A Living Review of Machine Learning for Particle Physics

Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics. Given the fast pace of this research, we have created a living review with the goal of providing 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 and contributions are most welcome, and we provide instructions for participating.

[1]  L. Hollenberg,et al.  Quantum Support Vector Machines for Continuum Suppression in B Meson Decays , 2021, Computing and Software for Big Science.

[2]  F. Bishara,et al.  Machine learning amplitudes for faster event generation , 2019, Physical Review D.

[3]  Jin Min Yang,et al.  Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC , 2019, Physics Letters B.

[4]  Matthew D. Klimek,et al.  Neural network-based approach to phase space integration , 2018, SciPost Physics.

[5]  Mike Williams,et al.  uBoost: a boosting method for producing uniform selection efficiencies from multivariate classifiers , 2013, 1305.7248.

[6]  Sung Hak Lim,et al.  Morphology for jet classification , 2020, Physical Review D.

[7]  Benjamin Nachman,et al.  Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters. , 2017, Physical review letters.

[8]  Andy Buckley,et al.  Xsec: the cross-section evaluation code , 2020, The European Physical Journal C.

[9]  A. Redelbach,et al.  An equation-of-state-meter for CBM using PointNet , 2021, Journal of High Energy Physics.

[10]  Salvatore Rappoccio,et al.  Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation , 2020, Journal of High Energy Physics.

[11]  Tom Heskes,et al.  Constraining the parameters of high-dimensional models with active learning , 2019, The European Physical Journal C.

[12]  Andrew Edmonds,et al.  Using machine learning to select high-quality measurements , 2021, ArXiv.

[13]  Y. Verma,et al.  Shower Identification in Calorimeter using Deep Learning , 2021, 2103.16247.

[14]  Meng Zhou,et al.  Application of radial basis functions neutral networks in spectral functions , 2021, Physical Review D.

[15]  F. T. Collaboration,et al.  Parton Distribution Functions , 2016 .

[16]  S. Nagu,et al.  Constraining nuclear effects in Argon using machine learning algorithms , 2021, 2105.12733.

[17]  Stefano Carrazza,et al.  VegasFlow: Accelerating Monte Carlo simulation across multiple hardware platforms , 2020, Comput. Phys. Commun..

[18]  Cheng Chen,et al.  Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning , 2020, ArXiv.

[19]  J. Pumplin,et al.  How to tell quark jets from gluon jets. , 1991, Physical review. D, Particles and fields.

[20]  S. Tripathy,et al.  Estimation of impact parameter and transverse spherocity in heavy-ion collisions at the LHC energies using machine learning , 2021, Physical Review D.

[21]  C. Cowden,et al.  On the use of neural networks for energy reconstruction in high-granularity calorimeters , 2021, Journal of Instrumentation.

[22]  D. Whiteson,et al.  Deep Learning and Its Application to LHC Physics , 2018, Annual Review of Nuclear and Particle Science.

[23]  Luke de Oliveira.  Tips and Tricks for Training GANs with Physics Constraints , 2017 .

[24]  A. Butter,et al.  Generative Networks for LHC Events , 2020, Artificial Intelligence for High Energy Physics.

[25]  Tilman Plehn,et al.  How to GAN LHC events , 2019, SciPost Physics.

[27]  Pile-Up Mitigation using Attention , 2021, 2107.02779.

[28]  S. Brown,et al.  Machine learning representation of the F2 structure function over all charted Q2 and x range , 2021, Physical Review C.

[29]  Michael Kagan,et al.  Image-Based Jet Analysis , 2020, Artificial Intelligence for High Energy Physics.

[30]  L. Gouskos,et al.  The Machine Learning landscape of top taggers , 2019, SciPost Physics.

[31]  Jan M. Pawlowski,et al.  Reducing autocorrelation times in lattice simulations with generative adversarial networks , 2018, Mach. Learn. Sci. Technol..

[32]  Ibrahim A. Hameed,et al.  Using Convolutional Neural Networks for the Helicity Classification of Magnetic Fields , 2021, Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021).

[33]  Top squark signal significance enhancement by different Machine Learning Algorithms , 2021, 2106.06813.

[34]  Bruce Mellado,et al.  The use of Generative Adversarial Networks to characterise new physics in multi-lepton final states at the LHC , 2021, ArXiv.

[35]  Tree boosting for learning EFT parameters , 2021, 2107.10859.

[36]  L. Dery,et al.  Weakly supervised classification in high energy physics , 2017, Journal of High Energy Physics.

[37]  M. Mahmoud,et al.  Modeling of charged-particle multiplicity and transverse-momentum distributions in pp collisions using a DNN , 2021, Scientific Reports.

[38]  G. Kasieczka,et al.  Deep-learning top taggers or the end of QCD? , 2017, 1701.08784.

[39]  Maurizio Pierini,et al.  Adversarially Learned Anomaly Detection on CMS open data: re-discovering the top quark , 2020, The European Physical Journal Plus.

[40]  J. de Seixas,et al.  Sparse deconvolution methods for online energy estimation in calorimeters operating in high luminosity conditions , 2021, Journal of Instrumentation.

[41]  Philip Harris,et al.  Machine learning uncertainties with adversarial neural networks , 2018, The European Physical Journal C.

[42]  Toru Sato,et al.  Model independent analysis of coupled-channel scattering: A deep learning approach , 2021, Physical Review D.

[43]  Martin Erdmann,et al.  Generating and Refining Particle Detector Simulations Using the Wasserstein Distance in Adversarial Networks , 2018, Computing and Software for Big Science.

[44]  MicroBooNE collaboration C. Adams,et al.  Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber , 2018, Physical Review D.

[45]  P. Harris,et al.  Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure , 2016, 1603.00027.

[46]  X. Ruan,et al.  Machine learning approach for the search of resonances with topological features at the Large Hadron Collider , 2020, International Journal of Modern Physics A.

[47]  O. Amram,et al.  Tag N’ Train: a technique to train improved classifiers on unlabeled data , 2020, Journal of High Energy Physics.

[48]  S. M. Etesami,et al.  A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution , 2019, Computing and Software for Big Science.

[49]  Eric A. Moreno,et al.  JEDI-net: a jet identification algorithm based on interaction networks , 2019, The European Physical Journal C.

[50]  P. Baldi,et al.  Jet flavor classification in high-energy physics with deep neural networks , 2016, 1607.08633.

[51]  Atlas Collaboration.  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 , 2020, 2004.01678.

[52]  R. K. Neely,et al.  Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber , 2020, Physical Review D.

[53]  B. Nachman,et al.  Extending the search for new resonances with machine learning , 2019, Physical Review D.

[54]  Fedor Ratnikov,et al.  Generative Models for Fast Calorimeter Simulation.LHCb case , 2018, ArXiv.

[55]  Tommaso Dorigo,et al.  Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review , 2020, ArXiv.

[56]  Kyle Cranmer,et al.  Hierarchical clustering in particle physics through reinforcement learning , 2020, ArXiv.

[57]  Ulrich Heintz,et al.  End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data , 2021, Physical Review D.

[58]  Gilles Louppe,et al.  Approximating Likelihood Ratios with Calibrated Discriminative Classifiers , 2015, 1506.02169.

[59]  Jin Min Yang,et al.  Detecting an axion-like particle with machine learning at the LHC , 2021, Journal of High Energy Physics.

[60]  Andrey Ustyuzhanin,et al.  Segmentation of EM showers for neutrino experiments with deep graph neural networks , 2021, Journal of Instrumentation.

[61]  Cheongjae Jang,et al.  Learning to increase matching efficiency in identifying additional b-jets in the tt̅b̅ process , 2021, ArXiv.

[62]  T. Tanimori,et al.  Development of convolutional neural networks for an electron-tracking Compton camera , 2021, Progress of Theoretical and Experimental Physics.

[63]  C. Collaboration,et al.  Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques , 2020, 2004.08262.

[64]  Anders Andreassen,et al.  binary junipr: An Interpretable Probabilistic Model for Discrimination. , 2019, Physical review letters.

[65]  Gilles Louppe,et al.  The frontier of simulation-based inference , 2020, Proceedings of the National Academy of Sciences.

[66]  B. Nachman,et al.  Classification without labels: learning from mixed samples in high energy physics , 2017, Journal of High Energy Physics.

[67]  B. Nachman.  A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty , 2019, SciPost Physics.

[68]  Andreas Ipp,et al.  Lattice gauge equivariant convolutional neural networks , 2020, Physical review letters.

[69]  Miron Livny,et al.  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 , 2020, Journal of Physics G: Nuclear and Particle Physics.

[70]  Savannah Thais,et al.  Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC , 2021, ArXiv.

[71]  Song Han,et al.  Fast inference of deep neural networks in FPGAs for particle physics , 2018, Journal of Instrumentation.

[72]  Mauro Verzetti,et al.  DeepJet : Generic physics object based jet multiclass classification for LHC experiments , 2017 .

[73]  P. Ambrozewicz,et al.  AI-based Monte Carlo event generator for electron-proton scattering , 2020 .

[74]  M. Sokoloff,et al.  Progress in developing a hybrid deep learning algorithm for identifying and locating primary vertices , 2021, EPJ Web of Conferences.

[75]  B. Nachman.  Anomaly Detection for Physics Analysis and Less Than Supervised Learning , 2020, Artificial Intelligence for High Energy Physics.

[76]  M. Freytsis,et al.  (Machine) learning to do more with less , 2017, Journal of High Energy Physics.

[77]  W. Brooks,et al.  AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case , 2019, Journal of Instrumentation.

[78]  Myeonghun Park,et al.  Deep-Learned Event Variables for Collider Phenomenology , 2021, Physical Review D.

[79]  Prabhat,et al.  Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors , 2020, 2003.11603.

[80]  David Shih,et al.  Simulation assisted likelihood-free anomaly detection , 2020 .

[81]  Leandro Giordano Almeida,et al.  Playing tag with ANN: boosted top identification with pattern recognition , 2015, 1501.05968.

[82]  Daniel O'Hanlon,et al.  Studying the Potential of Graphcore® IPUs for Applications in Particle Physics , 2020, Comput. Softw. Big Sci..

[83]  Corey Adams,et al.  PILArNet: Public Dataset for Particle Imaging Liquid Argon Detectors in High Energy Physics , 2020, ArXiv.

[84]  David Shih,et al.  CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows , 2021, ArXiv.

[85]  Michelle Kuchera,et al.  Bayesian Neural Networks for Fast SUSY Predictions , 2020 .

[86]  Sofia Vallecorsa,et al.  Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations , 2021, EPJ Web of Conferences.

[87]  B. Nachman,et al.  Identifying the Quantum Properties of Hadronic Resonances using Machine Learning , 2021, 2105.04582.

[88]  David Shih,et al.  New Methods and Datasets for Group Anomaly Detection From Fundamental Physics , 2021, ArXiv.

[89]  Benjamin Carlson,et al.  Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics , 2021, ArXiv.

[90]  B. Clerbaux,et al.  Study of Using Machine Learning for Level 1 Trigger Decision in JUNO Experiment , 2020, IEEE Transactions on Nuclear Science.

[91]  Patrick T. Komiske,et al.  Pileup Mitigation with Machine Learning (PUMML) , 2017, Journal of High Energy Physics.

[92]  Sascha Diefenbacher,et al.  Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed , 2020 .

[93]  Prabhat,et al.  Novel deep learning methods for track reconstruction , 2018, 1810.06111.

[94]  Stefano Carrazza,et al.  Jet Grooming through Reinforcement Learning , 2019, Journal of Physics: Conference Series.

[95]  Vladimir Gligorov,et al.  New approaches for boosting to uniformity , 2014, 1410.4140.

[96]  Astronomy,et al.  Convolutional neural networks for direct detection of dark matter , 2019, 1911.09210.

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[98]  Benjamin Nachman,et al.  Latent Space Refinement for Deep Generative Models , 2021, ArXiv.

[99]  K. Cranmer,et al.  MadMiner: Machine Learning-Based Inference for Particle Physics , 2019, Computing and Software for Big Science.

[100]  P. Baldi,et al.  Learning to isolate muons , 2021, Journal of High Energy Physics.

[101]  Daniel Alvestad,et al.  Beyond Cuts in Small Signal Scenarios -- Enhanced Sneutrino Detectability Using Machine Learning , 2021, 2108.03125.

[102]  S. Balasubramanian,et al.  Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE , 2020, Physical Review D.

[103]  Michael Spannowsky,et al.  Elvet - a neural network-based differential equation and variational problem solver , 2021, ArXiv.

[104]  Gianluca Cerminara,et al.  Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics , 2020, Frontiers in Big Data.

[105]  G. Zech,et al.  Binning-Free Unfolding Based on Monte Carlo Migration , 2003 .

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[107]  Prabhat,et al.  Track Seeding and Labelling with Embedded-space Graph Neural Networks , 2020, ArXiv.

[109]  G. Kasieczka,et al.  Per-object systematics using deep-learned calibration , 2020, SciPost Physics.

[110]  Muhammad Abbas,et al.  Extracting Signals of Higgs Boson From Background Noise Using Deep Neural Networks , 2020, ArXiv.

[111]  Thong Q. Nguyen,et al.  Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider , 2021, Nat. Mach. Intell..

[112]  Gurtej Kanwar,et al.  Flow-based sampling for multimodal distributions in lattice field theory , 2021, ArXiv.

[113]  G. Menardi,et al.  Nonparametric semi-supervised classification with application to signal detection in high energy physics , 2021, Statistical Methods & Applications.

[114]  Maria Spiropulu,et al.  Topology Classification with Deep Learning to Improve Real-Time Event Selection at the LHC , 2018, Computing and Software for Big Science.

[115]  D. Maître,et al.  A factorisation-aware matrix element emulator , 2021, Journal of High Energy Physics.

[116]  Philip Harris,et al.  Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml , 2020, Mach. Learn. Sci. Technol..

[117]  S. Gleyzer,et al.  Decoding Dark Matter Substructure without Supervision , 2020, 2008.12731.

[118]  B. Nachman,et al.  Neural networks for full phase-space reweighting and parameter tuning , 2019, Physical Review D.

[119]  Nicolo Cartiglia,et al.  First application of machine learning algorithms to the position reconstruction in Resistive Silicon Detectors , 2020, 2011.02410.

[120]  E. Metodiev,et al.  Jet Topics: Disentangling Quarks and Gluons at Colliders. , 2018, Physical review letters.

[121]  D. Bourilkov.  Machine and deep learning applications in particle physics , 2019, International Journal of Modern Physics A.

[122]  Vinicius Mikuni,et al.  ABCNet: an attention-based method for particle tagging , 2020, European physical journal plus.

[123]  A. Larkoski,et al.  Novel jet observables from machine learning , 2017, 1710.01305.

[124]  Sascha Diefenbacher,et al.  DCTRGAN: improving the precision of generative models with reweighting , 2020, Journal of Instrumentation.

[125]  Anibal D. Medina,et al.  Towards a method to anticipate dark matter signals with deep learning at the LHC , 2021, SciPost Physics.

[126]  P. G. Isar,et al.  Deep-learning based reconstruction of the shower maximum X max using the water-Cherenkov detectors of the Pierre Auger Observatory , 2021, Journal of Instrumentation.

[127]  Uros Seljak,et al.  Unsupervised in-distribution anomaly detection of new physics through conditional density estimation , 2020, ArXiv.

[128]  Javier Duarte,et al.  Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs , 2020, ArXiv.

[129]  J. Asorey,et al.  Probing ultra-light axion dark matter from 21 cm tomography using Convolutional Neural Networks , 2021, Journal of Cosmology and Astroparticle Physics.

[130]  Francesco Pandolfi,et al.  Fast and Accurate Simulation of Particle Detectors Using Generative Adversarial Networks , 2018, Computing and Software for Big Science.

[131]  Hoang Dai Nghia Nguyen,et al.  Dijet Resonance Search with Weak Supervision Using sqrt[s]=13  TeV pp Collisions in the ATLAS Detector. , 2020, Physical review letters.

[132]  G. Kasieczka,et al.  Unsupervised hadronic SUEP at the LHC , 2021, Journal of High Energy Physics.

[133]  Ward Haddadin,et al.  Invariant polynomials and machine learning , 2021, ArXiv.

[134]  B. Nachman,et al.  Neural resampler for Monte Carlo reweighting with preserved uncertainties , 2020, Physical Review D.

[135]  C. Bromberg,et al.  A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber , 2021, Journal of Instrumentation.

[136]  C. Tunnell,et al.  A review on machine learning for neutrino experiments , 2020, 2008.01242.

[137]  Simon Badger,et al.  Optimising simulations for diphoton production at hadron colliders using amplitude neural networks , 2021, Journal of High Energy Physics.

[138]  D. Whiteson,et al.  Resonance Searches with Machine Learned Likelihood Ratios , 2020, 2002.04699.

[139]  Michael Spannowsky,et al.  Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States , 2021, Journal of High Energy Physics.

[140]  Novel null tests for the spatial curvature and homogeneity of the Universe and their machine learning reconstructions , 2021, 2103.06789.

[141]  Dimitrios Gunopulos,et al.  Particle Cloud Generation with Message Passing Generative Adversarial Networks , 2021, ArXiv.

[142]  Sascha Diefenbacher,et al.  CapsNets continuing the convolutional quest , 2019, SciPost Physics.

[143]  K. Cranmer,et al.  Simulation-Based Inference Methods for Particle Physics , 2020, Artificial Intelligence for High Energy Physics.

[144]  Deepak Kar,et al.  Unfolding with Generative Adversarial Networks , 2018, 1806.00433.

[145]  Tilo Wettig,et al.  Machine learning for surface prediction in ACTS , 2021, EPJ Web of Conferences.

[146]  Jin Min Yang,et al.  Probing stop pair production at the LHC with graph neural networks , 2018, Journal of High Energy Physics.

[147]  Yu Lou,et al.  FPGAs-as-a-Service Toolkit (FaaST) , 2020, 2020 IEEE/ACM International Workshop on Heterogeneous High-performance Reconfigurable Computing (H2RC).

[148]  Simon Badger,et al.  Using neural networks for efficient evaluation of high multiplicity scattering amplitudes , 2020 .

[149]  Nikolai Gagunashvili,et al.  Machine learning approach to inverse problem and unfolding procedure , 2010, 1004.2006.

[150]  Martin Erdmann,et al.  Shared Data and Algorithms for Deep Learning in Fundamental Physics , 2021, ArXiv.

[151]  Hajime Nagahara,et al.  Development of a Vertex Finding Algorithm using Recurrent Neural Network , 2021, ArXiv.

[152]  J. Erdmann,et al.  A tagger for strange jets based on tracking information using long short-term memory , 2019, Journal of Instrumentation.

[153]  Gurtej Kanwar,et al.  Equivariant flow-based sampling for lattice gauge theory , 2020, Physical review letters.

[154]  Daniel A. Lidar,et al.  Solving a Higgs optimization problem with quantum annealing for machine learning , 2017, Nature.

[155]  Ian Watson,et al.  Quark Gluon Jet Discrimination with Weakly Supervised Learning , 2019, Journal of the Korean Physical Society.

[156]  P. Vahle,et al.  A convolutional neural network neutrino event classifier , 2016, ArXiv.

[157]  Jernej F. Kamenik,et al.  Uncovering latent jet substructure , 2019, Physical Review D.

[158]  L. Xia.  QBDT, a new boosting decision tree method with systematical uncertainties into training for High Energy Physics , 2018, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment.

[159]  Gorjan Alagic,et al.  #p , 2019, Quantum information & computation.

[160]  Felix Kahlhoefer,et al.  Constraining dark matter annihilation with cosmic ray antiprotons using neural networks , 2021, Journal of Cosmology and Astroparticle Physics.

[161]  J. A. Aguilar-Saavedra,et al.  A generic anti-QCD jet tagger , 2017, 1709.01087.

[162]  B. Nachman,et al.  Automating the construction of jet observables with machine learning , 2019, Physical Review D.

[163]  Gregor Kasieczka,et al.  Quark-gluon tagging: Machine learning vs detector , 2018, SciPost Physics.

[164]  Jonathan Shlomi,et al.  Graph neural networks in particle physics , 2020, Mach. Learn. Sci. Technol..

[165]  J. A. Aguilar-Saavedra,et al.  Mass Unspecific Supervised Tagging (MUST) for boosted jets , 2020, Journal of High Energy Physics.

[166]  W. Bhimji,et al.  Next Generation Generative Neural Networks for HEP , 2019, EPJ Web of Conferences.

[167]  Patrick T. Komiske,et al.  Learning to classify from impure samples with high-dimensional data , 2018, Physical Review D.

[168]  Maurizio Pierini,et al.  Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description , 2019, Journal of Physics: Conference Series.

[169]  Jinmian Li,et al.  Reconstructing boosted Higgs jets from event image segmentation , 2020, Journal of High Energy Physics.

[170]  Michela Paganini,et al.  Electromagnetic showers beyond shower shapes , 2018, 1806.05667.

[171]  C. Fitzpatrick,et al.  Using holistic event information in the trigger , 2018, 1808.00711.

[172]  Sven Krippendorf,et al.  GANs for generating EFT models , 2018, Physics Letters B.

[173]  Christina Gao,et al.  Event generation with normalizing flows , 2020 .

[174]  A. Simone,et al.  Guiding new physics searches with unsupervised learning , 2018, The European Physical Journal C.

[175]  Stefano Carrazza,et al.  MCNNTUNES: Tuning Shower Monte Carlo generators with machine learning , 2020, Comput. Phys. Commun..

[176]  Rafael Lima.  Sequence-based Machine Learning Models in Jet Physics , 2021, ArXiv.

[177]  M. Spannowsky,et al.  Adversarially-trained autoencoders for robust unsupervised new physics searches , 2019, Journal of High Energy Physics.

[178]  M. Kadastik,et al.  Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics , 2020, The European Physical Journal C.

[179]  Christophe Delaere,et al.  Advanced Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider , 2021, ArXiv.

[180]  C. Herold,et al.  Unsupervised outlier detection in heavy-ion collisions , 2020, Physica Scripta.

[182]  S. Nesseris,et al.  Machine learning improved fits of the sound horizon at the baryon drag epoch , 2021, Physical Review D.

[183]  S. Ardid,et al.  Deep learning reconstruction in ANTARES , 2021, Journal of Instrumentation.

[184]  Vinicius Mikuni,et al.  Point cloud transformers applied to collider physics , 2021, Mach. Learn. Sci. Technol..

[185]  Michela Paganini,et al.  CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks , 2017, ArXiv.

[186]  Alexander Vandenberg-Rodes,et al.  Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes , 2017, 1709.05681.

[187]  T. Trzciński,et al.  Generative Models for Fast Cluster Simulations in the TPC for the ALICE Experiment , 2018, Advances in Intelligent Systems and Computing.

[188]  R. D’Agnolo,et al.  Learning new physics from a machine , 2018, Physical Review D.

[189]  Deep Learning Searches for Vector-Like Leptons at the LHC and Electron/Muon Colliders , 2021, 2108.03926.

[190]  B. S. González,et al.  Tackling the muon identification in water Cherenkov detectors problem for the future Southern Wide-field Gamma-ray Observatory by means of machine learning , 2021, Neural Computing and Applications.

[191]  J. A. Aguilar-Saavedra.  Pulling the Higgs and top needles from the jet stack with feature extended supervised tagging , 2021, The European Physical Journal C.

[192]  Marko Jercic,et al.  Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model , 2020, Entropy.

[193]  M. Spannowsky,et al.  Unsupervised event classification with graphs on classical and photonic quantum computers , 2021, Journal of High Energy Physics.

[194]  E. P. Takeva,et al.  Search for non-resonant Higgs boson pair production in the bbℓνℓν final state with the ATLAS detector in pp collisions at √s =13 TeV , 2020 .

[195]  Barnabás Póczos,et al.  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 , 2018, ArXiv.

[196]  M. Pierini,et al.  The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider , 2021, SciPost Physics.

[197]  Max Welling,et al.  Accelerating the BSM interpretation of LHC data with machine learning , 2016, Physics of the Dark Universe.

[198]  Tilman Plehn,et al.  How to GAN event subtraction , 2019 .

[199]  Benjamin Nachman,et al.  Machine learning templates for QCD factorization in the search for physics beyond the standard model , 2019, Journal of High Energy Physics.

[200]  Wenjie Wu,et al.  Deep-Learning-Based Kinematic Reconstruction for DUNE , 2020, ArXiv.

[201]  Matthew D. Klimek,et al.  Improved neural network Monte Carlo simulation , 2020, 2009.07819.

[202]  Chase Shimmin,et al.  Particle Convolution for High Energy Physics , 2021, ArXiv.

[203]  Simone Marzani,et al.  Towards machine learning analytics for jet substructure , 2020 .

[204]  Philip Harris,et al.  GPU coprocessors as a service for deep learning inference in high energy physics , 2021, Mach. Learn. Sci. Technol..

[205]  Jake Grigsby,et al.  Deep learning analysis of deeply virtual exclusive photoproduction , 2020, Physical Review D.

[206]  Gilles Louppe,et al.  Likelihood-free inference with an improved cross-entropy estimator , 2018, ArXiv.

[207]  Martin Erdmann,et al.  Precise Simulation of Electromagnetic Calorimeter Showers Using a Wasserstein Generative Adversarial Network , 2018, Computing and Software for Big Science.

[208]  Jin Min Yang,et al.  Probing the triple Higgs boson coupling with machine learning at the LHC , 2020, Physical Review D.

[209]  David Rousseau,et al.  Further developments of FORM , 2018, Journal of Physics: Conference Series.

[210]  C. Fanelli,et al.  DeepRICH: learning deeply Cherenkov detectors , 2019, Mach. Learn. Sci. Technol..

[211]  Jennifer Thompson,et al.  Deep-learning jets with uncertainties and more , 2019, SciPost Physics.

[212]  Gregor Kasieczka,et al.  Deep-learned Top Tagging with a Lorentz Layer , 2017, SciPost Physics.

[213]  M. Buckley,et al.  Via Machinae: Searching for Stellar Streams using Unsupervised Machine Learning , 2021, 2104.12789.

[214]  G. Zech,et al.  Unfolding by weighting Monte Carlo events , 1995 .

[215]  Maria Spiropulu,et al.  Variational autoencoders for new physics mining at the Large Hadron Collider , 2018, Journal of High Energy Physics.

[216]  Inkyu Park,et al.  Quark-Gluon Jet Discrimination Using Convolutional Neural Networks , 2019, Journal of the Korean Physical Society.

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