Particle physics theoretical and experimental studies

Últimos artículos

• Searching for BSM Experimental Signatures with Large Lagrangian Models

  Ibrahim Elsharkawy, Victoria Knapp-Perez, Wahid Bhimji, Aishik Ghosh · 1 de octubre de 2026

  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. A case in point is dark matter, where the overwhelming gravitational evidence only goes so far in distinguishing between m…

• Infrared Subtraction with Artificial Intelligence

  Wenjie He, Xiaohui Liu, Yandong Liu, Zhan Wang · 30 de septiembre de 2026

  We present AI-developed local infrared subtraction, building on projection to Born and EFT matching. The framework separates an integrable radiation term from a finite contribution at Born kinematics, referred to as the Born contact. The contact is determined using the EFT singular distribution in a…

• LaMET-Agent: An Agent Framework for Large-Momentum Effective Theory Analysis

  Jinchen He, Xiangyu Jiang, Fei Yao, Dian-Jun Zhao · 29 de septiembre de 2026

  Large-momentum effective theory (LaMET) provides a first-principles framework for computing the $x$ dependence of light-cone parton distributions from lattice QCD. Over the past decade, theoretical and numerical advances have established a mature multi-stage workflow for systematic calculation of pa…

• Physics-Informed Self-Supervised Learning for Joint Wire Calibration and Interaction Position Reconstruction in Multi-Wire Parallel Plate Avalanche Counters

  Antoine Lemasson, Maurycy Rejmund · 25 de septiembre de 2026

  Scientific instruments require accurate calibration to convert detector signals into reliable physical observables. Conventional calibration procedures typically rely on dedicated calibration measurements, analytical response models or labelled reference data, limiting their ability to adapt to chan…

• The shape of quark flavors

  Shinsuke Kawai, Nobuchika Okada · 22 de septiembre de 2026

  We construct the Yukawa couplings of the quark sector as overlap integrals of Gaussian wave functions in extra spatial dimensions. Assuming that the wave function of each chiral fermion is localised at a point in the extra dimensions, while that of the Higgs doublet is flat, the Yukawa matrix elemen…

• Machine Learning for Invisible Dark Boson Searches at the Electron-Ion Collider

  Rojae Mighty, Ankush Reddy Kanuganti · 22 de septiembre de 2026

  We investigate whether adding $t$, the positive magnitude of the squared nuclear four-momentum transfer, enables boosted decision trees (BDTs) to improve invisible-dark-boson selection relative to optimized rectangular cuts at the Electron-Ion Collider. We model coherent exclusive scalar and vector …

• Automated Physics-Informed Neural-Networks-Based Calibration of Highly Segmented Silicon Telescopes

  M. Rejmund, A. Lemasson, P. Morfouace, D. Ramos, J. Taieb, J. D. Frankland · 21 de septiembre de 2026

  Transfer and multi-nucleon transfer reactions are essential tools for probing nuclear structure and reaction dynamics, requiring precise determination of the identity, energy, and emission angles of reaction products. The increasing granularity of modern silicon telescope arrays enhances experimenta…

• Fast BIB simulation at a future Muon Collider with generative machine learning

  Radha Mastandrea, Shiyu Peng, Benjamin Rosser, Matt LeBlanc · 14 de septiembre de 2026

  Beam-induced background (BIB) from muon decay products will be an overwhelming and unavoidable background at a future Muon Collider. In order to develop robust event reconstruction algorithms, we need large amounts of accurate BIB simulation to test on. BIB simulation is currently compute-limited: t…

• Learning the Geometry of Collider Events with Metric-Aware Deep Sets

  Lauren Hay, Rishabh Jain, Matt LeBlanc, Jennifer Roloff · 14 de septiembre de 2026

  Optimal transport gives structured data a geometry, but exact evaluation is costly in large pairwise analyses that exploit relationships among distances. Learned surrogates are faster, but need not preserve this metric structure. We develop a Deep Sets surrogate for OT between variable-size weighted…

• ResoSeg: Resonance Tagger using Transformer and Segment Model

  Chunkai Li, Junhao Yin, Ke Li, Jingde Chen · 14 de septiembre de 2026

  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 reconstruction algorithms tailored to each decay channel. We present the first application of segment…

• Searching for New Physics with Reinforcement Learning

  Jacky Kumar, Marianne Bouchard, David London · 10 de septiembre de 2026

  Finding new physics (NP) is the most important problem in particle physics today. Studying ``anomalies'', i.e., measurements of low-energy observables whose values disagree with the predictions of the Standard Model (SM), is a powerful search strategy. The SM Effective Field Theory (SMEFT) provides …

• "Transforming" LHCb: self-supervised maps of heavy-flavour decays

  Marko Stamenkovic, Greg Landsberg · 10 de septiembre de 2026

  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 detector signature. The large heavy-flavour data samples recorded by the LHCb experiment at the CERN L…

• Generative Nested Sampling of Atomistic Thermodynamic Landscapes

  Alessandro Coretti, Nico Unglert, Sebastian Falkner, Georg K. H. Madsen, Christoph Dellago · 4 de septiembre de 2026

  Nested sampling (NS) resolves the thermodynamics of an atomistic system from a single simulation, but its practical reach is limited by the Markov-chain updates needed to decorrelate walkers within each likelihood-constrained ensemble. Flow-based NS has removed this bottleneck for gravitational-wave…

• Hadronic Mono-Z Dark Matter Sensitivity with Flow Matching on CMS Open Data

  Hitesh Rasineni (VIT-AP University, Amaravati, India), Bhavishya Chebrolu (Mohan Babu University, Tirupati, India) · 4 de septiembre de 2026

  We present a projected sensitivity study for hadronic mono-$Z$ dark-matter production using CMS Run~2015D HTMHT open data corresponding to 2.256382381~\invfb, from which 1{,}439{,}523 events satisfy the hadronic mono-$Z$ selection. Backgrounds are modelled with a conditional flow-matching continuous…

• Panda Diplomacy: Foundation Model Pre-training across Particle Imaging Detectors for High Energy and Nuclear Physics

  Samuel Young, C\'esar Jes\'us-Valls, Kazuhiro Terao · 2 de septiembre de 2026

  Foundation models are increasingly being pursued in particle and nuclear physics, but existing approaches remain strongly tied to individual experiments through detector-specific architectures or pre-training objectives, limiting their reuse across sensing modalities. We show that a point cloud self…

• Learning Transverse Momentum Distributions from Raw Scattering Events via Conditional Diffusion

  Jitao Xu, Christopher Cocuzza, Kevin Braga, Daniel Lersch, Nobuo Sato, Yaohang Li · 28 de agosto de 2026

  Extracting transverse momentum dependent parton distribution functions (TMD PDFs) from semi-inclusive deep inelastic scattering (SIDIS) data is a central goal of the nucleon structure program at Jefferson Lab and the future Electron-Ion Collider. Traditional extraction methods rely on parameterized …

• Finding and using interpretable latents in a neutrino foundation model with sparse autoencoders

  Rapha\"el Bonnet-Guerrini, Johann Ioannou-Nikolaides, Inar Timiryasov, Vincenzo Piuri · 27 de agosto de 2026

  We present a first application of sparse-autoencoder-based mechanistic interpretability to particle physics. Studying a neutrino foundation model pretrained on IceCube data and fine-tuned for direction reconstruction, we identify a validated atlas of physical concepts in the model representation, us…

• S-matrix informed neural networks for amplitude analysis

  Wyatt A. Smith, Arkaitz Rodas, Marius D. Thomas, C\'esar Fern\'andez-Ram\'irez, Giorgio Foti, Lin Qiu, Adam P. Szczepaniak, Alessandro Pilloni · 26 de agosto de 2026

  Reconstructing scattering amplitudes from finite, noisy, and mutually inconsistent measurements is an ill-posed inverse problem common to many reactions relevant to particle physics. We introduce S-matrix informed neural networks (SINNs), and demonstrate their ability to learn scattering amplitudes …

• Symbolic Classification-Enabled LHC Limits Online BSM Global Fits

  Shehu AbdusSalam · 26 de agosto de 2026

  Global fits of Beyond the Standard Model (BSM) physics often involve a two-way interplay between theory and experiment. Theoretical models provide guidance for experimental searches, while experimental results, in turn, constrain theoretical frameworks. A crucial aspect of this feedback loop is the …

• Functional anatomy of Pythia-Herwig differences with Kolmogorov-Arnold networks

  Arghya Chattopadhyay · 18 de agosto de 2026

  Differences between high-energy event generators can arise at several stages of the collision simulation, from the hard scattering through parton showering and hadronization to the final event. These differences are usually summarized using observable distributions or global classifier scores. While…

• Pairton: Iterative Reconstruction of Short-Lived Particles

  Andreas Hermansen, Chris Scheulen, Tobias Golling · 17 de agosto de 2026

  We present Pairton, an iterative framework for reconstructing short-lived particles in high-energy collision events. By formulating particle reconstruction as a masked prediction process over graph structures, Pairton learns conditional distributions consistent with a factorised decomposition of dec…

• Unknown Unknowns: Model Misspecification in Machine Learning for Physics

  Juan Cruz-Martinez, Carolina Cuesta-Lazaro, Alexander Held, Michael Kagan · 17 de agosto de 2026

  Machine learning is now a central tool for solving inverse problems in particle physics and astronomy. Models are trained on simulation and deployed on real data, raising the question not just of whether they fit, but of whether they are wrong in ways we did not anticipate: the unknown unknowns. Thi…

• Contrastive Learning for Interpretable Anomaly Detection at Collider Experiments

  Haoyi Jia, Sagar Addepalli, Julia Gonski · 17 de agosto de 2026

  Generic event-level anomaly detection for collider physics has two recurring problems: anomaly scores are hard to interpret, and they correlate strongly with energy scale and object multiplicity. We present Organized Representation via Contrastive learning for Anomaly detection (ORCA), a two-stage f…

• Fine-tuned Normalizing Flows for ALICE Zero Degree Calorimeter Fast Simulation

  Emilia Majerz, Jacek Otwinowski, Witold Dzwinel, Jacek Kitowski · 14 de agosto de 2026

  Simulating the ALICE Zero Degree Calorimeter (ZDC) neutron detector responses at the LHC is computationally expensive, requiring complex Monte Carlo chains. We develop a generative surrogate, focusing on Normalizing Flows (NFs). Through transfer learning, we pre-train on the full imbalanced dataset …

• Tevatron Meets Megatron: Expert-Parallel LLM Reranker Training on an Academic Budget

  Zhichao Xu, Xueguang Ma, Shengyao Zhuang, Luyu Gao, Wenqian Ye, Yu Wang, Jamie Callan, Jimmy Lin · 4 de agosto de 2026

  Modern reranking recipes---billion-scale cross-encoders, mixture-of-experts (MoE) backbones, and distillation against strong teachers---have outpaced the training infrastructure available to most academic groups. Existing Tevatron reranker training relies on the Hugging Face Trainer with DeepSpeed o…