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
doaj +8 more sources
Higgs physics confronts the MW anomaly [PDF]
Physics Letters B, 2022 The recent high-precision measurement of the W mass by the CDF collaboration is in sharp tension with the Standard Model prediction as obtained by the electroweak fit. If confirmed, this finding can only be explained in terms of new physics effects.
Luca Di Luzio +2 more
doaj +5 more sources
Quantum anomaly detection for collider physics [PDF]
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
doaj +7 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
doaj +4 more sources
Unravelling physics beyond the standard model with classical and quantum anomaly detection [PDF]
Machine Learning: Science and Technology, 2023 Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadron Collider (LHC).
Julian Schuhmacher +9 more
doaj +2 more sources
Finding new physics without learning about it: anomaly detection as a tool for searches at colliders [PDF]
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
doaj +2 more sources
Autoencoders for unsupervised anomaly detection in high energy physics [PDF]
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
doaj +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
openalex +3 more sources
Axion and neutrino physics from anomaly cancellation [PDF]
, 2001 It has been recently shown that the requirement of anomaly cancellation in a (non-supersymmetric) six-dimensional version of the standard model fixes the field content to the known three generations.
A. Delgado +27 more
core +4 more sources
Rare and Different: Anomaly Scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC [PDF]
SciPost Physics, 2022 We propose a new method to define anomaly scores and apply this to particle physics collider events. Anomalies can be either rare, meaning that these events are a minority in the normal dataset, or different, meaning they have values that are not ...
Sascha Caron, Luc Hendriks, Rob Verheyen
doaj +2 more sources