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.
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Acknowledgments
We thank Timothy Cohen, Sascha Diefenbacher, Laura Jeanty, Simon Knapen, and Christiane Scherb for the useful discussions. In particular, we thank Christiane Scherb for the help on Hidden Valley simulations, and Laura Jeanty for providing detailed feedback on the manuscript. KB would like to thank Javier Montejo Berlingen for providing training and inspirations prior to this project. KB is supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics program under Award Number DE-SC0020244. KB’s visit to LBNL for this collaboration is supported by the US ATLAS Center program. BN and RM are supported by the U.S. Department of Energy (DOE), Office of Science under contract DE-AC02-05CH11231 and Grant No. 63038 from the John Templeton Foundation. RM is additionally supported by Grant No. DGE 2146752 from the National Science Foundation Graduate Research Fellowship Program. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0021099.
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Bai, K., Mastandrea, R. & Nachman, B. Non-resonant anomaly detection with background extrapolation. J. High Energ. Phys. 2024, 59 (2024). https://doi.org/10.1007/JHEP04(2024)059
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DOI: https://doi.org/10.1007/JHEP04(2024)059