Searches for new physics in collision events using a statistical technique for anomaly detection

Chekanov, Sergei

doi:10.21468/SciPostPhysProc.10.015

Searches for new physics in collision events using a statistical technique for anomaly detection

Journal Article · Wed Aug 10 00:00:00 EDT 2022 · SciPost Physics Proceedings

DOI:https://doi.org/10.21468/SciPostPhysProc.10.015· OSTI ID:1880705

Chekanov, Sergei ^[1]

Argonne National Laboratory

This paper discusses a statistical anomaly-detection method for model-independent searches for new physics in collision events produced at the Large Hadron Collider (LHC). The method requires calculations of Z-scores for a large number of Lorenz-invariant variables to identify events that deviate from those expected for the Standard Model (SM).

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1880705

Journal Information:: SciPost Physics Proceedings, Journal Name: SciPost Physics Proceedings Journal Issue: 10; ISSN 2666-4003

Publisher:: Stichting SciPostCopyright Statement

Country of Publication:: Netherlands

Language:: English

References (3)

A brief introduction to PYTHIA 8.1 Sjöstrand, Torbjörn; Mrenna, Stephen; Skands, Peter Computer Physics Communications, Vol. 178, Issue 11 https://doi.org/10.1016/j.cpc.2008.01.036	journal	June 2008
Imaging particle collision data for event classification using machine learning Chekanov, S. V. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 931 https://doi.org/10.1016/j.nima.2019.04.031	journal	July 2019
Machine Learning Using Rapidity-Mass Matrices for Event Classification Problems in HEP Chekanov, Sergei V. Universe, Vol. 7, Issue 1 https://doi.org/10.3390/universe7010019	journal	January 2021

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Searches for new physics in collision events using a statistical technique for anomaly detection

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