4.4 Review

AI for nuclear physics

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Physics, Multidisciplinary

Estimations of first 2+ energy states of even-even nuclei by using artificial neural networks

Serkan Akkoyun et al.

Summary: The first excited 2(+) energy states of nuclei provide important information related to nuclear structure, usually appearing in even-even nuclei with energy values increasing as closed shells are approached. Theoretical nuclear models and artificial neural networks can effectively study the energy levels and characteristics of excited states in nuclei.

INDIAN JOURNAL OF PHYSICS (2022)

Article Physics, Multidisciplinary

How machine learning conquers the unitary limit

Bastian Kaspschak et al.

Summary: Machine learning has become a key tool in physics and other scientific fields, allowing for solutions to quantum mechanical scattering problems and exploration of the unitary limit. The concept of unitary limit surfaces has been introduced to provide a geometric approach to investigating the unitary limit and handling systems with unnaturally large scattering lengths using standard multilayer perceptrons.

COMMUNICATIONS IN THEORETICAL PHYSICS (2021)

Article Physics, Nuclear

Revisiting Bayesian constraints on the transport coefficients of QCD

Jean-Francois Paquet

Summary: Multistage models based on relativistic viscous hydrodynamics have been successful in describing hadron measurements from relativistic nuclear collisions, with Bayesian analyses providing systematic constraints on the viscosities of QCD. This manuscript discusses recent developments in Bayesian analyses of heavy ion collision data, emphasizing the importance of closure tests and the role of emulators as proxies for multistage theoretical models. The ongoing Bayesian analysis of soft hadron measurements by the JETSCAPE Collaboration is used as context for the discussion.

NUCLEAR PHYSICS A (2021)

Article Physics, Multidisciplinary

Precision Determination of Pion-Nucleon Coupling Constants Using Effective Field Theory

P. Reinert et al.

Summary: The pion-nucleon coupling constants are fundamental observables that determine the strength of long-range nuclear forces and play a crucial role in nuclear physics research. Precision determination of these constants using chiral effective field theory and Bayesian methodology shows accurate values at the percent level with no significant charge dependence, marking an important step towards developing a precise theory of nuclear forces and structure.

PHYSICAL REVIEW LETTERS (2021)

Article Materials Science, Multidisciplinary

Machine learning to alleviate Hubbard-model sign problems

Jan-Lukas Wynen et al.

Summary: This paper demonstrates that neural networks can be trained to parametrize suitable manifolds for interacting systems with a sign problem, significantly reducing computational costs for small volume systems. The method is particularly effective in solving severe sign problems in nonbipartite systems like the tetrahedron Hubbard model.

PHYSICAL REVIEW B (2021)

Article Physics, Multidisciplinary

Pairing correlations and eigenvalues of two-body density matrix in atomic nuclei

Michelangelo Sambataro et al.

ANNALS OF PHYSICS (2020)

Article Physics, Multidisciplinary

Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8

A. Ashtari Esfahani et al.

NEW JOURNAL OF PHYSICS (2020)

Article Instruments & Instrumentation

Predicting particle accelerator failures using binary classifiers

Miha Rescic et al.

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT (2020)

Article Instruments & Instrumentation

Estimation of fusion reaction cross-sections by artificial neural networks

Serkan Akkoyun

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION B-BEAM INTERACTIONS WITH MATERIALS AND ATOMS (2020)

Article Physics, Nuclear

Towards high-order calculations of three-nucleon scattering in chiral effective field theory

E. Epelbaum et al.

EUROPEAN PHYSICAL JOURNAL A (2020)

Article Physics, Nuclear

Probing ab initio emergence of nuclear rotation

Mark A. Caprio et al.

EUROPEAN PHYSICAL JOURNAL A (2020)

Article Instruments & Instrumentation

AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case

E. Cisbani et al.

JOURNAL OF INSTRUMENTATION (2020)

Article Physics, Nuclear

Statistical aspects of nuclear mass models

V Kejzlar et al.

JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS (2020)

Article Physics, Nuclear

Machine learning for orders of magnitude speedup in multiobjective optimization of particle accelerator systems

Auralee Edelen et al.

PHYSICAL REVIEW ACCELERATORS AND BEAMS (2020)

Article Physics, Multidisciplinary

Clustering of Four-Component Unitary Fermions

William G. Dawkins et al.

PHYSICAL REVIEW LETTERS (2020)

Article Physics, Multidisciplinary

Taming Nuclear Complexity with a Committee of Multilayer Neural Networks

Raphael-David Lasseri et al.

PHYSICAL REVIEW LETTERS (2020)

Article Physics, Multidisciplinary

Equivariant Flow-Based Sampling for Lattice Gauge Theory

Gurtej Kanwar et al.

PHYSICAL REVIEW LETTERS (2020)

Article Astronomy & Astrophysics

Machine learning the deuteron

J. W. T. Keeble et al.

PHYSICS LETTERS B (2020)

Article Astronomy & Astrophysics

Spectral reconstruction with deep neural networks

Lukas Kades et al.

PHYSICAL REVIEW D (2020)

Article Astronomy & Astrophysics

Origin of single transverse-spin asymmetries in high-energy collisions

Justin Cammarota et al.

PHYSICAL REVIEW D (2020)

Article Physics, Nuclear

Quantified limits of the nuclear landscape

Leo Neufcourt et al.

PHYSICAL REVIEW C (2020)

Article Astronomy & Astrophysics

Machine-learning prediction for quasiparton distribution function matrix elements

Rui Zhang et al.

PHYSICAL REVIEW D (2020)

Article Physics, Nuclear

Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei

Leo Neufcourt et al.

PHYSICAL REVIEW C (2020)

Article Computer Science, Artificial Intelligence

DeepRICH: learning deeply Cherenkov detectors

Cristiano Fanelli et al.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2020)

Review Physics, Nuclear

r-process nucleosynthesis: connecting rare-isotope beam facilities with the cosmos

C. J. Horowitz et al.

JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS (2019)

Article Physics, Nuclear

Applications of deep learning to relativistic hydrodynamics

Hengfeng Huang et al.

NUCLEAR PHYSICS A (2019)

Article Physics, Multidisciplinary

Neutron Drip Line in the Ca Region from Bayesian Model Averaging

Leo Neufcourt et al.

PHYSICAL REVIEW LETTERS (2019)

Article Physics, Nuclear

Alpha half-lives calculation of superheavy nuclei with Qα-value predictions based on the Bayesian neural network approach

Ubaldo Banos Rodriguez et al.

JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS (2019)

Article Physics, Nuclear

Bayesian optimization in ab initio nuclear physics

A. Ekstrom et al.

JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS (2019)

Article Physics, Multidisciplinary

Bayesian Extraction of Jet Energy Loss Distributions in Heavy-Ion Collisions

Yayun He et al.

PHYSICAL REVIEW LETTERS (2019)

Article Physics, Multidisciplinary

Direct Comparison between Bayesian and Frequentist Uncertainty Quantification for Nuclear Reactions

G. B. King et al.

PHYSICAL REVIEW LETTERS (2019)

Article Physics, Multidisciplinary

Bayesian estimation of the specific shear and bulk viscosity of quark-gluon plasma

Jonah E. Bernhard et al.

NATURE PHYSICS (2019)

Article Physics, Multidisciplinary

Bayesian Evaluation of Incomplete Fission Yields

Zi-Ao Wang et al.

PHYSICAL REVIEW LETTERS (2019)

Article Physics, Nuclear

Model-independent tuning for maximizing free electron laser pulse energy

Alexander Scheinker et al.

PHYSICAL REVIEW ACCELERATORS AND BEAMS (2019)

Article Physics, Particles & Fields

Principal component analysis of collective flow in relativistic heavy-ion collisions

Ziming Liu et al.

EUROPEAN PHYSICAL JOURNAL C (2019)

Article Physics, Particles & Fields

Parton distributions with theory uncertainties: general formalism and first phenomenological studies NNPDF Collaboration

Rabah Abdul Khalek et al.

EUROPEAN PHYSICAL JOURNAL C (2019)

Article Instruments & Instrumentation

A new Transition Radiation detector based on GEM technology

F. Barbosa et al.

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT (2019)

Article Instruments & Instrumentation

Machine learning methods for track classification in the AT-TPC

M. P. Kuchera et al.

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT (2019)

Article Physics, Nuclear

Exploring Bayesian parameter estimation for chiral effective field theory using nucleon-nucleon phase shifts

S. Wesolowski et al.

JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS (2019)

Article Physics, Multidisciplinary

Global Sensitivity Analysis of Bulk Properties of an Atomic Nucleus

Andreas Ekstrom et al.

PHYSICAL REVIEW LETTERS (2019)

Article Astronomy & Astrophysics

Constraining the symmetry energy with heavy-ion collisions and Bayesian analyses

P. Morfouace et al.

PHYSICS LETTERS B (2019)

Article Physics, Particles & Fields

A machine learning study to identify spinodal clumping in high energy nuclear collisions

Jan Steinheimer et al.

JOURNAL OF HIGH ENERGY PHYSICS (2019)

Article Physics, Nuclear

Quantifying correlated truncation errors in effective field theory

J. A. Melendez et al.

PHYSICAL REVIEW C (2019)

Article Physics, Nuclear

Deep learning: Extrapolation tool for ab initio nuclear theory

Gianina Alina Negoita et al.

PHYSICAL REVIEW C (2019)

Article Physics, Nuclear

Exploring experimental conditions to reduce uncertainties in the optical potential

M. Catacora-Rios et al.

PHYSICAL REVIEW C (2019)

Article Astronomy & Astrophysics

Flow-based generative models for Markov chain Monte Carlo in lattice field theory

M. S. Albergo et al.

PHYSICAL REVIEW D (2019)

Proceedings Paper Physics, Nuclear

Constraining Fission Yields Using Machine Learning

Amy Lovell et al.

5TH INTERNATIONAL WORKSHOP ON NUCLEAR DATA EVALUATION FOR REACTOR APPLICATIONS (WONDER-2018) (2019)

Article Physics, Nuclear

Extrapolation of nuclear structure observables with artificial neural networks

W. G. Jiang et al.

PHYSICAL REVIEW C (2019)

Article Instruments & Instrumentation

Experimental test of an online ion-optics optimizer

A. M. Amthor et al.

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT (2018)

Review Physics, Nuclear

Parton distributions and lattice QCD calculations: A community white paper

Huey-Wen Lin et al.

PROGRESS IN PARTICLE AND NUCLEAR PHYSICS (2018)

Article Multidisciplinary Sciences

An equation-of-state-meter of quantum chromodynamics transition from deep learning

Long-Gang Pang et al.

NATURE COMMUNICATIONS (2018)

Article Physics, Particles & Fields

Energy flow polynomials: a complete linear basis for jet substructure

Patrick T. Komiske et al.

JOURNAL OF HIGH ENERGY PHYSICS (2018)

Article Physics, Multidisciplinary

Microscopic clustering in light nuclei

Martin Freer et al.

REVIEWS OF MODERN PHYSICS (2018)

Article Physics, Multidisciplinary

Fermions at Finite Density in 2+1 Dimensions with Sign-Optimized Manifolds

Andrei Alexandru et al.

PHYSICAL REVIEW LETTERS (2018)

Article Physics, Particles & Fields

A simple approach towards the sign problem using path optimisation

Francis Bursa et al.

JOURNAL OF HIGH ENERGY PHYSICS (2018)

Article Physics, Nuclear

Machine learning-based longitudinal phase space prediction of particle accelerators

C. Emma et al.

PHYSICAL REVIEW ACCELERATORS AND BEAMS (2018)

Article Physics, Nuclear

Constraining transfer cross sections using Bayes' theorem

A. E. Lovell et al.

PHYSICAL REVIEW C (2018)

Article Astronomy & Astrophysics

Machine learning action parameters in lattice quantum chromodynamics

Phiala E. Shanahan et al.

PHYSICAL REVIEW D (2018)

Article Astronomy & Astrophysics

Finite-density Monte Carlo calculations on sign-optimized manifolds

Andrei Alexandru et al.

PHYSICAL REVIEW D (2018)

Article Physics, Nuclear

Bayesian approach to model-based extrapolation of nuclear observables

Leo Neufcourt et al.

PHYSICAL REVIEW C (2018)

Article Physics, Nuclear

Uncertainty quantification in the nuclear shell model

Sota Yoshida et al.

PHYSICAL REVIEW C (2018)

Article Physics, Multidisciplinary

Ab initio Calculations of the Isotopic Dependence of Nuclear Clustering

Serdar Elhatisari et al.

PHYSICAL REVIEW LETTERS (2017)

Article Astronomy & Astrophysics

Toward solving the sign problem with path optimization method

Yuto Mori et al.

PHYSICAL REVIEW D (2017)

Review Physics, Nuclear

GPD phenomenology and DVCS fitting

Kresimir Kumericki et al.

EUROPEAN PHYSICAL JOURNAL A (2016)

Article Physics, Nuclear

Nuclear charge radii: density functional theory meets Bayesian neural networks

R. Utama et al.

JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS (2016)

Article Physics, Multidisciplinary

Nuclear Binding Near a Quantum Phase Transition

Serdar Elhatisari et al.

PHYSICAL REVIEW LETTERS (2016)

Review Physics, Multidisciplinary

Microscopic theory of nuclear fission: a review

N. Schunck et al.

REPORTS ON PROGRESS IN PHYSICS (2016)

Article Physics, Nuclear

Subleading harmonic flows in hydrodynamic simulations of heavy ion collisions

Aleksas Mazeliauskas et al.

PHYSICAL REVIEW C (2015)

Article Physics, Multidisciplinary

Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements

J. D. McDonnell et al.

PHYSICAL REVIEW LETTERS (2015)

Article Physics, Multidisciplinary

Principal Component Analysis of Event-by-Event Fluctuations

Rajeev S. Bhalerao et al.

PHYSICAL REVIEW LETTERS (2015)

Article Physics, Multidisciplinary

Constraining the Equation of State of Superhadronic Matter from Heavy-Ion Collisions

Scott Pratt et al.

PHYSICAL REVIEW LETTERS (2015)

Article Physics, Nuclear

Nuclear energy density optimization: Shell structure

M. Kortelainen et al.

PHYSICAL REVIEW C (2014)

Article Physics, Nuclear

Innovative applications of genetic algorithms to problems in accelerator physics

Alicia Hofler et al.

PHYSICAL REVIEW SPECIAL TOPICS-ACCELERATORS AND BEAMS (2013)

Article Physics, Particles & Fields

Consistent Empirical Physical Formulas for Potential Energy Curves of 38-66Ti Isotopes by Using Neural Networks

S. Akkoyun et al.

PHYSICS OF PARTICLES AND NUCLEI LETTERS (2013)

Article Physics, Nuclear

Simultaneous optimization of beam emittance and dynamic aperture for electron storage ring using genetic algorithm

Weiwei Gao et al.

PHYSICAL REVIEW SPECIAL TOPICS-ACCELERATORS AND BEAMS (2011)

Article Physics, Particles & Fields

Neural network generated parametrizations of deeply virtual Compton form factors

Kresimir Kumericki et al.

JOURNAL OF HIGH ENERGY PHYSICS (2011)

Article Physics, Nuclear

Multivariate optimization of a high brightness dc gun photoinjector

Ivan V. Bazarov et al.

PHYSICAL REVIEW SPECIAL TOPICS-ACCELERATORS AND BEAMS (2005)