Simulation-based anomaly detection for multileptons at the LHC

被引：5
作者：
Krzyzanska, Katarzyna [1 ]
Nachman, Benjamin [2 ,3 ]
机构：
[1] Princeton Univ, Dept Phys, Princeton, NJ 08544 USA
[2] Lawrence Berkeley Natl Lab, Phys Div, Berkeley, CA 94720 USA
[3] Univ Calif Berkeley, Berkeley Inst Data Sci, Berkeley, CA 94720 USA
来源：
JOURNAL OF HIGH ENERGY PHYSICS | 2023年 / 2023卷 / 01期
关键词：
Multi-Higgs Models; Other Weak Scale BSM Models; PHYSICS DETECTION;
D O I：
10.1007/JHEP01(2023)061
中图分类号：
O412 [相对论、场论]; O572.2 [粒子物理学];
学科分类号：
摘要：
Decays of Higgs boson-like particles into multileptons is a well-motivated process for investigating physics beyond the Standard Model (SM). A unique feature of this final state is the precision with which the SM is known. As a result, simulations are used directly to estimate the background. Current searches consider specific models and typically focus on those with a single free parameter to simplify the analysis and interpretation. In this paper, we explore recent proposals for signal model agnostic searches using machine learning in the multilepton final state. These tools can be used to simultaneously search for many models, some of which have no dedicated search at the Large Hadron Collider. We find that the machine learning methods offer broad coverage across parameter space beyond where current searches are sensitive, with a necessary loss of performance compared to dedicated searches by only about one order of magnitude.
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页数：19
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