对撞机，罕见事件搜索和中微子实验。
Compelling experimental evidence suggests the existence of new physics beyond the well-established and tested standard model of particle physics. Various current and upcoming experiments are searching for signatures of new physics. Despite the variety of approaches and theoretical models tested in these experiments, what they all have in common is the very large volume of complex data that they produce. This data challenge calls for powerful statistical methods. Machine learning has been in use in high-energy particle physics for well over a decade, but the rise of deep learning in the early 2010s has yielded a qualitative shift in terms of the scope and ambition of research. These modern machine learning developments are the focus of the present Review, which discusses methods and applications for new physics searches in the context of terrestrial high-energy physics experiments, including the Large Hadron Collider, rare event searches and neutrino experiments.