Results 21 to 30 of about 1,594,650 (367)
Machine Learning for Anomaly Detection in Particle Physics [PDF]
Reviews in Physics, 2023 The detection of out-of-distribution data points is a common task in particle physics. It is used for monitoring complex particle detectors or for identifying rare and unexpected events that may be indicative of new phenomena or physics beyond the Standard Model.
Vasilis Belis +2 more
semanticscholar +4 more sources
Dai-Freed anomalies in particle physics [PDF]
Journal of High Energy Physics, 2019 Abstract Anomalies can be elegantly analyzed by means of the Dai-Freed theorem. In this framework it is natural to consider a refinement of traditional anomaly cancellation conditions, which sometimes leads to nontrivial extra constraints in the fermion spectrum.
Iñaki García‐Etxebarria +1 more
openalex +10 more sources
Old and new physics interpretations of the NuTeV anomaly [PDF]
Journal of High Energy Physics, 2002 We discuss whether the NuTeV anomaly can be explained, compatibly with all other data, by QCD effects (maybe, if the strange sea is asymmetric, or there is a tiny violation of isospin), new physics in propagators or couplings of the vector bosons (not really), loops of supersymmetric particles (no), dimension six operators (yes, for one specific SU(2 ...
Sacha Davidson +4 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, Raymond Wynne
semanticscholar +1 more source
Creating simple, interpretable anomaly detectors for new physics in jet substructure [PDF]
Physical Review D, 2022 Anomaly detection with convolutional autoencoders is a popular method to search for new physics in a model-agnostic manner. These techniques are powerful, but they are still a"black box,"since we do not know what high-level physical observables determine
Layne Bradshaw +2 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
semanticscholar +1 more source
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
semanticscholar +1 more source
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
doaj +1 more source
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
semanticscholar +1 more source
Exploring B-physics anomalies at colliders [PDF]
Proceedings of The European Physical Society Conference on High Energy Physics — PoS(EPS-HEP2021), 2022 4 pages, 2 figures; contribution to the proceedings of the EPS-HEP 2021 ...
Alda Gallo, Jorge +2 more
openaire +3 more sources