Conditional MAFs interpolate QCD chiral phase structure across coupling, mass, and volume, reproducing reweighting while cutting required ensembles despite bias near transitions.
Canonical reference
Zhouet al., Progress in Particle and Nuclear Physics 135, 104084 (2024), 2303.15136
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Deep learning extracts a unified in-medium heavy quark potential from multi-energy bottomonium data, finding the real part close to vacuum Cornell form with weak screening while the imaginary part dominates suppression.
A physics-guided neural network embedding AdS5 Dirac equation and holographic Pomeron fits SLAC proton F2 data with chi-squared per degree of freedom of 0.91 and identifies a kinematic crossover at x approximately 0.19 while recovering Pomeron intercept of 1.0786.
Physics-informed neural networks extract a model-independent color dipole amplitude from inclusive HERA data that predicts exclusive J/ψ photoproduction cross-sections without parameter retuning.
A flow-matching generative model trained on CoLBT-hydro data conditionally generates marginal final-state hadron spectra from jet-induced hydro responses in 0-10% Pb+Pb collisions at 5.02 TeV, matching training data statistics with approximately six orders of magnitude computational speedup.
A physics-informed Bayesian neural network learns neutron-star equations of state from theoretical priors and constraints, then generates posterior mass-radius and mass-tidal-deformability distributions consistent with NICER radii and 2-solar-mass limits.
A PINN-trained quasi-parton model reproduces lattice cumulants at vanishing chemical potentials and supplies a consistent four-dimensional QCD equation of state at finite densities.
Radiative corrections applied to MINERvA antineutrino data yield updated values for the nucleon axial-vector form factor G_A and axial radius.
A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.
citing papers explorer
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Testing machine-learned distributions against Monte Carlo data for the QCD chiral phase transition
Conditional MAFs interpolate QCD chiral phase structure across coupling, mass, and volume, reproducing reweighting while cutting required ensembles despite bias near transitions.
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Unified Extraction of In-Medium Heavy Quark Potentials from RHIC to LHC Energies via Deep Learning
Deep learning extracts a unified in-medium heavy quark potential from multi-energy bottomonium data, finding the real part close to vacuum Cornell form with weak screening while the imaginary part dominates suppression.
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Probing Proton Structure via Physics-Guided Neural Networks in Holographic QCD
A physics-guided neural network embedding AdS5 Dirac equation and holographic Pomeron fits SLAC proton F2 data with chi-squared per degree of freedom of 0.91 and identifies a kinematic crossover at x approximately 0.19 while recovering Pomeron intercept of 1.0786.
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Extraction of the color dipole amplitude with physics-informed neural networks
Physics-informed neural networks extract a model-independent color dipole amplitude from inclusive HERA data that predicts exclusive J/ψ photoproduction cross-sections without parameter retuning.
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A flow-matching generative model for event-by-event jet-induced hydro response in high-energy heavy-ion collisions
A flow-matching generative model trained on CoLBT-hydro data conditionally generates marginal final-state hadron spectra from jet-induced hydro responses in 0-10% Pb+Pb collisions at 5.02 TeV, matching training data statistics with approximately six orders of magnitude computational speedup.
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A Physics Informed Bayesian Neural Network for the Neutron Star Equation of State
A physics-informed Bayesian neural network learns neutron-star equations of state from theoretical priors and constraints, then generates posterior mass-radius and mass-tidal-deformability distributions consistent with NICER radii and 2-solar-mass limits.
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Four-dimensional QCD equation of state from a quasi-parton model with physics-informed neural networks
A PINN-trained quasi-parton model reproduces lattice cumulants at vanishing chemical potentials and supplies a consistent four-dimensional QCD equation of state at finite densities.
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Nucleon axial-vector form factor and radius from radiatively-corrected antineutrino scattering data
Radiative corrections applied to MINERvA antineutrino data yield updated values for the nucleon axial-vector form factor G_A and axial radius.
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Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective
A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.