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Bayesian optimization in ab initio nuclear physics
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Alpha half-lives calculation of superheavy nuclei with Q α -value predictions based on the Bayesian neural network approach
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Statistical aspects of nuclear mass models
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Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8
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Deep neural networks for energy and position reconstruction in EXO-200
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AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case
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DeepRICH: learning deeply Cherenkov detectors
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Model-independent tuning for maximizing free electron laser pulse energy
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Extrapolation of nuclear structure observables with artificial neural networks
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Quantified limits of the nuclear landscape
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Propagation of statistical uncertainties of Skyrme mass models to simulations of r -process nucleosynthesis
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Nonparametric Bayesian approach to extrapolation problems in configuration interaction methods
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Subleading harmonic flows in hydrodynamic simulations of heavy ion collisions
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Refining mass formulas for astrophysical applications: A Bayesian neural network approach
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Data-driven analysis for the temperature and momentum dependence of the heavy-quark diffusion coefficient in relativistic heavy-ion collisions
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Constraining transfer cross sections using Bayes' theorem
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Bayesian approach to model-based extrapolation of nuclear observables
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Uncertainty quantification in the nuclear shell model
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Deep learning: Extrapolation tool for ab initio nuclear theory
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Predictions of nuclear β -decay half-lives with machine learning and their impact on r -process nucleosynthesis
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Flow-based generative models for Markov chain Monte Carlo in lattice field theory
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Machine-learning prediction for quasiparton distribution function matrix elements
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Strange quark suppression from a simultaneous Monte Carlo analysis of parton distributions and fragmentation functions
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Origin of single transverse-spin asymmetries in high-energy collisions
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Spectral reconstruction with deep neural networks
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Machine learning action parameters in lattice quantum chromodynamics
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Finite-density Monte Carlo calculations on sign-optimized manifolds
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Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber
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Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements
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Principal Component Analysis of Event-by-Event Fluctuations
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Nuclear Binding Near a Quantum Phase Transition
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First Simultaneous Extraction of Spin-Dependent Parton Distributions and Fragmentation Functions from a Global QCD Analysis
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Ab initio Calculations of the Isotopic Dependence of Nuclear Clustering
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Fermions at Finite Density in 2 + 1 Dimensions with Sign-Optimized Manifolds
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Neutron Drip Line in the Ca Region from Bayesian Model Averaging
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Direct Comparison between Bayesian and Frequentist Uncertainty Quantification for Nuclear Reactions
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Bayesian Extraction of Jet Energy Loss Distributions in Heavy-Ion Collisions
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Bayesian Evaluation of Incomplete Fission Yields
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Global Sensitivity Analysis of Bulk Properties of an Atomic Nucleus
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Clustering of Four-Component Unitary Fermions
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Equivariant Flow-Based Sampling for Lattice Gauge Theory
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Precision Determination of Pion-Nucleon Coupling Constants Using Effective Field Theory
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Microscopic clustering in light nuclei
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Optimum steering of photon beamlines in SPEAR
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GPD phenomenology and DVCS fitting: Entering the high-precision era
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Parton distributions with theory uncertainties: general formalism and first phenomenological studies: NNPDF Collaboration
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Bayesian Neural Network improvements to nuclear mass formulae and predictions in the SuperHeavy Elements region
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Bayesian extraction of $\hat{q}$ with multi-stage jet evolution approach
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April 2019