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Nuclear Theory·nucl-th

Thursday·July 9, 2026

14 papers—4 primary·10 cross-listed

All14Primary4Cross-lists10
  1. 01

    Kaon-deuteron correlation function from an effective field theory approach

    Juan Torres-Rincon · Àngels Ramos

    We present a study of femtoscopic correlation functions for and pairs, and compare our results with recent measurements by the ALICE Collaboration in both Pb-Pb and high-multiplicity collisions. The kaon-deuteron wave functions are derived from scattering amplitudes using a unitarized chiral effective theory model describing the elementary interactions of mesons with nucleons. We then evaluate the strong scattering amplitudes by solving the Faddeev equations within two distinct frameworks: the Impulse Approximation and the Fixed Center Approximation, which accounts for multiple scatterings. We also incorporate the long-range Coulomb effects between the kaon and the deuteron. We show that the correlation function exhibits large sensitivity to both the size of the emitting source and the relative momentum of the pair, being heavily influenced by rescattering processes. In contrast, the correlation function is dominated by the weakly repulsive interaction, showing deviations from purely Coulombic behavior only at small emission source sizes. Our predictions are in agreement with the ALICE experimental data, and also with the energy-shift and width of the level of the kaonic deuterium preliminary results from the SIDDHARTA 2 Collaboration.

    nucl-thhep-phActa Phys.Polon.Supp.(2026)·0 citations
  2. 02

    Machine learning the impact parameter in heavy-ion collisions at = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM

    Xiaoqing Yue🇨🇳 · Guojun Wei🇨🇳 · Yongjia Wang🇨🇳 · Zhilong Li🇨🇳 · Pengcheng Li🇨🇳 · Haojie Xu🇨🇳 · Xiangrong Zhu🇨🇳 · Qingfeng Li🇨🇳 · Fuhu Liu🇨🇳 · Yasushi Nara🇨🇳

    By generating heavy-ion collision data with the ultrarelativistic quantum molecular dynamics (UrQMD) model, a multiphase transport (AMPT) model, and the JAM model, the impact parameter () in Au+Au collisions at = 4 and 11 GeV is reconstructed using supervised learning and unsupervised learning in machine learning (ML). In supervised learning, the performance of ML algorithm is cross-checked by using data obtained from these three transport models. It is found that the typical mean absolute error (MAE) which measures the average magnitude of the absolute difference between the true and predicted is between 0.2-0.4 fm, even when training ML algorithm with data generated from one model but testing with data from others. While the conventional method (i.e., a polynomial fit to multiplicity as a function of ) only works for data generated from the same model. In the classification task, the present ML-based method also shows significantly superior results compared to the traditional approach. In unsupervised learning, the K-means clustering algorithm is used to partition collision events directly from experimental-style observables, showing that the algorithm autonomously identifies six clusters corresponding to different centrality classes without relying on predefined model-based binning. Our study demonstrates the strong robustness of using an ML algorithm trained on transport-model data for impact-parameter determination, and indicates that this method has the potential to be generalized to handle real experimental data.

    nucl-thPRC(2026)·1 citation
  3. 03

    Hadronic and partonic composition of QCD matter across the crossover

    Artemiy Lysenko🇺🇦 · Mark I. Gorenstein🇺🇦 · Marek Gazdzicki🇵🇱 · Roman Poberezhniuk🇺🇦 · Volodymyr Vovchenko🇺🇸

    We construct a simple equation of state of strongly interacting matter at zero chemical potentials that provides a unified description of lattice QCD thermodynamics in terms of hadronic and partonic degrees of freedom. The hadronic phase is described by the quantum van der Waals hadron resonance gas, extended by excluded-volume repulsion between mesons, while the quark-gluon plasma is modeled as an ideal gas of quarks and gluons supplemented with a phenomenological interaction term proportional to . The two regimes are connected by a smooth crossover switching function. The three model parameters - the meson hard-core radius, the strength of the partonic interaction term, and the switching temperature - are determined from a fit to lattice QCD results for the trace anomaly. The resulting equation of state reproduces the lattice data on the pressure, entropy density, energy density, and speed of sound in the temperature range - MeV. The fit yields a meson hard-core radius fm, a partonic interaction scale MeV, and a switching temperature MeV, substantially exceeding both the pseudocritical temperature of the QCD chiral crossover and the chemical freeze-out temperature. This finding suggests that the transition from hadronic to partonic degrees of freedom is considerably more gradual than indicated by the chiral pseudocritical temperature alone, with hadronic states remaining an important component of strongly interacting matter up to temperatures of about MeV, well above the QCD chiral crossover.

    nucl-thhep-ph0 citations
  4. 04

    Extracting Barrier Distributions from Fusion Cross Sections

    Aaron Philip

    Studying fusion cross sections provides insight into the fusion process, details about the internal structure of heavier nuclear systems, and a window into astrophysical processes. Barrier distributions, extracted from fusion excitation functions, are immensely useful for comparing theoretical model predictions and experimental results. Extracting this barrier distribution from the measured cross-section data amounts to taking the second derivative of the energy-weighted cross section. In practice, barrier distributions are highly sensitive to the quality of collected experimental data and the choice of step size when using standard point difference schemes. In this work, we explore Bayesian methods for extracting a posterior distribution over barrier distributions that could reasonably describe experimental data. We benchmark Gaussian processes and recently developed Bayesian machine learning inference algorithms against realistic simulated data generated from a simple model of fusion excitation functions. We find that Gaussian processes often exhibit aliasing at higher energies of the barrier distribution. We demonstrate that the BNN architectures can more faithfully recover the barrier distribution with quantified uncertainties at all energies, while also identifying key regions of high uncertainty and model discrepancy to determine precisely where additional experiments would be maximally impactful. We use our conclusions to calibrate models to measured experimental data. All methods are comparatively robust to data sparsity and irregularity, but we find that the single most important factor dictating the fidelity of all models is the relative size of experimental uncertainties. We release an open-source version of our analysis and a user-friendly implementation of our method to encourage its future usage for experimental analysis.

    nucl-th1 citation

Affiliations

first authorsco-authorsvia INSPIRE