Results 11 to 20 of about 1,452,791 (344)
Event-Based Anomaly Detection for Searches for New Physics [PDF]
Universe, 2022 This paper discusses model-agnostic searches for new physics at the Large Hadron Collider using anomaly-detection techniques for the identification of event signatures that deviate from the Standard Model (SM).
Sergei Chekanov, Walter Hopkins
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Self-supervised anomaly detection for new physics [PDF]
Physical Review D, 2022 We investigate a method of model-agnostic anomaly detection through studying jets, collimated sprays of particles produced in high-energy collisions. We train a transformer neural network to encode simulated QCD"event space"dijets into a low-dimensional ...
B. Dillon, R. Mastandrea, B. Nachman
semanticscholar +5 more sources
Deep Set Auto Encoders for Anomaly Detection in Particle Physics [PDF]
SciPost Physics, 2022 There is an increased interest in model agnostic search strategies for physics beyond the standard model at the Large Hadron Collider. We introduce a Deep Set Variational Autoencoder and present results on the Dark Machines Anomaly Score Challenge. We
Bryan Ostdiek
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Anomaly detection in high-energy physics using a quantum autoencoder [PDF]
Physical Review D, 2021 The lack of evidence for new interactions and particles at the Large Hadron Collider has motivated the high-energy physics community to explore model-agnostic data-analysis approaches to search for new physics.
Vishal S. Ngairangbam +2 more
openalex +2 more sources
Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward [PDF]
IEEE Access, 2023 Advancements in digital automation for smart grids have led to the installation of measurement devices like phasor measurement units (PMUs), micro-PMUs ( $\mu $ -PMUs), and smart meters.
Mehdi Jabbari Zideh +2 more
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Quantum Generative Adversarial Networks For Anomaly Detection In High Energy Physics [PDF]
International Conference on Quantum Computing and Engineering, 2023 The standard model (SM) of particle physics represents a theoretical paradigm for the description of the fundamental forces of nature. Despite its broad applicability, the SM does not enable the description of all physically possible events.
Elie Bermot +6 more
semanticscholar +1 more source
The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics [PDF]
Reports on progress in physics. Physical Society, 2021 A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this
G. Kasieczka +46 more
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Anomaly detection in collider physics via factorized observables [PDF]
Physical Review D, 2023 To maximize the discovery potential of high-energy colliders, experimental searches should be sensitive to unforeseen new physics scenarios. This goal has motivated the use of machine learning for unsupervised anomaly detection.
E. Metodiev, Jesse Thaler, R. Wynne
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Nuclear Physics B, 2022 Anomaly detection algorithms have been proved to be useful in the search of new physics beyond the Standard Model. However, a prerequisite for using an anomaly detection algorithm is that the signal to be sought is indeed anomalous.
Ji-Chong Yang, Yu-Chen Guo, Li-Hua Cai
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New physics interpretation of $W$-boson mass anomaly [PDF]
, 2022 The CDF collaboration has recently reported an updated result on the $W$-boson mass measurement, showing a $7\sigma$ deviation from the standard model prediction. The discrepancy may indicate new contributions to the Fermi coupling constant.
Motoi Endo, S. Mishima
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