Neural network maximum entropy framework for distribution reconstruction in heavy-ion collisions
We develop a neural-network maximum-entropy (NN+MaxEnt) framework for reconstructing probability distributions from limited observables in heavy-ion collisions. The method combines flexible neural-network representations with Shannon-entropy regularization, preserving positivity and normalization without assuming a fixed analytic form. After validation with Gaussian, Poisson, and mixed-Poisson closure tests, we apply the framework to two physics-motivated inverse problems: an effective multiplicity reconstruction constrained by functional renormalization group cumulants, used as a closure test, and the conditional jet-energy-loss distribution extracted from single-inclusive jet $R_{AA}$ data in Pb+Pb collisions at $\sqrt{s_{NN}}=2.76$~TeV. For the fRG closure test, NN+MaxEnt accurately reproduces the imposed cumulants and yields distributions consistent with conventional MaxEnt solutions. For jets, the reconstructed energy-loss distributions reproduce the measured $R_{AA}$; at an initial jet momentum $x=50~\mathrm{GeV}$, the conditional mean energy loss is $\langle\Delta p_T\rangle\simeq11.8~\mathrm{GeV}$, with a central $16\text{--}84\%$ interval of $9.0\text{--}15.0~\mathrm{GeV}$. The extracted energy-loss profile is qualitatively consistent with Bayesian MCMC and LBT results. NN+MaxEnt thus provides a flexible, less ansatz-dependent framework for regularized distribution reconstruction from observables connected to the underlying distribution through differentiable forward maps.