Condensates and anomaly cascade in vector-like theories [PDF]
Journal of High Energy Physics, 2021 We study the bilinear and higher-order fermion condensates in 4-dimensional SU(N) gauge theories with a single Dirac fermion in a general representation.
Mohamed M. Anber
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Challenges for unsupervised anomaly detection in particle physics [PDF]
Journal of High Energy Physics, 2022 Anomaly detection relies on designing a score to determine whether a particular event is uncharacteristic of a given background distribution. One way to define a score is to use autoencoders, which rely on the ability to reconstruct certain types of data
Katherine Fraser +4 more
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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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Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows
Frontiers in Big Data, 2022 We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a working example, we consider the DarkMachines challenge dataset. We show how
Pratik Jawahar +9 more
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A Qualitative Strategy for Fusion of Physics into Empirical Models for Process Anomaly Detection
Energies, 2022 To facilitate the automated online monitoring of power plants, a systematic and qualitative strategy for anomaly detection is presented. This strategy is essential to provide credible reasoning on why and when an empirical versus hybrid (i.e., physics ...
Ahmad Y. Al Rashdan +9 more
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Finding new physics without learning about it: anomaly detection as a tool for searches at colliders
European Physical Journal C: Particles and Fields, 2021 In this paper we propose a new strategy, based on anomaly detection methods, to search for new physics phenomena at colliders independently of the details of such new events.
M. Crispim Romão +2 more
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Quasi anomalous knowledge: searching for new physics with embedded knowledge
Journal of High Energy Physics, 2021 Discoveries of new phenomena often involve a dedicated search for a hypothetical physics signature. Recently, novel deep learning techniques have emerged for anomaly detection in the absence of a signal prior.
Sang Eon Park +4 more
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Interpreting electroweak precision data including the W-mass CDF anomaly
Journal of High Energy Physics, 2022 We perform a global fit of electroweak data, finding that the anomaly in the W mass claimed by the CDF collaboration can be reproduced as a universal new-physics correction to the T parameter or |H † D μ H|2 operator.
Alessandro Strumia
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Quantum anomaly detection for collider physics
Journal of High Energy Physics, 2023 We explore the use of Quantum Machine Learning (QML) for anomaly detection at the Large Hadron Collider (LHC). In particular, we explore a semi-supervised approach in the four-lepton final state where simulations are reliable enough for a direct ...
Sulaiman Alvi +2 more
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Autoencoders for unsupervised anomaly detection in high energy physics
Journal of High Energy Physics, 2021 Autoencoders are widely used in machine learning applications, in particular for anomaly detection. Hence, they have been introduced in high energy physics as a promising tool for model-independent new physics searches.
Thorben Finke +4 more
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