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.

November 2024

• Improving smuon searches with Neural Networks (2024)
• Pseudo-observables and Deep Neural Network for mixed CP -- H to tau tau decays at LHC (2024)
• Truth, beauty, and goodness in grand unification: a machine learning approach (2024)
• Reweighting simulated events using machine-learning techniques in the CMS experiment (2024)
• Fast multi-geometry calorimeter simulation with conditional self-attention variational autoencoders (2024)
• Transformers for Charged Particle Track Reconstruction in High Energy Physics (2024)
• Search for vector-like leptons coupling to first- and second-generation Standard Model leptons in \(pp\) collisions at $\sqrt{s} (2024)
• Rejection Sampling with Autodifferentiation -- Case study: Fitting a Hadronization Model (2024)
• The Fundamental Limit of Jet Tagging (2024)
• Generative Unfolding with Distribution Mapping (2024)
• Physics-informed neural networks viewpoint for solving the Dyson-Schwinger equations of quantum electrodynamics (2024)
• New Physics Through Flavor Tagging at FCC-ee (2024)
• A versatile framework for attitude tuning of beamlines at advanced light sources (2024)
• Profile Likelihoods on ML-Steroids (2024)
• A Lorentz-Equivariant Transformer for All of the LHC (2024)

October 2024

• Systematic Interpretability and the Likelihood for Boosted Top Quark Identification (2024)
• Machine Learning Electroweakino Production (2024)
• Point cloud-based diffusion models for the Electron-Ion Collider (2024)
• Hybrid quantum-classical approach for combinatorial problems at hadron colliders (2024)
• HGPflow: Extending Hypergraph Particle Flow to Collider Event Reconstruction (2024)
• Variational inference for pile-up removal at hadron colliders with diffusion models (2024)
• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation (2024)
• SIGMA: Single Interpolated Generative Model for Anomalies (2024)
• On learning higher-order cumulants in diffusion models (2024)
• Diffusion models for lattice gauge field simulations (2024)
• cymyc -- Calabi-Yau Metrics, Yukawas, and Curvature (2024)
• Optimal Equivariant Architectures from the Symmetries of Matrix-Element Likelihoods (2024)
• A novel quantum machine learning classifier to search for new physics (2024)
• Probing Light Scalars and Vector-like Quarks at the High-Luminosity LHC (2024)
• Performance of the CMS high-level trigger during LHC Run 2 (2024)
• Advancing Physics Data Analysis through Machine Learning and Physics-Informed Neural Networks (2024)
• Fast Perfekt: Regression-based refinement of fast simulation (2024)
• Machine Learning-Powered Data Cleaning for LEGEND (2024)
• Application of Machine Learning Based Top Quark and W Jet Tagging to Hadronic Four-Top Final States Induced by SM and BSM Processes (2024)
• MACK: Mismodeling Addressed with Contrastive Knowledge (2024)
• Exploring jets: substructure and flavour tagging in CMS and ATLAS (2024)
• Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC (2024)
• Learning Efficient Representations of Neutrino Telescope Events (2024)
• Observation of a rare beta decay of the charmed baryon with a Graph Neural Network (2024)
• Continuous normalizing flows for lattice gauge theories (2024)
• Application of Particle Transformer to quark flavor tagging in the ILC project (2024)
• Calabi-Yau metrics through Grassmannian learning and Donaldson's algorithm (2024)
• Machine learning tagged boosted dark photon: A signature of fermionic portal matter at the LHC (2024)
• Machine learning opportunities for online and offline tagging of photo-induced and diffractive events in continuous readout experiments (2024)
• Advancing Tools for Simulation-Based Inference (2024)
• Measurements of decay branching fractions of the Higgs boson to hadronic final states at the CEPC (2024)
• Building Hadron Potentials from Lattice QCD with Deep Neural Networks (2024)
• FAIR Universe HiggsML Uncertainty Challenge Competition (2024)
• Real-time Position Reconstruction for the KamLAND-Zen Experiment using Hardware-AI Co-design (2024)
• Intelligent Pixel Detectors: Towards a Radiation Hard ASIC with On-Chip Machine Learning in 28 nm CMOS (2024)
• Model-independent searches of new physics in DARWIN with a semi-supervised deep learning pipeline (2024)

September 2024

• Novel machine learning applications at the LHC (2024)
• Nanosecond hardware regression trees in FPGA at the LHC (2024)
• Bootstrapping string models with entanglement minimization and Machine-Learning (2024)
• Polarized and unpolarized gluon PDFs: generative machine learning applications for lattice QCD matrix elements at short distance and large momentum (2024)
• A Variational Approach to Quantum Field Theory (2024)
• Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA (2024)
• Machine Learning Toric Duality in Brane Tilings (2024)
• Signal model parameter scan using Normalizing Flow [DOI] (2024)
• Is Tokenization Needed for Masked Particle Modelling? (2024)
• Conformal Fields from Neural Networks (2024)
• Unveiling the Secrets of New Physics Through Top Quark Tagging (2024)
• Search for light long-lived particles decaying to displaced jets in proton-proton collisions at \(\sqrt{s}\) (2024)
• Evaluating Modifications to Classifiers for Identification of Higgs Bosons (2024)
• Reinforcement learning-based statistical search strategy for an axion model from flavor (2024)
• Multidimensional Deconvolution with Profiling (2024)
• Converting sWeights to Probabilities with Density Ratios (2024)
• Detect anomalous quartic gauge couplings at muon colliders with quantum kernel k-means (2024)
• ADFilter -- A Web Tool for New Physics Searches With Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks (2024)
• Application of Kolmogorov-Arnold Networks in high energy physics (2024)
• An Extended Closure Relation by LightGBM for Neutrino Radiation Transport in Core-collapse Supernovae (2024)
• A novel machine learning method to detect double-\(\Lambda\) hypernuclear events in nuclear emulsions (2024)

August 2024

• Variational Monte Carlo with Neural Network Quantum States for Yang-Mills Matrix Model (2024)
• Estimating event-by-event multiplicity by a Machine Learning Method for Hadronization Studies (2024)
• Semi-supervised permutation invariant particle-level anomaly detection (2024)
• Vertex Imaging Hadron Calorimetry Using AI/ML Tools (2024)
• Estimation of the pseudoscalar glueball mass based on a modified Transformer (2024)
• Multiple testing for signal-agnostic searches of new physics with machine learning (2024)
• From strange-quark tagging to fragmentation tagging with machine learning (2024)
• RODEM Jet Datasets (2024)
• Full Detector Simulation of a Projective Dual-Readout Segmented Crystal Electromagnetic Calorimeter with Precision Timing (2024)
• Electron-nucleus cross sections from transfer learning (2024)
• Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-Resolution (2024)
• Bayesian Inference analysis of jet quenching using inclusive jet and hadron suppression measurements (2024)
• Pay Attention To Mean Fields For Point Cloud Generation (2024)
• Learning the Simplicity of Scattering Amplitudes (2024)
• Modelling parametric uncertainty in PDEs models via Physics-Informed Neural Networks (2024)
• Neural Network Modeling of Heavy-Quark Potential from Holography (2024)
• Calibrating Bayesian Generative Machine Learning for Bayesiamplification (2024)
• Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects [DOI] (2024)
• Differentiable MadNIS-Lite (2024)