Machine Learning in Nuclear Physics

Abstract

Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Review gives a snapshot of nuclear physics research which has been transformed by machine learning techniques.

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• How well do we know the neutron-matter equation of state at the densities inside neutron stars? A Bayesian approach with correlated uncertainties
• Constraints on large-$x$ parton distributions from new weak boson production and deep-inelastic scattering data
• Critical slowing down and error analysis in lattice QCD simulations
• Deep learning in color: towards automated quark/gluon jet discrimination
• The equation of state for dense nucleonic matter from a metamodeling. I. Foundational aspects
• Ab initio predictions link the neutron skin of ${}^{208}$Pb to nuclear forces
• Search for Neutrinoless Double-Beta Decay with the Complete EXO-200 Dataset
• First measurement of electron neutrino appearance in NOvA
• Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects
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• Constraining the dense matter equation of state with joint analysis of NICER and LIGO/Virgo measurements
• First simultaneous extraction of spin-dependent parton distributions and fragmentation functions from a global QCD analysis
• Flow-based generative models for Markov chain Monte Carlo in lattice field theory
• Jet Flavour Classification Using DeepJet
• Accurate molecular polarizabilities with coupled-cluster theory and machine learning
• Neutron star mass and radius measurements from atmospheric model fits to X-ray burst cooling tail spectra
• Bayesian approach to model-based extrapolation of nuclear observables
• Equivariant flow-based sampling for lattice gauge theory
• Astrophysical Constraints on the Symmetry Energy and the Neutron Skin of ${}^{208}$Pb with Minimal Modeling Assumptions
• Constraining the Eq. of State of Super-Hadronic Matter from Heavy-Ion Collisions
• The equation of state for dense nucleonic matter from a meta-modeling. II. Predictions for neutron stars properties
• Quantifying Correlated Truncation Errors in Effective Field Theory
• Bayesian truncation errors in chiral effective field theory: nucleon-nucleon observables
• Searching for neutrinoless double-beta decay of ${}^{130}$Te with CUORE
• Neutron drip line in the Ca region from Bayesian model averaging
• $\Delta$-Machine Learning for Potential Energy Surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) Level of Theory
• Science Requirements and Detector Concepts for the Electron-Ion Collider: EIC Yellow Report
• The Nuclear Science References (NSR) Database and Web Retrieval System
• Bayesian optimization of a free-electron laser
• Automation and control of laser wakefield accelerators using Bayesian optimisation
• Machine Learning of Explicit Order Parameters: From the Ising Model to SU(2) Lattice Gauge Theory
• Covariant approximation averaging
• Quantifying uncertainties and correlations in the nuclear-matter equation of state
• Predictions of nuclear $\beta$-decay half-lives with machine learning and their impacts on $r$ process
• Nuclear charge radii: Density functional theory meets Bayesian neural networks
• Eigenvector continuation with subspace learning
• Theia: An advanced optical neutrino detector
• A data-driven analysis for the temperature and momentum dependence of the heavy quark diffusion coefficient in relativistic heavy-ion collisions
• Probing Majorana neutrinos with double-$\beta$ decay
• Determining the jet transport coefficient $\hat{q}$ from inclusive hadron suppression measurements using Bayesian parameter estimation
• Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements
• Origin of single transverse-spin asymmetries in high-energy collisions
• Deep Learning and Density Functional Theory
• Simultaneous Monte Carlo analysis of parton densities and fragmentation functions
• Global sensitivity analysis of bulk properties of an atomic nucleus
• A Search for Neutrinoless Double-Beta Decay in ${}^{76}$Ge with 26 kg-yr of Exposure from the MAJORANA DEMONSTRATOR
• Constraining the equation of state of high-density cold matter using nuclear and astronomical measurements
• Machine learning the nuclear mass
• Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
• Pileup Mitigation with Machine Learning (PUMML)
• Sampling using $SU(N)$ gauge equivariant flows
• Bayesian Evaluation of Incomplete Fission Yields
• Eigenvector Continuation as an Efficient and Accurate Emulator for Uncertainty Quantification
• First Monte Carlo global analysis of nucleon transversity with lattice QCD constraints
• Reconstructing parton distribution functions from Ioffe time data: from Bayesian methods to Neural Networks
• Strange quark suppression from a simultaneous Monte Carlo analysis of parton distributions and fragmentation functions
• Quantified limits of the nuclear landscape
• Refining mass formulas for astrophysical applications: a Bayesian neural network approach
• Get on the BAND Wagon: A Bayesian Framework for Quantifying Model Uncertainties in Nuclear Dynamics
• Machine Learning for Orders of Magnitude Speedup in Multi-Objective Optimization of Particle Accelerator Systems
• Bayesian modeling of the nuclear equation of state for neutron star tidal deformabilities and GW170817
• Exploring Bayesian parameter estimation for chiral effective field theory using nucleon-nucleon phase shifts
• LHCb Topological Trigger Reoptimization
• Deep Learning and AdS/CFT
• Combining Electromagnetic and Gravitational-Wave Constraints on Neutron-Star Masses and Radii
• Direct Measurements of Neutrino Mass
• Variational Monte Carlo calculations of $\mathbf{A}\le 4$ nuclei with an artificial neural-network correlator ansatz
• Taming nuclear complexity with a committee of multilayer neural networks
• NLO PDFs from the ABMP16 fit
• Bayesian Inference of the Symmetry Energy of Super-Dense Neutron-Rich Matter from Future Radius Measurements of Massive Neutron Stars
• Study of Charge Radii with Neural Networks
• Rigorous constraints on three-nucleon forces in chiral effective field theory from fast and accurate calculations of few-body observables
• Pulse shape discrimination for GERDA Phase I data
• Direct comparison between Bayesian and frequentist uncertainty quantification for nuclear reactions
• Constraining the symmetry energy with heavy-ion collisions and Bayesian analyses
• Machine Learning Spatial Geometry from Entanglement Features
• Finite-Density Monte Carlo Calculations on Sign-Optimized Manifolds
• Applications and Techniques for Fast Machine Learning in Science
• nEXO Pre-Conceptual Design Report
• An efficient method to compute the residual phase on a Lefschetz thimble
• Reconstructing QCD Spectral Functions with Gaussian Processes
• Deep Learning and Holographic QCD
• Decoding Beta-Decay Systematics: A Global Statistical Model for Beta^- Halflives
• Efficient emulators for scattering using eigenvector continuation
• Deep learning the Hohenberg-Kohn maps of Density Functional Theory
• Deep Learning Beyond Lefschetz Thimbles
• Galaxy Formation: Bayesian History Matching for the Observable Universe
• Deep learning: Extrapolation tool for ab initio nuclear theory
• Machine learning action parameters in lattice quantum chromodynamics
• Background rejection in NEXT using deep neural networks
• Nuclear masses learned from a probabilistic neural network
• Nuclear masses in extended kernel ridge regression with odd-even effects
• Machine learning the deuteron
• Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning
• Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei
• Extrapolation of nuclear structure observables with artificial neural networks
• Lattice gauge equivariant convolutional neural networks
• AdS/CFT as a deep Boltzmann machine
• Extensive Studies of the Neutron Star Equation of State from the Deep Learning Inference with the Observational Data Augmentation
• Heavy quark potential in quark-gluon Plasma: Deep neural network meets lattice quantum chromodynamics
• Confronting lattice parton distributions with global QCD analysis
• Multi-messenger and multi-physics Bayesian inference for GW170817 binary neutron star merger
• Validating neural-network refinements of nuclear mass models
• A machine learning study to identify spinodal clumping in high energy nuclear collisions
• Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory
• Efficient Modelling of Trivializing Maps for Lattice ${\varphi }^4$ Theory Using Normalizing Flows: A First Look at Scalability
• Halo effective field theory constrains the solar Beryllium-7 + proton -> Boron-8 + photon rate
• Nuclear liquid-gas phase transition with machine learning
• Flow-based sampling for fermionic lattice field theories
• Toward emulating nuclear reactions using eigenvector continuation
• Fermions at Finite Density in (2+1)d with Sign-Optimized Manifolds
• Thimble regularization at work: from toy models to chiral random matrix theories
• LEGEND-1000 Preconceptual Design Report
• Lefschetz thimbles decomposition for the Hubbard model on the hexagonal lattice
• Bayesian evaluation of charge yields of fission fragments of 239U
• Complex-valued neural networks for machine learning on non-stationary physical data
• Bayesian extraction of jet energy loss distributions in heavy-ion collisions
• Neural-network analysis of Parton Distribution Functions from Ioffe-time pseudodistributions
• Vertex and Energy Reconstruction in JUNO with Machine Learning Methods
• Bayesian inference of nuclear symmetry energy from measured and imagined neutron skin thickness in ${}^{116,118,120,122,124,130,132}$Sn, ${}^{208}$Pb, and ${}^{48}$Ca
• Propagation of Statistical Uncertainties of Skyrme Mass Models to Simulations of $r$-Process Nucleosynthesis
• Fast & accurate emulation of two-body scattering observables without wave functions
• Statistical aspects of nuclear mass models
• Bayesian nonparametric inference of neutron star equation of state via neural network
• Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning
• A Bayesian Approach for Parameter Estimation and Prediction using a Computationally Intensive Model
• Constraining Transfer Cross Sections Using Bayes' Theorem
• Estimation of fusion-evaporation cross sections by artificial neural networks
• Mapping distinct phase transitions to a neural network
• A fast centrality-meter for heavy-ion collisions at the CBM experiment
• Method to solve quantum few-body problems with artificial neural networks
• Microscopically-based energy density functionals for nuclei using the density matrix expansion: Full optimization and validation
• Development, characterisation, and deployment of the SNO+ liquid scintillator
• Covariant Density Functional Theory in Nuclear Physics and Astrophysics
• Hamilton dynamics for the Lefschetz thimble integration akin to the complex Langevin method
• Nuclear binding energy predictions using neural networks: Application of the multilayer perceptron
• Quantifying uncertainties in neutron-alpha scattering with chiral nucleon-nucleon and three-nucleon forces
• Deep learning based pulse shape discrimination for germanium detectors
• Quantifying Uncertainties on Fission Fragment Mass Yields With Mixture Density Networks
• Machine-learning-based identification for initial clustering structure in relativistic heavy-ion collisions
• Lightweight self-conjugate nucleus ${}^{80}$Zr
• Fast emulation of quantum three-body scattering
• Deep learning jet modifications in heavy-ion collisions
• Reducing Autocorrelation Times in Lattice Simulations with Generative Adversarial Networks
• The nuclear symmetry energy from neutron skins and pure neutron matter in a Bayesian framework
• Machine-learning physics from unphysics: Finding deconfinement temperature in lattice Yang-Mills theories from outside the scaling window
• Path integral contour deformations for observables in $SU(N)$ gauge theory
• Recent advances in the quantification of uncertainties in reaction theory
• Flow-based sampling for multimodal and extended-mode distributions in lattice field theory
• Machine learning-based inversion of nuclear responses
• The description of giant dipole resonance key parameters with multitask neural networks
• $S$-factor and scattering-parameter extractions from ${}^3\mathrm{He}+{}^4\mathrm{He}\to {}^7\mathrm{Be}+\gamma$
• Multiobjective optimization of the dynamic aperture for SLS 2.0 using surrogate models based on artificial neural networks
• Machine Learning Methods for Track Classification in the AT-TPC
• Nuclei with up to $\mathbf{A}=6$ nucleons with artificial neural network wave functions
• Electromagnetic Showers Beyond Shower Shapes
• Normalizing Flows and the Real-Time Sign Problem
• Experimental Aspects of Jet Physics at a Future EIC
• Machine-Learning Prediction for Quasi-PDF Matrix Elements
• Application Of Support Vector Machines To Global Prediction Of Nuclear Properties
• Leveraging Machine Learning to Alleviate Hubbard Model Sign Problems
• Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies
• Machine Learning Estimators for Lattice QCD Observables
• Optimizing multilayer Bayesian neural networks for evaluation of fission yields
• Neural ODE and Holographic QCD
• Impact of statistical uncertainties on the composition of the outer crust of a neutron star
• Reconstructing the Kinematics of Deep Inelastic Scattering with Deep Learning
• Quantifying modeling uncertainties when combining multiple gravitational-wave detections from binary neutron star sources
• Bayesian inference of the incompressibility, skewness and kurtosis of nuclear matter from empirical pressures in relativistic heavy-ion collisions
• Cherenkov Detectors Fast Simulation Using Neural Networks
• AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case
• Revisiting Bayesian constraints on the transport coefficients of QCD
• Fast & rigorous predictions for $A=6$ nuclei with Bayesian posterior sampling
• Exploring experimental conditions to reduce uncertainties in the optical potential
• Self-learning Emulators and Eigenvector Continuation
• Neural Canonical Transformation with Symplectic Flows
• A simple approach towards the sign problem using path optimisation
• Exact representations of many body interactions with RBM neural networks
• Estimation of Impact Parameter and Transverse Spherocity in heavy-ion collisions at the LHC energies using Machine Learning
• Explainable machine learning of the underlying physics of high-energy particle collisions
• Accurate and confident prediction of electron beam longitudinal properties using spectral virtual diagnostics
• Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
• Classifying Pole of Amplitude Using Deep Neural Network
• Bayesian parameter estimation in $\chi$EFT using Hamiltonian Monte Carlo
• Conception and software implementation of a nuclear data evaluation pipeline
• Designing Optimal Experiments: An Application to Proton Compton Scattering
• Hot and Dense Homogeneous Nucleonic Matter Constrained by Observations, Experiment, and Theory
• Bayesian optimization in ab initio nuclear physics
• Trees and Forests in Nuclear Physics
• Ge Detectors and $0\nu \beta \beta$: The Search for Double Beta Decay with Germanium Detectors: Past, Present and Future
• Testing trivializing maps in the Hybrid Monte Carlo algorithm
• Parton distribution functions from lattice QCD using Bayes-Gauss-Fourier transforms
• Constructing approximate shell-model wavefunctions by eigenvector continuation
• Deeply Learning Deep Inelastic Scattering Kinematics
• Extrapolating from neural network models: a cautionary tale
• Suppression of Cosmic Muon Spallation Backgrounds in Liquid Scintillator Detectors Using Convolutional Neural Networks
• Uncertainty quantification of an empirical shell-model interaction using principal component analysis
• Learning to Identify Electrons
• Uncertainty quantification in nuclear shell model
• Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks
• Neutrinoless Double Beta Decay with ${}^{82}$SeF${}_6$ and Direct Ion Imaging
• Detecting Chiral Magnetic Effect via Deep Learning
• Accelerating Coupled Cluster Calculations with Nonlinear Dynamics and Shallow Machine Learning
• Non-parametric Bayesian approach to extrapolation problems in configuration interaction methods
• Probing criticality with deep learning in relativistic heavy-ion collisions
• Classifying near-threshold enhancement using deep neural network
• Statistical error propagation in ab initio no-core full configuration calculations of light nuclei
• Bayesian approach to long-range correlations and multiplicity fluctuations in nucleus-nucleus collisions
• A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators
• Empirical physical formula for potential energy curves of 38-66Ti isotopes by using neural networks
• Bayesian inference on the isospin splitting of nucleon effective mass from giant resonances in ${}^{208}$Pb
• DeepRICH: Learning Deeply Cherenkov Detectors
• Optimization and Supervised Machine Learning Methods for Fitting Numerical Physics Models without Derivatives
• Cyclotron Radiation Emission Spectroscopy Signal Classification with Machine Learning in Project 8
• Machine learning spectral functions in lattice QCD
• Deep Learning Hamiltonian Monte Carlo
• Automatic trajectory recognition in Active Target Time Projection Chambers data by means of hierarchical clustering
• Machine Learning for Imaging Cherenkov Detectors
• Probing heavy ion collisions using quark and gluon jet substructure with machine learning
• A tagger for strange jets based on tracking information using long short-term memory
• Unsupervised Learning for Identifying Events in Active Target Experiments
• Bayesian Approach for Linear Optics Correction
• Morphology of three-body quantum states from machine learning
• Reconstruction of Storage Ring 's Linear Optics with Bayesian Inference
• Calibration of Energy Density Functionals with Deformed Nuclei
• Signal recognition efficiencies of artificial neural-network pulse-shape discrimination in HPGe $0\nu \beta \beta$-decay searches
• Machine-learning approach to finite-size effects in systems with strongly interacting fermions
• FastDIRC: a fast Monte Carlo and reconstruction algorithm for DIRC detectors
• Topological defects and confinement with machine learning: the case of monopoles in compact electrodynamics
• Statistical Global Modeling of Beta-Decay Halflives Systematics Using Multilayer Feedforward Neural Networks and Support Vector Machines
• Does Bayesian Model Averaging improve polynomial extrapolations? Two toy problems as tests
• Improvement Studies of an Effective Interaction for N=Z sd-shell Nuclei by Neural Networks
• Instantaneous stochastic perturbation theory
• Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator
• From noise to information: The transfer function formalism for uncertainty quantification on nuclear density reconstruction
• Generative models for sampling of lattice field theories
• Using machine learning to separate hadronic and electromagnetic interactions in the GlueX forward calorimeter
• Classical and Machine Learning Methods for Event Reconstruction in NeuLAND
• Identifying Heavy-Flavor Jets Using Vectors of Locally Aggregated Descriptors
• Advanced Control Methods for Particle Accelerators (ACM4PA) 2019 Workshop Report
• A Neural Network Perturbation Theory Based on the Born Series
• A Bayesian Analysis of Nuclear Deformation Properties with Skyrme Energy Functionals
• Relativistic Cyclotron Radiation Detection of Tritium Decay Electrons as a New Technique for Measuring the Neutrino Mass
• Online Bayesian Optimization for Beam Alignment in the SECAR Recoil Mass Separator

Cited by in corpus (60)

• Dense Nuclear Matter Equation of State from Heavy-Ion Collisions
• Controlling mass and energy diffusion with metamaterials
• Exploring QCD matter in extreme conditions with Machine Learning
• The Present and Future of QCD
• Nuclear mass predictions using machine learning models
• Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
• Nuclear charge radius predictions by kernel ridge regression with odd-even effects
• Deep-neural-network approach to solving the ab initio nuclear structure problem
• Deep learning bulk spacetime from boundary optical conductivity
• Revealing the nature of hidden charm pentaquarks with machine learning
• Decay of superheavy nuclei based on the random forest algorithm
• Machine learning-based jet and event classification at the Electron-Ion Collider with applications to hadron structure and spin physics
• Deep-learning quasi-particle masses from QCD equation of state
• Global prediction of nuclear charge density distributions using deep neural network
• Machine Learning for the Prediction of Converged Energies from Ab Initio Nuclear Structure Calculations
• Precise neural network predictions of energies and radii from the no-core shell model
• Principal components of nuclear mass models
• Nuclear mass predictions based on convolutional neural network
• Neural Network Emulation of Spontaneous Fission
• Constraining the Woods-Saxon potential in fusion reactions based on the neural network
• Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows
• Quantum Vision Transformers for Quark-Gluon Classification
• Optimization of generator coordinate method with machine-learning techniques for nuclear spectra and neutrinoless double-beta decay: ridge regression for nuclei with axial deformation
• Machine learning method for ${}^{12}$C event classification and reconstruction in the active target time-projection chamber
• Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models
• Deconstructing experimental decay energy spectra: the ${}^{26}$O case
• Efficient Solutions of Fermionic Systems using Artificial Neural Networks
• Machine learning method to determine concentrations of structural defects in irradiated materials
• Solving Schrodinger equations using physically constrained neural network
• Sign-Problem-Free Nuclear Quantum Monte Carlo Simulation
• Neural Network Emulation of Flow in Heavy-Ion Collisions at Intermediate Energies
• Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay Line Detectors
• Machine learning study to identify collective flow in small and large colliding systems
• Inference of Parameters for Back-shifted Fermi Gas Model using Feedback Neural Network
• Nuclear mass predictions based on deep neural network and finite-range droplet model (2012)
• Quantum many-body solver using artificial neural networks and its applications to strongly correlated electron systems
• Deep learning-based holography for T-linear resistivity
• Global Framework for Emulation of Nuclear Calculations
• Inclusive, prompt and non-prompt $\mathrm{J}/\psi$ identification in proton-proton collisions at the Large Hadron Collider using machine learning
• Estimation of collision centrality in terms of the number of participating nucleons in heavy-ion collisions using deep learning
• IMSRG-Net: A machine learning-based solver for In-Medium Similarity Renormalization Group
• First application of Markov Chain Monte Carlo-based Bayesian data analysis to the Doppler-Shift Attenuation Method
• Deep Learning the Forecast of Galactic Cosmic-Ray Spectra
• Deep learning in bifurcations of particle trajectories
• Machine learning the deuteron: new architectures and uncertainty quantification
• Particle Identification at VAMOS++ with Machine Learning Techniques
• Experimental Study of the ${}^{\text{38}}$S Excited Level Scheme
• Bayesian model mixing with multi-reference energy density functional
• Predictions of charge density distributions for nuclei with $Z\ge 8$
• Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversal
• Systematically Constructing the Likelihood for Boosted $H\to gg$ Decays
• Jet momentum reconstruction in the QGP background with machine learning
• A prototype neutron-detector array for future deep-underground s-process studies
• Microscopic derivation of the interacting boson model parameters with machine learning
• Identification and online monitoring of experimental measurement states via Cuscore statistic
• Applying Deep Learning Technique to Chiral Magnetic Wave Search
• Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
• Neural-network solution of subtracted three-body Faddeev integral equations near the Efimov limit
• Extraction of the color dipole amplitude with physics-informed neural networks
• Quantum-classical simulation of quantum field theory by quantum circuit learning