High-dimensional anomaly detection with radiative return in e+e− collisions

Journal of High Energy Physics

Jerry Lai¹  Inês Ochoa²  Julia Gonski³  Benjamin Nachman⁴ 

[1] Department of Electrical Engineering and Computer Sciences, University of California;Laboratory of Instrumentation and Experimental Particle Physics;Nevis Laboratories, Columbia University;Physics Division, Lawrence Berkeley National Laboratory;

关键词: Beyond Standard Model;    e +-e − Experiments;    Particle and Resonance Production;   

DOI  :  10.1007/JHEP04(2022)156

来源: DOAJ

【 摘 要 】

Abstract Experiments at a future e + e − collider will be able to search for new particles with masses below the nominal centre-of-mass energy by analyzing collisions with initial-state radiation (radiative return). We show that machine learning methods that use imperfect or missing training labels can achieve sensitivity to generic new particle production in radiative return events. In addition to presenting an application of the classification without labels (CWoLa) search method in e + e − collisions, our study combines weak supervision with variable-dimensional information by deploying a deep sets neural network architecture. We have also investigated some of the experimental aspects of anomaly detection in radiative return events and discuss these in the context of future detector design.

【 授权许可】

Unknown