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Machine Learning for Anomaly Detection in Particle Physics
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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. Recent advances in Machine Learning for anomaly detection have encouraged the utilization of such techniques on particle physics problems. This review article provides an overview of the state-of-the-art techniques for anomaly detection in particle physics using machine learning. We discuss the challenges associated with anomaly detection in large and complex data sets, such as those produced by high-energy particle colliders, and highlight some of the successful applications of anomaly detection in particle physics experiments.
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Cited by 14 Pith papers
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Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders
Simplex demixing recovers T mutually irreducible jet-flavor topics from M mixed samples via the (T−1)-simplex geometry of a multi-category classifier, demonstrated on Pythia dijets.
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Learning Standard Model structure from LHC data with Riemannian flow matching
ShellFlow, a Riemannian flow-matching transformer fed only on-shell and invariant-mass priors and ~8×10^8 recorded ATLAS events, reproduces the SM's dilepton resonances, Weinberg angle, and top/W mass peaks in a singl...
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Enhancing anomaly detection with topology-aware autoencoders
Autoencoders with latent spaces shaped like S^2, S^2×S^2, or RP^2, matched to the phase-space topology of the background, reduce spurious reconstruction errors and give a small but consistent anomaly-detection gain ov...
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Explicit or Implicit? Encoding Physics at the Precision Frontier
On three precision classification tasks — reweighting-based unfolding, likelihood-ratio estimation, and weakly supervised anomaly detection — a Lorentz-equivariant transformer and a pretrained foundation model perform...
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Look everywhere effects in anomaly detection
Weakly supervised anomaly detectors that train and test on the same data produce badly miscalibrated p-values; independent test sets are calibrated but insensitive, while k-fold cross-validation is a workable middle ground.
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Graph theory inspired anomaly detection at the LHC
Sparse globally rigid graph representations of jets, combined with roughly 30 reclustered subjets, improve graph autoencoder anomaly detection on the LHC Olympics benchmark.
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Search for new physics in final states with semi-visible jets or anomalous signatures using the ATLAS detector
ATLAS finds no sign of semi-visible jets from Z' decays and excludes Z' masses from 2000 to 3200 GeV for invisible fractions between 0.2 and 0.37.
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Factorization for Collider Dataspace Correlators
Derives a factorization theorem and NLL resummation for SEMD-based event correlators, predicting a universal ledge in quark-jet non-Gaussianity.
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A Step Toward Interpretability: Smearing the Likelihood
Smearing the likelihood over an energy metric reveals the physical scales used by a jet classifier, and the needed smearing radius follows a power-law scaling with dataset size.
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Automatizing the search for mass resonances using BumpNet
One trained convolutional network predicts bump significance across mass histograms of different sizes and backgrounds, approaching the accuracy of the ideal likelihood-ratio test.
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Weakly supervised machine learning for model-agnostic searches of new phenomena in the $\gamma$-ray sky
Weakly supervised classifiers trained on background-versus-mixture samples can identify anomalous gamma-ray sources without labeled signal templates, approaching supervised performance in controlled benchmarks.
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Shedding Light on Dark Matter at the LHC with Machine Learning
A machine-learned LHC analysis projects 5-sigma sensitivity to singlino-dominated NMSSM dark matter via radiative higgsino decays to photons, covering higgsino masses up to 225 GeV.
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Generator Based Inference (GBI)
Generator Based Inference uses data-derived background generators to turn resonant anomaly detection into parameter estimation, reaching 0.1 sigma signal sensitivity on the LHCO benchmark.
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Quantum similarity learning for anomaly detection
A hybrid Transformer-quantum circuit similarity-learning network reaches AUC 96.1% on simulated di-Higgs anomaly detection, slightly above a classical baseline, with clustering mitigating shot noise.
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