We demonstrate the power of machine-learned likelihood ratios for resonance searches in a benchmark model featuring a heavy Z' boson. The likelihood ratio is expressed as a function of multivariate detector level observables, but rather than being calculated explicitly as in matrix-element-based approaches, it is learned from a joint likelihood ratio which depends on latent information from simulated samples. We show that bounds drawn using the machine learned likelihood ratio are tighter than those drawn using a likelihood ratio calculated from histograms.
Center for Nuclear Theory, Department of Physics and Astronomy, Stony Brook University（核理论中心，物理与天文学系，石溪大学）；Massachusetts Institute of Technology（麻省理工学院）；Université Paris-Saclay, CNRS/IN2P3, IJCLab（巴黎-萨克雷大学，CNRS/IN2P3，IJCLab）；Institute for Particle Physics Phenomenology（粒子物理学现象研究所）；Department of Physics, Durham University（物理系，杜ham大学）
Multiple testing for signal-agnostic searches of new physics with machine learning
NSF AI Institute for Artificial Intelligence and Fundamental Interactions（NSF人工智能与基础相互作用AI研究所）；MIT Laboratory for Nuclear Science（麻省理工学院核科学实验室）；School of Engineering and Applied Sciences, Harvard University（哈佛大学工程与应用科学学院）；University of Genoa（热那亚大学）；INFN, Sezione di Genova（意大利核物理研究所热那亚分所）
本文研究利用多重检验策略增强机器学习中的信号不可知搜索，通过结合不同超参数的检验并聚合 p 值或统计量，在避免模型偏差的同时实现与最佳检验相当且对异常响应更均匀的性能。
On Focusing Statistical Power for Searches and Measurements in Particle Physics
Carnegie Mellon University（卡内基梅隆大学）；University of California, Irvine（加州大学尔湾分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Georgia Institute of Technology（佐治亚理工学院）；Federal University of São Carlos（圣卡洛斯联邦大学）
