A Living Review of Machine Learning for Particle Physics

Modern machine learning techniques, including deep learning, is rapidly being applied, adapted, and developed for high energy physics. The goal of this document is to provide a nearly comprehensive list of citations for those developing and applying these approaches to experimental, phenomenological, or theoretical analyses. As a living document, it will be updated as often as possible to incorporate the latest developments. A list of proper (unchanging) reviews can be found within. Papers are grouped into a small set of topics to be as useful as possible. Suggestions are most welcome.

The purpose of this note is to collect references for modern machine learning as applied to particle physics. A minimal number of categories is chosen in order to be as useful as possible. Note that papers may be referenced in more than one category. The fact that a paper is listed in this document does not endorse or validate its content - that is for the community (and for peer-review) to decide. Furthermore, the classification here is a best attempt and may have flaws - please let us know if (a) we have missed a paper you think should be included, (b) a paper has been misclassified, or (c) a citation for a paper is not correct or if the journal information is now available. In order to be as useful as possible, this document will continue to evolve so please check back before you write your next paper. If you find this review helpful, please consider citing it using \cite{hepmllivingreview} in HEPML.bib.

• Reviews

  • Modern reviews

    • Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning [DOI]
    • Deep Learning and its Application to LHC Physics [DOI]
    • Machine Learning in High Energy Physics Community White Paper [DOI]
    • Machine learning at the energy and intensity frontiers of particle physics
    • Machine learning and the physical sciences [DOI]
    • Machine and Deep Learning Applications in Particle Physics [DOI]
    • Modern Machine Learning and Particle Physics
    • Machine Learning in the Search for New Fundamental Physics
    • Artificial Intelligence and Machine Learning in Nuclear Physics

  • Specialized reviews

    • The Machine Learning Landscape of Top Taggers [DOI]
    • Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review
    • Graph neural networks in particle physics [DOI]
    • A Review on Machine Learning for Neutrino Experiments [DOI]
    • Generative Networks for LHC events
    • Parton distribution functions
    • Simulation-based inference methods for particle physics
    • Anomaly Detection for Physics Analysis and Less than Supervised Learning
    • Graph Neural Networks for Particle Tracking and Reconstruction
    • Distributed Training and Optimization Of Neural Networks
    • The frontier of simulation-based inference [DOI]
    • Machine Learning scientific competitions and datasets
    • Image-Based Jet Analysis
    • Quantum Machine Learning in High Energy Physics [DOI]
    • Sequence-based Machine Learning Models in Jet Physics
    • A survey of machine learning-based physics event generation
    • Deep Learning From Four Vectors
    • Solving Simulation Systematics in and with AI/ML
    • Symmetry Group Equivariant Architectures for Physics
    • Machine Learning and LHC Event Generation
    • Machine Learning and Cosmology
    • New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
    • Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges
    • Physics Community Needs, Tools, and Resources for Machine Learning
    • Boosted decision trees
    • Data Science and Machine Learning in Education

  • Classical papers

    • Neural Networks and Cellular Automata in Experimental High-energy Physics
    • Finding Gluon Jets With a Neural Trigger

  • Datasets

    • The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
    • The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
    • Shared Data and Algorithms for Deep Learning in Fundamental Physics
    • LHC physics dataset for unsupervised New Physics detection at 40 MHz
    • A FAIR and AI-ready Higgs Boson Decay Dataset
    • Particle Transformer for Jet Tagging

• Classification

  • Parameterized classifiers

    • Parameterized neural networks for high-energy physics [DOI]

    • Approximating Likelihood Ratios with Calibrated Discriminative Classifiers

    • E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once

    • Jet images

      • How to tell quark jets from gluon jets
      • Jet-Images: Computer Vision Inspired Techniques for Jet Tagging [DOI]
      • Playing Tag with ANN: Boosted Top Identification with Pattern Recognition [DOI]
      • Jet-images — deep learning edition [DOI]
      • Quark versus Gluon Jet Tagging Using Jet Images with the ATLAS Detector
      • Boosting $H\to b\bar b$ with Machine Learning [DOI]
      • Learning to classify from impure samples with high-dimensional data [DOI]
      • Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks [DOI]
      • Deep learning in color: towards automated quark/gluon [DOI]
      • Deep-learning Top Taggers or The End of QCD? [DOI]
      • Pulling Out All the Tops with Computer Vision and Deep Learning [DOI]
      • Reconstructing boosted Higgs jets from event image segmentation
      • An Attention Based Neural Network for Jet Tagging
      • Quark-Gluon Jet Discrimination Using Convolutional Neural Networks [DOI]
      • Learning to Isolate Muons
      • Deep learning jet modifications in heavy-ion collisions
      • Identifying the Quantum Properties of Hadronic Resonances using Machine Learning

    • Event images

      • Topology classification with deep learning to improve real-time event selection at the LHC [DOI]
      • Convolutional Neural Networks with Event Images for Pileup Mitigation with the ATLAS Detector
      • Boosting $H\to b\bar b$ with Machine Learning [DOI]
      • End-to-End Physics Event Classification with the CMS Open Data: Applying Image-based Deep Learning on Detector Data to Directly Classify Collision Events at the LHC [DOI]
      • Disentangling Boosted Higgs Boson Production Modes with Machine Learning
      • Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning [DOI]
      • End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data
      • Jet Single Shot Detection

    • Sequences

      • Jet Flavor Classification in High-Energy Physics with Deep Neural Networks [DOI]
      • Topology classification with deep learning to improve real-time event selection at the LHC [DOI]
      • Jet Flavour Classification Using DeepJet [DOI]
      • Development of a Vertex Finding Algorithm using Recurrent Neural Network
      • Sequence-based Machine Learning Models in Jet Physics
      • Identification of Jets Containing $b$-Hadrons with Recurrent Neural Networks at the ATLAS Experiment

    • Trees

      • QCD-Aware Recursive Neural Networks for Jet Physics [DOI]
      • Recursive Neural Networks in Quark/Gluon Tagging [DOI]
      • Introduction and analysis of a method for the investigation of QCD-like tree data

    • Graphs

      • Neural Message Passing for Jet Physics
      • Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
      • Probing stop pair production at the LHC with graph neural networks [DOI]
      • Pileup mitigation at the Large Hadron Collider with graph neural networks [DOI]
      • Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC [DOI]
      • JEDI-net: a jet identification algorithm based on interaction networks [DOI]
      • Learning representations of irregular particle-detector geometry with distance-weighted graph networks [DOI]
      • Interpretable deep learning for two-prong jet classification with jet spectra [DOI]
      • Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions [DOI]
      • Probing triple Higgs coupling with machine learning at the LHC
      • Casting a graph net to catch dark showers [DOI]
      • Graph neural networks in particle physics [DOI]
      • Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics [DOI]
      • Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons [DOI]
      • Track Seeding and Labelling with Embedded-space Graph Neural Networks
      • Graph neural network for 3D classification of ambiguities and optical crosstalk in scintillator-based neutrino detectors [DOI]
      • The Boosted Higgs Jet Reconstruction via Graph Neural Network
      • Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
      • Particle Track Reconstruction using Geometric Deep Learning
      • Jet tagging in the Lund plane with graph networks [DOI]
      • Vertex and Energy Reconstruction in JUNO with Machine Learning Methods
      • MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
      • Towards a realistic track reconstruction algorithm based on graph neural networks for the HL-LHC
      • Deep Learning strategies for ProtoDUNE raw data denoising
      • Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers
      • Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC
      • Charged particle tracking via edge-classifying interaction networks
      • Jet characterization in Heavy Ion Collisions by QCD-Aware Graph Neural Networks
      • Graph Generative Models for Fast Detector Simulations in High Energy Physics
      • Segmentation of EM showers for neutrino experiments with deep graph neural networks
      • Anomaly detection with Convolutional Graph Neural Networks
      • Energy-weighted Message Passing: an infra-red and collinear safe graph neural network algorithm
      • Improved Constraints on Effective Top Quark Interactions using Edge Convolution Networks
      • Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
      • Graph Neural Networks for Charged Particle Tracking on FPGAs
      • Machine Learning for Particle Flow Reconstruction at CMS
      • An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging
      • End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks

    • Sets (point clouds)

      • Energy Flow Networks: Deep Sets for Particle Jets [DOI]
      • ParticleNet: Jet Tagging via Particle Clouds [DOI]
      • ABCNet: An attention-based method for particle tagging [DOI]
      • Secondary Vertex Finding in Jets with Neural Networks
      • Equivariant Energy Flow Networks for Jet Tagging
      • Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks
      • Zero-Permutation Jet-Parton Assignment using a Self-Attention Network
      • Learning to Isolate Muons
      • Point Cloud Transformers applied to Collider Physics
      • SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention
      • Particle Convolution for High Energy Physics
      • Deep Sets based Neural Networks for Impact Parameter Flavour Tagging in ATLAS
      • Particle Transformer for Jet Tagging

    • Physics-inspired basis

      • Automating the Construction of Jet Observables with Machine Learning [DOI]
      • How Much Information is in a Jet? [DOI]
      • Novel Jet Observables from Machine Learning [DOI]
      • Energy flow polynomials: A complete linear basis for jet substructure [DOI]
      • Deep-learned Top Tagging with a Lorentz Layer [DOI]
      • Resurrecting $b\bar{b}h$ with kinematic shapes

    • $W/Z$ tagging

      • Jet-images — deep learning edition [DOI]
      • Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks [DOI]
      • QCD-Aware Recursive Neural Networks for Jet Physics [DOI]
      • Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques [DOI]
      • Boosted $W$ and $Z$ tagging with jet charge and deep learning [DOI]
      • Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons [DOI]
      • Jet tagging in the Lund plane with graph networks [DOI]
      • A $W^\pm$ polarization analyzer from Deep Neural Networks

    • $H\rightarrow b\bar{b$}

      • Automating the Construction of Jet Observables with Machine Learning [DOI]
      • Boosting $H\to b\bar b$ with Machine Learning [DOI]
      • Interaction networks for the identification of boosted $H \rightarrow b\overline{b}$ decays [DOI]
      • Interpretable deep learning for two-prong jet classification with jet spectra [DOI]
      • Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques [DOI]
      • Disentangling Boosted Higgs Boson Production Modes with Machine Learning
      • Benchmarking Machine Learning Techniques with Di-Higgs Production at the LHC
      • The Boosted Higgs Jet Reconstruction via Graph Neural Network
      • Extracting Signals of Higgs Boson From Background Noise Using Deep Neural Networks
      • Learning to increase matching efficiency in identifying additional b-jets in the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process
      • Higgs tagging with the Lund jet plane

    • quarks and gluons

      • Quark versus Gluon Jet Tagging Using Jet Images with the ATLAS Detector
      • Deep learning in color: towards automated quark/gluon [DOI]
      • Recursive Neural Networks in Quark/Gluon Tagging [DOI]
      • DeepJet: Generic physics object based jet multiclass classification for LHC experiments
      • Probing heavy ion collisions using quark and gluon jet substructure
      • JEDI-net: a jet identification algorithm based on interaction networks [DOI]
      • Quark-Gluon Tagging: Machine Learning vs Detector [DOI]
      • Towards Machine Learning Analytics for Jet Substructure [DOI]
      • Quark Gluon Jet Discrimination with Weakly Supervised Learning [DOI]
      • Quark-Gluon Jet Discrimination Using Convolutional Neural Networks [DOI]
      • Jet tagging in the Lund plane with graph networks [DOI]
      • Safety of Quark/Gluon Jet Classification
      • Identifying the Quantum Properties of Hadronic Resonances using Machine Learning
      • Quarks and gluons in the Lund plane

    • top quark tagging

      • Playing Tag with ANN: Boosted Top Identification with Pattern Recognition [DOI]
      • DeepJet: Generic physics object based jet multiclass classification for LHC experiments
      • The Machine Learning Landscape of Top Taggers [DOI]
      • Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions [DOI]
      • CapsNets Continuing the Convolutional Quest [DOI]
      • Deep-learned Top Tagging with a Lorentz Layer [DOI]
      • Deep-learning Top Taggers or The End of QCD? [DOI]
      • Pulling Out All the Tops with Computer Vision and Deep Learning [DOI]
      • Boosted Top Quark Tagging and Polarization Measurement using Machine Learning
      • Morphology for Jet Classification
      • Jet tagging in the Lund plane with graph networks [DOI]
      • Pulling the Higgs and Top needles from the jet stack with Feature Extended Supervised Tagging
      • End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data
      • Leveraging universality of jet taggers through transfer learning
      • Application of deep learning in top pair and single top quark production at the LHC
      • BIP: Boost Invariant Polynomials for Efficient Jet Tagging

    • strange jets

      • Strange Jet Tagging
      • A tagger for strange jets based on tracking information using long short-term memory [DOI]
      • Maximum performance of strange-jet tagging at hadron colliders

    • $b$-tagging

      • Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV [DOI]
      • Jet Flavor Classification in High-Energy Physics with Deep Neural Networks [DOI]
      • Identifying Heavy-Flavor Jets Using Vectors of Locally Aggregated Descriptors [DOI]
      • Jet Flavour Classification Using DeepJet [DOI]
      • Identification of Jets Containing $b$-Hadrons with Recurrent Neural Networks at the ATLAS Experiment
      • Deep Sets based Neural Networks for Impact Parameter Flavour Tagging in ATLAS

    • Flavor physics

      • 'Deep' Dive into $b \to c$ Anomalies: Standardized and Future-proof Model Selection Using Self-normalizing Neural Networks

    • BSM particles and models

      • Automating the Construction of Jet Observables with Machine Learning [DOI]
      • Searching for Exotic Particles in High-Energy Physics with Deep Learning [DOI]
      • Interpretable deep learning for two-prong jet classification with jet spectra [DOI]
      • A deep neural network to search for new long-lived particles decaying to jets [DOI]
      • Fast convolutional neural networks for identifying long-lived particles in a high-granularity calorimeter [DOI]
      • Casting a graph net to catch dark showers [DOI]
      • Distinguishing $W'$ Signals at Hadron Colliders Using Neural Networks [DOI]
      • Deep learnig analysis of the inverse seesaw in a 3-3-1 model at the LHC [DOI]
      • Comparing Traditional and Deep-Learning Techniques of Kinematic Reconstruction for polarisation Discrimination in Vector Boson Scattering [DOI]
      • Invisible Higgs search through Vector Boson Fusion: A deep learning approach [DOI]
      • Sensing Higgs cascade decays through memory [DOI]
      • Phenomenology of vector-like leptons with Deep Learning at the Large Hadron Collider [DOI]
      • WIMPs or else? Using Machine Learning to disentangle LHC signatures
      • Exploring the standard model EFT in VH production with machine learning [DOI]
      • Advanced Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider
      • Towards a method to anticipate dark matter signals with deep learning at the LHC
      • Top squark signal significance enhancement by different Machine Learning Algorithms
      • Detecting an axion-like particle with machine learning at the LHC
      • Unsupervised Hadronic SUEP at the LHC
      • Extract the energy scale of anomalous $\gamma\gamma \to W^+W^-$ scattering in the vector boson scattering process using artificial neural networks
      • Beyond Cuts in Small Signal Scenarios - Enhanced Sneutrino Detectability Using Machine Learning
      • Deep Learning Searches for Vector-Like Leptons at the LHC and Electron/Muon Colliders
      • Probing Higgs exotic decay at the LHC with machine learning
      • Machine Learning Optimized Search for the $Z'$ from $U(1){L\mu-L_\tau}$ at the LHC
      • Boosted decision trees in the era of new physics: a smuon analysis case study
      • How to use Machine Learning to improve the discrimination between signal and background at particle colliders
      • Event-level variables for semivisible jets using anomalous jet tagging
      • Improving heavy Dirac neutrino prospects at future hadron colliders using machine learning
      • Influence of QCD parton shower in deep learning invisible Higgs through vector boson fusion
      • Solving Combinatorial Problems at Particle Colliders Using Machine Learning
      • Phenomenology at the Large Hadron Collider with Deep Learning: the case of vector-like quarks decaying to light jets
      • Active learning BSM parameter spaces
      • Deep Learning Jet Image as a Probe of Light Higgsino Dark Matter at the LHC
      • Probing highly collimated photon-jets with deep learning
      • Measuring the anomalous quartic gauge couplings in the $W^+W^-\to W^+W^-$ process at muon collider using artificial neural networks
      • Machine learning the trilinear and light-quark Yukawa couplings from Higgs pair kinematic shapes

    • Particle identification

      • Electromagnetic Showers Beyond Shower Shapes [DOI]
      • Survey of Machine Learning Techniques for High Energy Electromagnetic Shower Classification
      • Calorimetry with Deep Learning: Particle Classification, Energy Regression, and Simulation for High-Energy Physics
      • Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics [DOI]
      • Learning representations of irregular particle-detector geometry with distance-weighted graph networks [DOI]
      • Learning to Identify Electrons
      • Shower Identification in Calorimeter using Deep Learning
      • A Neural-Network-defined Gaussian Mixture Model for particle identification applied to the LHCb fixed-target programme
      • Using Machine Learning for Particle Identification in ALICE
      • Artificial Intelligence for Imaging Cherenkov Detectors at the EIC

    • Neutrino Detectors

      • A Convolutional Neural Network Neutrino Event Classifier [DOI]
      • Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber [DOI]
      • Convolutional Neural Networks for Electron Neutrino and Electron Shower Energy Reconstruction in the NO$\nu$A Detectors
      • Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber [DOI]
      • Scalable deep convolutional neural networks for sparse, locally dense liquid argon time projection chamber data [DOI]
      • Event reconstruction for KM3NeT/ORCA using convolutional neural networks [DOI]
      • PILArNet: Public Dataset for Particle Imaging Liquid Argon Detectors in High Energy Physics
      • Point Proposal Network for Reconstructing 3D Particle Positions with Sub-Pixel Precision in Liquid Argon Time Projection Chambers
      • Neutrino interaction classification with a convolutional neural network in the DUNE far detector [DOI]
      • Clustering of electromagnetic showers and particle interactions with graph neural networks in liquid argon time projection chambers [DOI]
      • Scalable, Proposal-free Instance Segmentation Network for 3D Pixel Clustering and Particle Trajectory Reconstruction in Liquid Argon Time Projection Chambers
      • Augmented signal processing in Liquid Argon Time Projection Chambers with a deep neural network [DOI]
      • A Review on Machine Learning for Neutrino Experiments [DOI]
      • Graph neural network for 3D classification of ambiguities and optical crosstalk in scintillator-based neutrino detectors [DOI]
      • A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber
      • Study of using machine learning for level 1 trigger decision in JUNO experiment
      • Deep-Learning-Based Kinematic Reconstruction for DUNE
      • Semantic Segmentation with a Sparse Convolutional Neural Network for Event Reconstruction in MicroBooNE
      • Quantum Convolutional Neural Networks for High Energy Physics Data Analysis
      • Vertex and Energy Reconstruction in JUNO with Machine Learning Methods
      • A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory
      • Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for Particle Imaging Detectors
      • Deep Learning strategies for ProtoDUNE raw data denoising
      • Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers
      • A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber
      • Segmentation of EM showers for neutrino experiments with deep graph neural networks
      • CNNs for enhanced background discrimination in DSNB searches in large-scale water-Gd detectors
      • The use of Boosted Decision Trees for Energy Reconstruction in JUNO experiment
      • Deep learning reconstruction in ANTARES
      • Convolutional Neural Networks for Shower Energy Prediction in Liquid Argon Time Projection Chambers
      • Electromagnetic Shower Reconstruction and Energy Validation with Michel Electrons and $\pi^0$ Samples for the Deep-Learning-Based Analyses in MicroBooNE
      • Wire-Cell 3D Pattern Recognition Techniques for Neutrino Event Reconstruction in Large LArTPCs: Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation
      • Improvement of the NOvA Near Detector Event Reconstruction and Primary Vertexing through the Application of Machine Learning Methods
      • Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
      • Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
      • Towards Designing and Exploiting Generative Networks for Neutrino Physics Experiments using Liquid Argon Time Projection Chambers
      • Application of Transfer Learning to Neutrino Interaction Classification

    • Direct Dark Matter Detectors

      • Boosted decision trees approach to neck alpha events discrimination in DEAP-3600 experiment
      • Improving sensitivity to low-mass dark matter in LUX using a novel electrode background mitigation technique
      • Convolutional Neural Networks for Direct Detection of Dark Matter [DOI]
      • Deep Learning for direct Dark Matter search with nuclear emulsions
      • Scanning the landscape of axion dark matter detectors: applying gradient descent to experimental design
      • Machine-learning techniques applied to three-year exposure of ANAIS-112
      • Signal-agnostic dark matter searches in direct detection data with machine learning
      • Domain-informed neural networks for interaction localization within astroparticle experiments
      • Improving the machine learning based vertex reconstruction for large liquid scintillator detectors with multiple types of PMTs

    • Cosmology, Astro Particle, and Cosmic Ray physics

      • Detecting Subhalos in Strong Gravitational Lens Images with Image Segmentation
      • Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning [DOI]
      • Inverting cosmic ray propagation by Convolutional Neural Networks
      • Particle Track Reconstruction using Geometric Deep Learning
      • Deep-Learning based Reconstruction of the Shower Maximum $X_{\mathrm{max}}$ using the Water-Cherenkov Detectors of the Pierre Auger Observatory
      • A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications
      • Tackling the muon identification in water Cherenkov detectors problem for the future Southern Wide-field Gamma-ray Observatory by means of Machine Learning
      • Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using Machine Learning techniques
      • A convolutional-neural-network estimator of CMB constraints on dark matter energy injection
      • A neural network classifier for electron identification on the DAMPE experiment
      • Bayesian nonparametric inference of neutron star equation of state via neural network
      • Novel null tests for the spatial curvature and homogeneity of the Universe and their machine learning reconstructions
      • Machine Learning the 6th Dimension: Stellar Radial Velocities from 5D Phase-Space Correlations
      • Via Machinae: Searching for Stellar Streams using Unsupervised Machine Learning
      • Development of Convolutional Neural Networks for an Electron-Tracking Compton Camera
      • Machine Learning improved fits of the sound horizon at the baryon drag epoch
      • Using Convolutional Neural Networks for the Helicity Classification of Magnetic Fields
      • Dim but not entirely dark: Extracting the Galactic Center Excess' source-count distribution with neural nets
      • Constraining dark matter annihilation with cosmic ray antiprotons using neural networks
      • Probing Ultra-light Axion Dark Matter from 21cm Tomography using Convolutional Neural Networks
      • Inferring dark matter substructure with astrometric lensing beyond the power spectrum
      • A neural simulation-based inference approach for characterizing the Galactic Center $\gamma$-ray excess
      • Inference of cosmic-ray source properties by conditional invertible neural networks
      • Novel pre-burst stage of gamma-ray bursts from machine learning [DOI]
      • Deep learning techniques for Imaging Air Cherenkov Telescopes
      • Estimating the warm dark matter mass from strong lensing images with truncated marginal neural ratio estimation
      • BlaST -- A Machine-Learning Estimator for the Synchrotron Peak of Blazars
      • Modeling early-universe energy injection with Dense Neural Networks

    • Tracking

      • Particle Track Reconstruction with Deep Learning
      • Novel deep learning methods for track reconstruction
      • The Tracking Machine Learning challenge : Accuracy phase [DOI]
      • Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
      • An updated hybrid deep learning algorithm for identifying and locating primary vertices
      • Secondary Vertex Finding in Jets with Neural Networks
      • Track Seeding and Labelling with Embedded-space Graph Neural Networks
      • First application of machine learning algorithms to the position reconstruction in Resistive Silicon Detectors [DOI]
      • Beyond 4D Tracking: Using Cluster Shapes for Track Seeding
      • Hashing and metric learning for charged particle tracking
      • Development of a Vertex Finding Algorithm using Recurrent Neural Network
      • Towards a realistic track reconstruction algorithm based on graph neural networks for the HL-LHC
      • Progress in developing a hybrid deep learning algorithm for identifying and locating primary vertices
      • Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC
      • Physics and Computing Performance of the Exa.TrkX TrackML Pipeline
      • Charged particle tracking via edge-classifying interaction networks
      • Using Machine Learning to Select High-Quality Measurements
      • Optical Inspection of the Silicon Micro-strip Sensors for the CBM Experiment employing Artificial Intelligence
      • Machine learning for surface prediction in ACTS
      • Ariadne: PyTorch Library for Particle Track Reconstruction Using Deep Learning
      • Reconstruction of Large Radius Tracks with the Exa.TrkX pipeline

    • Heavy Ions / Nuclear Physics

      • An equation-of-state-meter of quantum chromodynamics transition from deep learning [DOI]
      • Probing heavy ion collisions using quark and gluon jet substructure
      • Deep learning jet modifications in heavy-ion collisions
      • Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning [DOI]
      • Estimation of Impact Parameter and Transverse Spherocity in heavy-ion collisions at the LHC energies using Machine Learning
      • Constraining nuclear effects in Argon using machine learning algorithms
      • Detecting Chiral Magnetic Effect via Deep Learning
      • Classifying near-threshold enhancement using deep neural network
      • Application of radial basis functions neutral networks in spectral functions
      • Deep Learning for the Classification of Quenched Jets
      • inclusiveAI: A machine learning representation of the $F_2$ structure function over all charted $Q^2$ and $x$ range
      • Jet tomography in heavy ion collisions with deep learning
      • An equation-of-state-meter for CBM using PointNet
      • Probing criticality with deep learning in relativistic heavy-ion collisions
      • Modeling of charged-particle multiplicity and transverse-momentum distributions in pp collisions using a DNN
      • Machine-learning-based identification for initial clustering structure in relativistic heavy-ion collisions
      • Particles Multiplicity Based on Rapidity in Landau and Artificial Neural Network(ANN) Models
      • Multiparton Interactions in pp collisions from Machine Learning
      • Implementation of machine learning techniques to predict impact parameter and transverse spherocity in heavy-ion collisions at the LHC
      • Deep Learning Exotic Hadrons
      • Entropy per rapidity in Pb-Pb central collisions using Thermal and Artificial neural network(ANN) models at LHC energies
      • Studying Hadronization by Machine Learning Techniques
      • The information content of jet quenching and machine learning assisted observable design
      • Classification of quark and gluon jets in hot QCD medium with deep learning
      • Jet tomography in hot QCD medium with deep learning
      • Determination of impact parameter in high-energy heavy-ion collisions via deep learning
      • Neural network reconstruction of the dense matter equation of state from neutron star observables
      • Particle ratios with in Hadron Resonance Gas (HRG) and Artificial Neural Network (ANN) models
      • New tool for kinematic regime estimation in semi-inclusive deep-inelastic scattering
      • Efficient emulation of relativistic heavy ion collisions with transfer learning
      • Identifying quenched jets in heavy ion collisions with machine learning
      • AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collider
      • Identify Hadronic Molecule States by Neural Network
      • Machine Learning model driven prediction of the initial geometry in Heavy-Ion Collision experiments

    • Hyperparameters

      • Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics [DOI]
      • Application of Deep Learning Technique to an Analysis of Hard Scattering Processes at Colliders

    • Weak/Semi supervision

      • Weakly Supervised Classification in High Energy Physics [DOI]
      • Classification without labels: Learning from mixed samples in high energy physics [DOI]
      • Learning to classify from impure samples with high-dimensional data [DOI]
      • Anomaly Detection for Resonant New Physics with Machine Learning [DOI]
      • Extending the search for new resonances with machine learning [DOI]
      • Machine Learning on data with sPlot background subtraction [DOI]
      • (Machine) Learning to Do More with Less [DOI]
      • An operational definition of quark and gluon jets [DOI]
      • Jet Topics: Disentangling Quarks and Gluons at Colliders [DOI]
      • Dijet resonance search with weak supervision using 13 TeV pp collisions in the ATLAS detector [DOI]
      • Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data [DOI]
      • Data-driven quark and gluon jet modification in heavy-ion collisions [DOI]
      • Machine learning approach for the search of resonances with topological features at the Large Hadron Collider
      • Quark Gluon Jet Discrimination with Weakly Supervised Learning [DOI]
      • An investigation of over-training within semi-supervised machine learning models in the search for heavy resonances at the LHC
      • Disentangling Quarks and Gluons with CMS Open Data
      • Semi-supervised Graph Neural Networks for Pileup Noise Removal
      • Going off topics to demix quark and gluon jets in $\alpha_S$ extractions

    • Unsupervised

      • Fuzzy Jets [DOI]
      • Metric Space of Collider Events [DOI]
      • Learning the latent structure of collider events [DOI]
      • Uncovering latent jet substructure [DOI]
      • Linearized Optimal Transport for Collider Events [DOI]
      • Foundations of a Fast, Data-Driven, Machine-Learned Simulator
      • Symmetries, Safety, and Self-Supervision

    • Reinforcement Learning

      • Jet grooming through reinforcement learning [DOI]
      • Hierarchical clustering in particle physics through reinforcement learning
      • Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster
      • Particle Physics Model Building with Reinforcement Learning
      • Reframing Jet Physics with New Computational Methods
      • A machine learning pipeline for autonomous numerical analytic continuation of Dyson-Schwinger equations

    • Quantum Machine Learning

      • Solving a Higgs optimization problem with quantum annealing for machine learning
      • Quantum adiabatic machine learning with zooming [DOI]
      • Quantum Machine Learning for Particle Physics using a Variational Quantum Classifier [DOI]
      • Event Classification with Quantum Machine Learning in High-Energy Physics [DOI]
      • Quantum Convolutional Neural Networks for High Energy Physics Data Analysis
      • Application of Quantum Machine Learning using the Quantum Variational Classifier Method to High Energy Physics Analysis at the LHC on IBM Quantum Computer Simulator and Hardware with 10 qubits
      • Quantum Machine Learning in High Energy Physics [DOI]
      • Hybrid Quantum-Classical Graph Convolutional Network
      • Unsupervised Event Classification with Graphs on Classical and Photonic Quantum Computers
      • Quantum Support Vector Machines for Continuum Suppression in B Meson Decays
      • Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
      • Higgs analysis with quantum classifiers
      • Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States
      • Style-based quantum generative adversarial networks for Monte Carlo events
      • Leveraging Quantum Annealer to identify an Event-topology at High Energy Colliders
      • Anomaly detection in high-energy physics using a quantum autoencoder
      • Quantum Machine Learning for $b$-jet identification
      • Completely Quantum Neural Networks
      • Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data
      • Unsupervised Quantum Circuit Learning in High Energy Physics
      • Quantum Anomaly Detection for Collider Physics

    • Feature ranking

      • Mapping Machine-Learned Physics into a Human-Readable Space [DOI]
      • Resurrecting $b\bar{b}h$ with kinematic shapes

    • Attention

      • Development of a Vertex Finding Algorithm using Recurrent Neural Network

    • Regularization

      • Combine and Conquer: Event Reconstruction with Bayesian Ensemble Neural Networks

    • Optimal Transport

      • Metric Space of Collider Events [DOI]
      • Linearized Optimal Transport for Collider Events [DOI]
      • Use of a Generalized Energy Mover's Distance in the Search for Rare Phenomena at Colliders [DOI]
      • Transport away your problems: Calibrating stochastic simulations with optimal transport
      • Which Metric on the Space of Collider Events?

    • Software

      • On the impact of modern deep-learning techniques to the performance and time-requirements of classification models in experimental high-energy physics [DOI]
      • Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree [DOI]
      • Deep topology classifiers for a more efficient trigger selection at the LHC
      • Topology classification with deep learning to improve real-time event selection at the LHC [DOI]
      • Using holistic event information in the trigger
      • Fast convolutional neural networks for identifying long-lived particles in a high-granularity calorimeter [DOI]
      • A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications
      • Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case [DOI]
      • Towards an Interpretable Data-driven Trigger System for High-throughput Physics Facilities
      • The Tracking Machine Learning challenge : Throughput phase
      • Jet Single Shot Detection
      • Ariadne: PyTorch Library for Particle Track Reconstruction Using Deep Learning

    • Hardware/firmware

      • Fast inference of deep neural networks in FPGAs for particle physics [DOI]
      • Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML [DOI]
      • Fast inference of Boosted Decision Trees in FPGAs for particle physics [DOI]
      • GPU coprocessors as a service for deep learning inference in high energy physics
      • Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics [DOI]
      • Studying the potential of Graphcore IPUs for applications in Particle Physics [DOI]
      • PDFFlow: parton distribution functions on GPU
      • FPGAs-as-a-Service Toolkit (FaaST) [DOI]
      • Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
      • PDFFlow: hardware accelerating parton density access [DOI]
      • Fast convolutional neural networks on FPGAs with hls4ml
      • Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference
      • Sparse Deconvolution Methods for Online Energy Estimation in Calorimeters Operating in High Luminosity Conditions
      • Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics
      • A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC
      • Muon trigger with fast Neural Networks on FPGA, a demonstrator
      • Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider
      • Graph Neural Networks for Charged Particle Tracking on FPGAs
      • Accelerating Deep Neural Networks for Real-time Data Selection for High-resolution Imaging Particle Detectors [DOI]
      • Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows
      • Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml
      • Nanosecond machine learning regression with deep boosted decision trees in FPGA for high energy physics

    • Deployment

      • MLaaS4HEP: Machine Learning as a Service for HEP
      • Distributed training and scalability for the particle clustering method UCluster [DOI]

• Regression

  • Pileup

    • Pileup Mitigation with Machine Learning (PUMML) [DOI]
    • Convolutional Neural Networks with Event Images for Pileup Mitigation with the ATLAS Detector
    • Pileup mitigation at the Large Hadron Collider with graph neural networks [DOI]
    • Jet grooming through reinforcement learning [DOI]
    • Pile-Up Mitigation using Attention
    • Semi-supervised Graph Neural Networks for Pileup Noise Removal

  • Calibration

    • Parametrizing the Detector Response with Neural Networks [DOI]
    • Simultaneous Jet Energy and Mass Calibrations with Neural Networks
    • Generalized Numerical Inversion: A Neural Network Approach to Jet Calibration
    • Calorimetry with Deep Learning: Particle Classification, Energy Regression, and Simulation for High-Energy Physics
    • Per-Object Systematics using Deep-Learned Calibration [DOI]
    • A deep neural network for simultaneous estimation of b jet energy and resolution [DOI]
    • How to GAN Higher Jet Resolution
    • Deep learning jet modifications in heavy-ion collisions
    • Calorimetric Measurement of Multi-TeV Muons via Deep Regression
    • Transport away your problems: Calibrating stochastic simulations with optimal transport
    • On the Use of Neural Networks for Energy Reconstruction in High-granularity Calorimeters
    • Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data [DOI]
    • Perspectives on the Calibration of CNN Energy Reconstruction in Highly Granular Calorimeters
    • Deeply Learning Deep Inelastic Scattering Kinematics
    • Energy reconstruction in a liquid argon calorimeter cell using convolutional neural networks
    • Using Convolutional Neural Networks to Reconstruct Energy of GeV Scale IceCube Neutrinos
    • Reconstructing the Kinematics of Deep Inelastic Scattering with Deep Learning
    • Implicit Quantile Neural Networks for Jet Simulation and Correction
    • Reconstructing partonic kinematics at colliders with Machine Learning
    • Machine Learning for Particle Flow Reconstruction at CMS
    • Machine-learning-based prediction of parameters of secondaries in hadronic showers using calorimetric observables
    • Deep Regression of Muon Energy with a K-Nearest Neighbor Algorithm
    • Reconstruction of Missing Resonances Combining Nearest Neighbors Regressors and Neural Network Classifiers
    • A Holistic Approach to Predicting Top Quark Kinematic Properties with the Covariant Particle Transformer
    • Deep learning applications for quality control in particle detector construction
    • Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics
    • Bias and Priors in Machine Learning Calibrations for High Energy Physics
    • Deep learning techniques for energy clustering in the CMS ECAL
    • $\nu$-Flows: conditional neutrino regression

  • Recasting

    • The BSM-AI project: SUSY-AI--generalizing LHC limits on supersymmetry with machine learning
    • Accelerating the BSM interpretation of LHC data with machine learning [DOI]
    • Bayesian Neural Networks for Fast SUSY Predictions [DOI]
    • Exploration of Parameter Spaces Assisted by Machine Learning

  • Matrix elements

    • Using neural networks for efficient evaluation of high multiplicity scattering amplitudes [DOI]
    • (Machine) Learning Amplitudes for Faster Event Generation
    • $\textsf{Xsec}$: the cross-section evaluation code [DOI]
    • Matrix Element Regression with Deep Neural Networks -- breaking the CPU barrier
    • Unveiling the pole structure of S-matrix using deep learning
    • Model independent analysis of coupled-channel scattering: a deep learning approach
    • Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
    • A factorisation-aware Matrix element emulator
    • Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates
    • Targeting Multi-Loop Integrals with Neural Networks
    • Fast and precise model calculation for KATRIN using a neural network
    • SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine ALearning
    • Simplifying Polylogarithms with Machine Learning

  • Parameter estimation

    • Numerical analysis of neutrino physics within a high scale supersymmetry model via machine learning [DOI]
    • Parametrized classifiers for optimal EFT sensitivity
    • MCNNTUNES: tuning Shower Monte Carlo generators with machine learning [DOI]
    • Deep-Learned Event Variables for Collider Phenomenology
    • Using Machine Learning techniques in phenomenological studies in flavour physics
    • Machine learning a manifold

  • Parton Distribution Functions (and related)

    • Neural-network analysis of Parton Distribution Functions from Ioffe-time pseudodistributions [DOI]
    • Deep Learning Analysis of Deeply Virtual Exclusive Photoproduction
    • PDFFlow: hardware accelerating parton density access [DOI]
    • Compressing PDF sets using generative adversarial networks
    • The Path to Proton Structure at One-Percent Accuracy
    • An open-source machine learning framework for global analyses of parton distributions
    • Exploring the substructure of nucleons and nuclei with machine learning
    • A new generation of simultaneous fits to LHC data using deep learning

  • Lattice Gauge Theory

    • Equivariant flow-based sampling for lattice gauge theory [DOI]
    • Lattice gauge equivariant convolutional neural networks
    • Generalization capabilities of translationally equivariant neural networks
    • Heavy Quark Potential in QGP: DNN meets LQCD
    • Flow-based sampling for multimodal distributions in lattice field theory
    • Machine Learning Estimators for Lattice QCD Observables [DOI]
    • Machine-learning prediction for quasiparton distribution function matrix elements [DOI]
    • A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D-Wave quantum annealer [DOI]
    • Lattice gauge symmetry in neural networks
    • Machine learning Hadron Spectral Functions in Lattice QCD
    • Equivariance and generalization in neural networks
    • Rethinking the ill-posedness of the spectral function reconstruction -- why is it fundamentally hard and how Artificial Neural Networks can help

  • Function Approximation

    • Elvet -- a neural network-based differential equation and variational problem solver
    • Invariant polynomials and machine learning
    • Function Approximation for High-Energy Physics: Comparing Machine Learning and Interpolation Methods
    • Reconstructing spectral functions via automatic differentiation
    • Robust and Provably Monotonic Networks

  • Symbolic Regression

    • Back to the Formula -- LHC Edition

• Decorrelation methods.

  • Learning to Pivot with Adversarial Networks [url]
  • Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure [DOI]
  • Convolved Substructure: Analytically Decorrelating Jet Substructure Observables
  • uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers [DOI]
  • Decorrelated Jet Substructure Tagging using Adversarial Neural Networks [DOI]
  • Mass Agnostic Jet Taggers [DOI]
  • Performance of mass-decorrelated jet substructure
  • DisCo Fever: Robust Networks Through Distance Correlation [DOI]
  • QBDT, a new boosting decision tree method with systematical uncertainties into training for High Energy Physics [DOI]
  • Machine Learning Uncertainties with Adversarial Neural Networks [DOI]
  • Reducing the dependence of the neural network function to systematic uncertainties in the input space [DOI]
  • New approaches for boosting to uniformity [DOI]
  • A deep neural network to search for new long-lived particles decaying to jets [DOI]
  • Adversarial domain adaptation to reduce sample bias of a high energy physics classifier
  • ABCDisCo: Automating the ABCD Method with Machine Learning [DOI]
  • Enhancing searches for resonances with machine learning and moment decomposition
  • A Cautionary Tale of Decorrelating Theory Uncertainties
  • Meta-learning and data augmentation for mass-generalised jet taggers
  • Online-compatible Unsupervised Non-resonant Anomaly Detection

• Generative models / density estimation

  • GANs:

    • Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis [DOI]
    • Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters [DOI]
    • CaloGAN : Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks [DOI]
    • Image-based model parameter optimization using Model-Assisted Generative Adversarial Networks [DOI]
    • How to GAN Event Subtraction [DOI]
    • Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description [DOI]
    • How to GAN away Detector Effects [DOI]
    • 3D convolutional GAN for fast simulation
    • Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks [DOI]
    • Lund jet images from generative and cycle-consistent adversarial networks [DOI]
    • How to GAN LHC Events [DOI]
    • Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model [DOI]
    • DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC [DOI]
    • LHC analysis-specific datasets with Generative Adversarial Networks
    • Generative Models for Fast Calorimeter Simulation.LHCb case [DOI]
    • Deep generative models for fast shower simulation in ATLAS
    • Regressive and generative neural networks for scalar field theory [DOI]
    • Three dimensional Generative Adversarial Networks for fast simulation
    • Generative models for fast simulation
    • Unfolding with Generative Adversarial Networks
    • Fast and Accurate Simulation of Particle Detectors Using Generative Adversarial Networks [DOI]
    • Generating and refining particle detector simulations using the Wasserstein distance in adversarial networks [DOI]
    • Generative models for fast cluster simulations in the TPC for the ALICE experiment
    • RICH 2018 [DOI]
    • GANs for generating EFT models [DOI]
    • Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network [DOI]
    • Reducing Autocorrelation Times in Lattice Simulations with Generative Adversarial Networks [DOI]
    • Tips and Tricks for Training GANs with Physics Constraints
    • Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters [DOI]
    • Next Generation Generative Neural Networks for HEP
    • Calorimetry with Deep Learning: Particle Classification, Energy Regression, and Simulation for High-Energy Physics
    • Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics [DOI]
    • A Novel Scenario in the Semi-constrained NMSSM [DOI]
    • Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
    • AI-based Monte Carlo event generator for electron-proton scattering
    • DCTRGAN: Improving the Precision of Generative Models with Reweighting [DOI]
    • GANplifying Event Samples
    • Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
    • Simulating the Time Projection Chamber responses at the MPD detector using Generative Adversarial Networks
    • Explainable machine learning of the underlying physics of high-energy particle collisions
    • A Data-driven Event Generator for Hadron Colliders using Wasserstein Generative Adversarial Network [DOI]
    • Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case [DOI]
    • Validation of Deep Convolutional Generative Adversarial Networks for High Energy Physics Calorimeter Simulations
    • Compressing PDF sets using generative adversarial networks
    • Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations
    • The use of Generative Adversarial Networks to characterise new physics in multi-lepton final states at the LHC
    • Latent Space Refinement for Deep Generative Models
    • Particle Cloud Generation with Message Passing Generative Adversarial Networks
    • Black-Box Optimization with Local Generative Surrogates [url]
    • Fast Simulation of a High Granularity Calorimeter by Generative Adversarial Networks
    • Photon detection probability prediction using one-dimensional generative neural network
    • Polarization measurement for the dileptonic channel of $W^+ W^-$ scattering using generative adversarial network
    • Style-based quantum generative adversarial networks for Monte Carlo events
    • Machine Learning for the LHCb Simulation
    • Non-Parametric Data-Driven Background Modelling using Conditional Probabilities
    • SymmetryGAN: Symmetry Discovery with Deep Learning
    • Hadrons, Better, Faster, Stronger
    • Calomplification - The Power of Generative Calorimeter Models
    • Towards a Deep Learning Model for Hadronization
    • Towards Reliable Neural Generative Modeling of Detectors
    • Generative Surrogates for Fast Simulation: TPC Case
    • GAN with an Auxiliary Regressor for the Fast Simulation of the Electromagnetic Calorimeter Response

  • Autoencoders

    • Deep Learning as a Parton Shower
    • Deep generative models for fast shower simulation in ATLAS
    • Variational Autoencoders for Anomalous Jet Tagging
    • Variational Autoencoders for Jet Simulation
    • Foundations of a Fast, Data-Driven, Machine-Learned Simulator
    • Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network
    • Bump Hunting in Latent Space
    • {End-to-end Sinkhorn Autoencoder with Noise Generator
    • Graph Generative Models for Fast Detector Simulations in High Energy Physics
    • DeepRICH: Learning Deeply Cherenkov Detectors [DOI]
    • An Exploration of Learnt Representations of W Jets
    • Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC
    • Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows
    • Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
    • Hadrons, Better, Faster, Stronger
    • Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
    • Modeling hadronization using machine learning

  • Normalizing flows

    • Flow-based generative models for Markov chain Monte Carlo in lattice field theory [DOI]
    • Equivariant flow-based sampling for lattice gauge theory [DOI]
    • Flows for simultaneous manifold learning and density estimation
    • Exploring phase space with Neural Importance Sampling [DOI]
    • Event Generation with Normalizing Flows [DOI]
    • i-flow: High-Dimensional Integration and Sampling with Normalizing Flows [DOI]
    • Anomaly Detection with Density Estimation [DOI]
    • Data-driven Estimation of Background Distribution through Neural Autoregressive Flows
    • SARM: Sparse Autoregressive Model for Scalable Generation of Sparse Images in Particle Physics [DOI]
    • Measuring QCD Splittings with Invertible Networks
    • Efficient sampling of constrained high-dimensional theoretical spaces with machine learning
    • Latent Space Refinement for Deep Generative Models
    • CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
    • Flow-based sampling for multimodal distributions in lattice field theory
    • Learning to discover: expressive Gaussian mixture models for multi-dimensional simulation and parameter inference in the physical sciences
    • Classifying Anomalies THrough Outer Density Estimation (CATHODE)
    • Black-Box Optimization with Local Generative Surrogates [url]
    • Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference [url]
    • Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows
    • Inference of cosmic-ray source properties by conditional invertible neural networks
    • CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows
    • Generative Networks for Precision Enthusiasts
    • Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows
    • Event Generation and Density Estimation with Surjective Normalizing Flows
    • $\nu$-Flows: conditional neutrino regression

  • Diffusion Models

    • Score-based Generative Models for Calorimeter Shower Simulation

  • Physics-inspired

    • JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
    • Binary JUNIPR: an interpretable probabilistic model for discrimination [DOI]
    • Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model [DOI]
    • Explainable machine learning of the underlying physics of high-energy particle collisions
    • Symmetry meets AI

  • Mixture Models

    • Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning
    • Mixture Density Network Estimation of Continuous Variable Maximum Likelihood Using Discrete Training Samples
    • A Neural-Network-defined Gaussian Mixture Model for particle identification applied to the LHCb fixed-target programme

  • Phase space generation

    • Efficient Monte Carlo Integration Using Boosted Decision
    • Exploring phase space with Neural Importance Sampling [DOI]
    • Event Generation with Normalizing Flows [DOI]
    • i-flow: High-Dimensional Integration and Sampling with Normalizing Flows [DOI]
    • Neural Network-Based Approach to Phase Space Integration [DOI]
    • VegasFlow: accelerating Monte Carlo simulation across multiple hardware platforms [DOI]
    • A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties [DOI]
    • Improved Neural Network Monte Carlo Simulation [DOI]
    • Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows [DOI]
    • How to GAN Event Unweighting
    • Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates
    • A machine learning approach for efficient multi-dimensional integration [DOI]

  • Gaussian processes

    • Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes
    • Accelerating the BSM interpretation of LHC data with machine learning [DOI]
    • $\textsf{Xsec}$: the cross-section evaluation code [DOI]
    • AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case [DOI]

• Anomaly detection.

  • Learning New Physics from a Machine [DOI]
  • Anomaly Detection for Resonant New Physics with Machine Learning [DOI]
  • Extending the search for new resonances with machine learning [DOI]
  • Learning Multivariate New Physics [DOI]
  • Searching for New Physics with Deep Autoencoders [DOI]
  • QCD or What? [DOI]
  • A robust anomaly finder based on autoencoder
  • Variational Autoencoders for New Physics Mining at the Large Hadron Collider [DOI]
  • Adversarially-trained autoencoders for robust unsupervised new physics searches [DOI]
  • Novelty Detection Meets Collider Physics [DOI]
  • Guiding New Physics Searches with Unsupervised Learning [DOI]
  • Does SUSY have friends? A new approach for LHC event analysis [DOI]
  • Nonparametric semisupervised classification for signal detection in high energy physics
  • Uncovering latent jet substructure [DOI]
  • Simulation Assisted Likelihood-free Anomaly Detection [DOI]
  • Anomaly Detection with Density Estimation [DOI]
  • A generic anti-QCD jet tagger [DOI]
  • Transferability of Deep Learning Models in Searches for New Physics at Colliders [DOI]
  • Use of a Generalized Energy Mover's Distance in the Search for Rare Phenomena at Colliders [DOI]
  • Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark [DOI]
  • Dijet resonance search with weak supervision using 13 TeV pp collisions in the ATLAS detector [DOI]
  • Learning the latent structure of collider events [DOI]
  • Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders [DOI]
  • Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data [DOI]
  • Variational Autoencoders for Anomalous Jet Tagging
  • Anomaly Awareness
  • Unsupervised Outlier Detection in Heavy-Ion Collisions
  • Decoding Dark Matter Substructure without Supervision
  • Mass Unspecific Supervised Tagging (MUST) for boosted jets [DOI]
  • Simulation-Assisted Decorrelation for Resonant Anomaly Detection
  • Anomaly Detection With Conditional Variational Autoencoders
  • Unsupervised clustering for collider physics
  • Combining outlier analysis algorithms to identify new physics at the LHC
  • Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
  • Uncovering hidden patterns in collider events with Bayesian probabilistic models
  • Unsupervised in-distribution anomaly detection of new physics through conditional density estimation
  • The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
  • Model-Independent Detection of New Physics Signals Using Interpretable Semi-Supervised Classifier Tests
  • Topological Obstructions to Autoencoding
  • Unsupervised Event Classification with Graphs on Classical and Photonic Quantum Computers
  • Bump Hunting in Latent Space
  • Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
  • Better Latent Spaces for Better Autoencoders
  • Autoencoders for unsupervised anomaly detection in high energy physics
  • Via Machinae: Searching for Stellar Streams using Unsupervised Machine Learning
  • Anomaly detection with Convolutional Graph Neural Networks
  • Anomalous Jet Identification via Sequence Modeling
  • The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
  • RanBox: Anomaly Detection in the Copula Space
  • Rare and Different: Anomaly Scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC
  • LHC physics dataset for unsupervised New Physics detection at 40 MHz
  • New Methods and Datasets for Group Anomaly Detection From Fundamental Physics
  • The Data-Directed Paradigm for BSM searches
  • Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider
  • Classifying Anomalies THrough Outer Density Estimation (CATHODE)
  • Deep Set Auto Encoders for Anomaly Detection in Particle Physics
  • Challenges for Unsupervised Anomaly Detection in Particle Physics
  • Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows
  • Signal-agnostic dark matter searches in direct detection data with machine learning
  • Anomaly detection from mass unspecific jet tagging
  • A method to challenge symmetries in data with self-supervised learning
  • Stressed GANs snag desserts, a.k.a Spotting Symmetry Violation with Symmetric Functions
  • Online-compatible Unsupervised Non-resonant Anomaly Detection
  • Event-based anomaly detection for new physics searches at the LHC using machine learning
  • Learning New Physics from an Imperfect Machine
  • Autoencoders for Semivisible Jet Detection
  • Anomaly detection in high-energy physics using a quantum autoencoder
  • Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure
  • Taming modeling uncertainties with Mass Unspecific Supervised Tagging
  • Quantum Anomaly Detection for Collider Physics
  • Self-supervised Anomaly Detection for New Physics
  • Data-directed search for new physics based on symmetries of the SM [DOI]
  • CURTAINs for your Sliding Window: Constructing Unobserved Regions by Transforming Adjacent Intervals
  • Learning new physics efficiently with nonparametric methods
  • ''Flux+Mutability'': A Conditional Generative Approach to One-Class Classification and Anomaly Detection
  • Event Generation and Density Estimation with Surjective Normalizing Flows
  • A Normalized Autoencoder for LHC Triggers
  • Mixture-of-theories Training: Can We Find New Physics and Anomalies Better by Mixing Physical Theories?

• Simulation-based (`likelihood-free') Inference

  • Parameter estimation

    • Neural Networks for Full Phase-space Reweighting and Parameter Tuning [DOI]
    • Likelihood-free inference with an improved cross-entropy estimator
    • Resonance Searches with Machine Learned Likelihood Ratios
    • Constraining Effective Field Theories with Machine Learning [DOI]
    • A Guide to Constraining Effective Field Theories with Machine Learning [DOI]
    • MadMiner: Machine learning-based inference for particle physics [DOI]
    • Mining gold from implicit models to improve likelihood-free inference [DOI]
    • Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
    • Parameter Estimation using Neural Networks in the Presence of Detector Effects [DOI]
    • Targeted Likelihood-Free Inference of Dark Matter Substructure in Strongly-Lensed Galaxies
    • Parameter Inference from Event Ensembles and the Top-Quark Mass
    • Measuring QCD Splittings with Invertible Networks
    • E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once
    • Tree boosting for learning EFT parameters
    • Black-Box Optimization with Local Generative Surrogates [url]
    • A neural simulation-based inference approach for characterizing the Galactic Center $\gamma$-ray excess
    • Machine Learning the Higgs-Top CP Phase
    • Constraining CP-violation in the Higgs-top-quark interaction using machine-learning-based inference
    • A method for approximating optimal statistical significances with machine-learned likelihoods

  • Unfolding

    • OmniFold: A Method to Simultaneously Unfold All Observables [DOI]
    • Unfolding with Generative Adversarial Networks
    • How to GAN away Detector Effects [DOI]
    • Machine learning approach to inverse problem and unfolding procedure
    • Machine learning as an instrument for data unfolding
    • Advanced event reweighting using multivariate analysis
    • Unfolding by weighting Monte Carlo events
    • Binning-Free Unfolding Based on Monte Carlo Migration
    • Invertible Networks or Partons to Detector and Back Again [DOI]
    • Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference [url]
    • Foundations of a Fast, Data-Driven, Machine-Learned Simulator
    • Comparison of Machine Learning Approach to other Unfolding Methods
    • Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution
    • Preserving New Physics while Simultaneously Unfolding All Observables
    • Measurement of lepton-jet correlation in deep-inelastic scattering with the H1 detector using machine learning for unfolding
    • Presenting Unbinned Differential Cross Section Results
    • Feed-forward neural network unfolding
    • Optimizing Observables with Machine Learning for Better Unfolding

  • Domain adaptation

    • Reweighting with Boosted Decision Trees [DOI]
    • Neural Networks for Full Phase-space Reweighting and Parameter Tuning [DOI]
    • Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
    • DCTRGAN: Improving the Precision of Generative Models with Reweighting [DOI]
    • Neural Conditional Reweighting
    • Model independent measurements of Standard Model cross sections with Domain Adaptation

  • BSM

    • Simulation Assisted Likelihood-free Anomaly Detection [DOI]
    • Resonance Searches with Machine Learned Likelihood Ratios
    • Constraining Effective Field Theories with Machine Learning [DOI]
    • A Guide to Constraining Effective Field Theories with Machine Learning [DOI]
    • Mining gold from implicit models to improve likelihood-free inference [DOI]
    • MadMiner: Machine learning-based inference for particle physics [DOI]
    • Use of a Generalized Energy Mover's Distance in the Search for Rare Phenomena at Colliders [DOI]
    • Exploring Parameter Spaces with Artificial Intelligence and Machine Learning Black-Box Optimisation Algorithms

  • Differentiable Simulation

    • Differentiable Matrix Elements with MadJax

• Uncertainty Quantification

  • Interpretability

    • Jet-images — deep learning edition [DOI]
    • What is the Machine Learning? [DOI]
    • CapsNets Continuing the Convolutional Quest [DOI]
    • Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation
    • Resurrecting $b\bar{b}h$ with kinematic shapes
    • Safety of Quark/Gluon Jet Classification
    • An Exploration of Learnt Representations of W Jets
    • Explaining machine-learned particle-flow reconstruction
    • Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure
    • Improving Parametric Neural Networks for High-Energy Physics (and Beyond)
    • Lessons on interpretable machine learning from particle physics [DOI]

  • Estimation

    • A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty [DOI]
    • AI Safety for High Energy Physics
    • Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks [DOI]
    • Understanding Event-Generation Networks via Uncertainties
    • Exploring the Universality of Hadronic Jet Classification

  • Mitigation

    • Adversarial learning to eliminate systematic errors: a case study in High Energy Physics
    • Machine Learning Uncertainties with Adversarial Neural Networks [DOI]
    • Learning to Pivot with Adversarial Networks [url]
    • Combine and Conquer: Event Reconstruction with Bayesian Ensemble Neural Networks
    • Improving robustness of jet tagging algorithms with adversarial training

  • Uncertainty- and inference-aware learning

    • Constraining the Parameters of High-Dimensional Models with Active Learning [DOI]
    • Deep-Learning Jets with Uncertainties and More [DOI]
    • INFERNO: Inference-Aware Neural Optimisation [DOI]
    • Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters [DOI]
    • Uncertainty Aware Learning for High Energy Physics
    • Punzi-loss: A non-differentiable metric approximation for sensitivity optimisation in the search for new particles
    • neos: End-to-End-Optimised Summary Statistics for High Energy Physics [DOI]

• Experimental results. This section is incomplete as there are many results that directly and indirectly (e.g. via flavor tagging) use modern machine learning techniques. We will try to highlight experimental results that use deep learning in a critical way for the final analysis sensitivity.

  • Performance studies

    • Identification of hadronic tau lepton decays using a deep neural network

  • Searches and measurements were ML reconstruction is a core component

    • Search for an anomalous excess of inclusive charged-current $\nu_e$ interactions in the MicroBooNE experiment using Wire-Cell reconstruction
    • Search for an anomalous excess of charged-current quasi-elastic $\nu_e$ interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction

  • Final analysis discriminate for searches

    • Search for non-resonant Higgs boson pair production in the $bb\ell\nu\ell\nu$ final state with the ATLAS detector in $pp$ collisions at $\sqrt{s} [DOI]
    • Search for Higgs boson decays into a $Z$ boson and a light hadronically decaying resonance using 13 TeV $pp$ collision data from the ATLAS detector [DOI]
    • Dijet resonance search with weak supervision using 13 TeV pp collisions in the ATLAS detector [DOI]
    • Inclusive search for highly boosted Higgs bosons decaying to bottom quark-antiquark pairs in proton-proton collisions at $\sqrt{s} [DOI]

  • Measurements using deep learning directly (not through object reconstruction)

    • Measurement of lepton-jet correlation in deep-inelastic scattering with the H1 detector using machine learning for unfolding