Abstract
Abstract
Complete anomaly detection strategies that are both signal sensitive and compatible with background estimation have largely focused on resonant signals. Non-resonant new physics scenarios are relatively under-explored and may arise from off-shell effects or final states with significant missing energy. In this paper, we extend a class of weakly supervised anomaly detection strategies developed for resonant physics to the non-resonant case. Machine learning models are trained to reweight, generate, or morph the background, extrapolated from a control region. A classifier is then trained in a signal region to distinguish the estimated background from the data. The new methods are demonstrated using a semi-visible jet signature as a benchmark signal model, and are shown to automatically identify the anomalous events without specifying the signal ahead of time.
Publisher
Springer Science and Business Media LLC
Reference134 articles.
1. G. Kasieczka et al., The LHC olympics 2020 a community challenge for anomaly detection in high energy physics, Rept. Prog. Phys. 84 (2021) 124201 [arXiv:2101.08320] [INSPIRE].
2. T. Aarrestad et al., The dark machines anomaly score challenge: benchmark data and model independent event classification for the Large Hadron Collider, SciPost Phys. 12 (2022) 043 [arXiv:2105.14027] [INSPIRE].
3. G. Karagiorgi et al., Machine learning in the search for new fundamental physics, arXiv:2112.03769 [INSPIRE].
4. M. Feickert and B. Nachman, A living review of machine learning for particle physics, arXiv:2102.02770 [INSPIRE].
5. J.H. Collins, K. Howe and B. Nachman, Anomaly detection for resonant new physics with machine learning, Phys. Rev. Lett. 121 (2018) 241803 [arXiv:1805.02664] [INSPIRE].
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献