High Energy & Particle · Physics & Astronomy
High Energy Physics - Experiment
The newest papers in High Energy Physics - Experiment that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
73 papers, from 6 September 2026 to 5 November 2026: 73 arXiv preprints.
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Newest papers, page 3
Minimal-length kinematics of coherent light and a proposed two-color vacuum chronoscopy
The unification of quantum theory and general relativity suggests the existence of a minimal measurable length, leading to a generalized uncertainty principle (GUP) and an energy-dependent vacuum…Quantum-enhanced Search for Galactic Dark Photons Using Cat-State Cavities
We present a quantum-enhanced search for the Galactic dark-photon background (GDPB), a relativistic and anisotropic vector field that can be produced by dark-matter decay or annihilation in the…A low-cost compact muon detector for millikelvin operation
Ionizing radiation produces correlated errors in superconducting qubits, posing some challenges to quantum error correction.Machine-learning-based neutrino-event generation constrained by experimental measurements
We present a generative surrogate for charged-current \(ν_μ\)-nucleus scattering that can be calibrated to measured differential cross sections without event-level experimental samples.RINO: Renormalization Group Distillation with No Labels
Supervised machine learning in high-energy physics (HEP) requires training on labeled simulations of particle collisions, which are known to be unfaithful to the underlying physics.Using Line Segment Tracking to enable ML-based Track Reconstruction in CMS
High-pileup conditions in CMS during the HL-LHC era make charged-particle tracking increasingly challenging as detector occupancy and combinatorics grow.Local Diagnostics of Learned Event Representations for Anomaly Detection at the HL-LHC
Anomaly detection, the identification of collision events that deviate from Standard Model expectations, is one of the most promising model-agnostic strategies for new-physics searches at the Large…TLS loss in superconducting resonators on epitaxial hBN thin films
The integration of low-loss dielectrics into superconducting circuits is of importance to the functionality of quantum computers and sensors.How to scale your HEP ML models: A recipe for robust architecture comparisons at scale
Much of the recent progress in machine learning domains such as language models has come from scaling laws that predict performance as a function of training effort.Magnus: neutrino oscillation probabilities for any Hermitian Hamiltonian, any number of flavors, and any matter profile
Interpreting neutrino oscillation measurements requires computing the probability that a neutrino born with one flavor is detected with another.Conditional Capacity and Routing in Mixture-of-Experts Particle Transformers
Mixture-of-Experts (MoE) models can increase parameter capacity without proportionally increasing active computation, but it is unclear how this trade-off behaves in particle-physics transformers.Application of sequence learning for predicting radiation damage of the CMS electromagnetic calorimeter
In this paper we use machine learning methods to predict radiation damage in the lead tungstate crystals of the CMS electromagnetic calorimeter at the Large Hadron Collider.How Quantum Is Bottomonium in the Quark-Gluon Plasma?
We study how much quantum structure bottomonium retains during its evolution in the quark-gluon plasma.PINNing the pion: conformal deep learning for $F_π(s)$ and the $(g-2)_μ$ hadronic contribution
Extracting the pion electromagnetic form factor $F_π(s)$ through phenomenological curve-fitting models introduces model dependence, unphysical artefacts, and kinematic inconsistencies.Quantum Information in High-Energy Physics: a top subject
As a work selected for the Frontiers of Science Award, we present in the Proceedings of the ICBS an overview of how the techniques from the field of Quantum Information (QI) can be implemented in…Scaling Collider Event Generation with Residual-Quantized Tokens
Full detector simulation and reconstruction of collider events are projected to become major bottlenecks at the High-Luminosity Large Hadron Collider, motivating the development of fast, ML-based…Searching for BSM Experimental Signatures with Large Lagrangian Models
The search for physics Beyond the Standard Model (BSM) is generally limited not by the supply of theory descriptions but by the lack of discriminating experimental observations.Infrared Subtraction with Artificial Intelligence
We present AI-developed local infrared subtraction, building on projection to Born and EFT matching.Hybrid Neural Simulation-Based Inference for Robust Applications and Limited-Budget Scenarios
We develop two hybrid techniques that approach the performance of neural simulation-based inference (NSBI) analyses while substantially reducing the computational cost of inference and preserving…Fast and Precise Learned Charged-Particle Trajectory Regression at the Large Hadron Collider
We propose a training recipe that treats charged-particle trajectory parameter regression on high-energy physics detector data as a sequence-modeling task.