High Energy & Particle · Physics & Astronomy
High Energy Physics - Experiment
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73 papers, from 6 September 2026 to 5 November 2026: 73 arXiv preprints.
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Newest papers, page 4
Machine learning for the LHC physics program: a 2025-2026 stocktake
The first sentence of this abstract--and the introduction to these proceedings--was authored by a human, but the bulk of this document was generated by an agentic AI system.Hypothesis-awkward: Property-Based Testing Strategies for Awkward Array
Hypothesis-awkward is a collection of Hypothesis strategies for Awkward Array.Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks
Reconstruction and simulation at a collider experiment are long chains of specialised algorithms, each tuned to a single step.Nonlocal advantage of quantum coherence in tau-lepton pairs from electron--positron collisions
Quantum correlations have recently attracted significant attention in collider physics, with Nonlocal Advantage of Quantum Coherence (NAQC) representing the strongest form of quantum correlation…Leveraging Industrial Foundation Models at the Edge of Particle Physics Detectors via Distillation Learning and Hardware Co-design
Data acquisition (DAQ) systems at future particle physics experiments stand to benefit from the extremes of AI/ML development: large-scale foundation models can enhance the performance of feature…Comprehensive reconstruction of collider events with hypergraph representation learning and graph-conditioned diffusion
In particle collider experiments, event reconstruction is the task of inferring the kinematics of short-lived particles produced in the hard scatter from the stable final states recorded by detectors.Self-Supervised Learning for Robust Resonance Mass Regression in Cascade Decays
Reconstructing the mass of a heavy resonance from its decay products with missing energy is one of the central tasks that directly determine the sensitivity in new physics searches at collider…Similarity Pairing with Energy Mover's Distance for Self-Supervised Pre-Training at the LHC
Many self-supervised methods for training foundation models at the Large Hadron Collider (LHC) rely on data augmentations to encourage the model to embed events into a representation space invariant…ResoSeg: Resonance Tagger using Transformer and Segment Model
Deep learning has been widely applied across many areas of experimental high-energy physics, yet existing models address only event-level classification or object tagging and therefore still require…Fast BIB simulation at a future Muon Collider with generative machine learning
Beam-induced background (BIB) from muon decay products will be an overwhelming and unavoidable background at a future Muon Collider.Inclusive electron-nucleus cross section models from domain adaptation
We apply transfer learning (TL) to construct data-driven models of inclusive electron-nucleus cross sections."Transforming" LHCb: self-supervised maps of heavy-flavour decays
Decays of beauty and charm hadrons provide sensitive probes of physics beyond the standard model, including decays with invisible particles, in which part of the final state leaves no reconstructed…Likelihood-Based Unsupervised Anomaly Detection in CMS Dijet Events
We present an unsupervised search for anomalous dijet events in proton--proton collision data using neural spline flow density estimation.