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A Living Review of Machine Learning for Particle Physics
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Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics. Given the fast pace of this research, we have created a living review with the goal of providing a nearly comprehensive list of citations for those developing and applying these approaches to experimental, phenomenological, or theoretical analyses. As a living document, it will be updated as often as possible to incorporate the latest developments. A list of proper (unchanging) reviews can be found within. Papers are grouped into a small set of topics to be as useful as possible. Suggestions and contributions are most welcome, and we provide instructions for participating.
Forward citations
Cited by 13 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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Learning transferable event representations for charmed baryon physics at BESIII
A Particle Transformer pre-trained on simulated Lambda_c events transfers across 12 decay channels, improving classification and momentum-direction regression over training from scratch in low-statistics regimes.
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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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Transformer-Based Approach to Enhance Positron Tracking Performance in MEG II
A Transformer-based hit classifier improves MEG II positron tracking efficiency and resolution, yielding an expected ~10% gain in μ→eγ sensitivity.
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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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Learning Broken Symmetries with Approximate Invariance
A dual-subnet network with a learned pT-dependent weighting learns broken symmetries faster than unconstrained networks while avoiding the performance ceiling of exactly invariant networks.
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Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network
A 1D multi-channel CNN trained on normalized histograms of simulated mono-jet and mono-Z events can partially classify one- versus two-component dark matter and regress masses, but only in a background-free, model-spe...
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Electroweak phase transition in SMEFT: Gravitational wave and collider complementarity
Strong first-order electroweak phase transitions in dimension-6 SMEFT can be probed by future gravitational wave detectors and by di-Higgs production at the HL/HE-LHC, with correlated sensitivity regions.
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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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Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough
Verification of ML in fundamental physics is essential precisely when models enter statistical modeling, inference, or hypothesis testing, and is bounded by unavoidable inductive bias, sample complexity, and experimen...
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hep-aid: A Python Library for Sample Efficient Parameter Scans in Beyond the Standard Model Phenomenology
hep-aid is a modular Python library that packages active search, neural network, and MCMC parameter scan methods with a Higgs physics software stack, and its demonstrations show sample efficiency gains on test and BSM...
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Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
A perspective article reviewing physics-driven machine learning for inverse problems in QCD, without introducing new data, derivations, or quantitative results.
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