Recent Publications

This is an automatically compiled list of papers which have been added to the living review that were made public within the previous 4 months at the time of updating. This is not an exhaustive list of released papers, and is only able to find those which have both year and month data provided in the bib reference.

May 2024

• Improving Neutrino Energy Reconstruction with Machine Learning
• Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production
• Boosting probes of CP violation in the top Yukawa coupling with Deep Learning
• The Phase Space Distance Between Collider Events
• Generating configurations of increasing lattice size with machine learning and the inverse renormalization group
• Deep Learning Calabi-Yau four folds with hybrid and recurrent neural network architectures
• Equivariant neural networks for robust \(\textit{CP}\) observables
• Dark sector searches with the CMS experiment
• Test of light-lepton universality in \(\tau\) decays with the Belle II experiment
• Deep learning lattice gauge theories
• Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics
• Accelerating Resonance Searches via Signature-Oriented Pre-training
• Pole structure of \(P_\psi^N(4312)^+\) via machine learning and uniformized S-matrix
• Building imaginary-time thermal filed theory with artificial neural networks
• Advancing Set-Conditional Set Generation: Graph Diffusion for Fast Simulation of Reconstructed Particles
• Learning BPS Spectra and the Gap Conjecture
• Incorporating Physical Priors into Weakly-Supervised Anomaly Detection
• Preheating with deep learning
• Leptoquark Searches at TeV Scale Using Neural Networks at Hadron Collider
• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• Flavor dependent Critical endpoint from holographic QCD through machine learning
• Searches for the BSM scenarios at the LHC using decision tree based machine learning algorithms: A comparative study and review of Random Forest, Adaboost, XGboost and LightGBM frameworks
• Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulation
• Statistical divergences in high-dimensional hypothesis testing and a modern technique for estimating them
• RELICS: a REactor neutrino LIquid xenon Coherent elastic Scattering experiment
• Folded context condensation in Path Integral formalism for infinite context transformers
• ATLAS searches for additional scalars and exotic Higgs boson decays with the LHC Run 2 dataset
• HEP ML Lab: An end-to-end framework for applying machine learning into phenomenology studies
• Measurement of atmospheric neutrino oscillation parameters using convolutional neural networks with 9.3 years of data in IceCube DeepCore
• Search for new resonances decaying to pairs of merged diphotons in proton-proton collisions at \(\sqrt{s}\)

April 2024

• Unifying Simulation and Inference with Normalizing Flows
• Exploring Transport Properties of Quark-Gluon Plasma with a Machine-Learning assisted Holographic Approach
• The Landscape of Unfolding with Machine Learning
• Bayesian Active Search on Parameter Space: a 95 GeV Spin-0 Resonance in the (\(B-L\))SSM
• Flow-based Nonperturbative Simulation of First-order Phase Transitions
• Classical integrability in the presence of a cosmological constant: analytic and machine learning results
• BUFF: Boosted Decision Tree based Ultra-Fast Flow matching
• OmniLearn: A Method to Simultaneously Facilitate All Jet Physics Tasks
• Robust Independent Validation of Experiment and Theory: Rivet version 4 release note
• Foundations of automatic feature extraction at LHC--point clouds and graphs
• Using analytic models to describe effective PDFs
• Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion
• Search for a resonance decaying into a scalar particle and a Higgs boson in the final state with two bottom quarks and two photons in proton-proton collisions at a center of mass energy of 13 TeV with the ATLAS detector
• On Machine Learning Complete Intersection Calabi-Yau 3-folds
• Magnetic Monopole Phenomenology at Future Hadron Colliders
• Strategies for Machine Learning Applied to Noisy HEP Datasets: Modular Solid State Detectors from SuperCDMS
• A machine learning-based study of open-charm hadrons in proton-proton collisions at the Large Hadron Collider
• Xiwu: A Basis Flexible and Learnable LLM for High Energy Physics
• Search for Higgs Boson Pair Production with One Associated Vector Boson in Proton-Proton Collisions at \(\sqrt{s}\)
• Complete Optimal Non-Resonant Anomaly Detection
• Determination of \(K^0_S\) Fragmentation Functions including BESIII Measurements and using Neural Networks
• Trials Factor for Semi-Supervised NN Classifiers in Searches for Narrow Resonances at the LHC
• Physics Event Classification Using Large Language Models
• Boosted four-top production at the LHC : a window to Randall-Sundrum or extended color symmetry
• Helicity-dependent parton distribution functions at next-to-next-to-leading order accuracy from inclusive and semi-inclusive deep-inelastic scattering data
• Deep learning for flow observables in high energy heavy-ion collisions
• Probing intractable beyond-standard-model parameter spaces armed with Machine Learning
• Machine Learning in High Energy Physics: A review of heavy-flavor jet tagging at the LHC

March 2024

• Meson mass and width: Deep learning approach
• Differentiable nuclear deexcitation simulation for low energy neutrino physics
• Probing Heavy Charged Higgs Boson Using Multivariate Technique at Gamma-Gamma Collider
• Feynman Diagrams as Computational Graphs
• A Deep Learning Framework for Disentangling Triangle Singularity and Pole-Based Enhancements
• One flow to correct them all: improving simulations in high-energy physics with a single normalising flow and a switch
• Particle identification with machine learning from incomplete data in the ALICE experiment
• Deep Probabilistic Direction Prediction in 3D with Applications to Directional Dark Matter Detectors
• Predicting Feynman periods in \(\phi^4\)-theory
• CaloPointFlow II Generating Calorimeter Showers as Point Clouds
• Gravitational Duals from Equations of State
• Normalizing Flows for Domain Adaptation when Identifying \(\Lambda\) Hyperon Events
• Improving \(\Lambda\) Signal Extraction with Domain Adaptation via Normalizing Flows
• Quantum chaos in the sparse SYK model
• ML-based Calibration and Control of the GlueX Central Drift Chamber
• Deep Generative Models for Ultra-High Granularity Particle Physics Detector Simulation: A Voyage From Emulation to Extrapolation
• Unsupervised and lightly supervised learning in particle physics
• Improving tracking algorithms with machine learning: a case for line-segment tracking at the High Luminosity LHC
• CapsLorentzNet: Integrating Physics Inspired Features with Graph Convolution
• High-energy physics image classification: A Survey of Jet Applications
• NuGraph2: A Graph Neural Network for Neutrino Physics Event Reconstruction
• Neural network representation of quantum systems
• Moments of Clarity: Streamlining Latent Spaces in Machine Learning using Moment Pooling
• Exploration at the high-energy frontier: ATLAS Run 2 searches investigating the exotic jungle beyond the Standard Model
• Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models
• Dark Matter-induced electron excitations in silicon and germanium with Deep Learning
• OmniJet-\(\alpha\): The first cross-task foundation model for particle physics
• New Pathways in Neutrino Physics via Quantum-Encoded Data Analysis
• Heavy quarkonium spectral function in an anisotropic background [DOI]
• Jet Discrimination with Quantum Complete Graph Neural Network
• Observation of electroweak production of \(W^+W^-\) in association with jets in proton-proton collisions at $\sqrt{s}
• Real-Time Charged Track Reconstruction for CLAS12
• Neural Network Learning and Quantum Gravity
• Higgs couplings in SMEFT via Zh production at the HL-LHC