Training, validation and posterior datasets for Bayesian calibration of the Thermal Blast-Wave model

This dataset contains the training and validation data used for constructing Gaussian-process emulators, together with posterior samples obtained from the Bayesian parameter estimation presented in the associated paper:Oleksandr Vitiuk, David Blaschke, Benjamin Dönigus, and Gerd Röpke. Nonequilibrium phenomenology of identified particle spectra in heavy-ion collisions at energies available at the CERN Large Hadron Collider. Phys. Rev. C 113, 044902 (2026). https://doi.org/10.1103/db8g-55dw The dataset is organised into two independent model scenarios: Without non-equilibrium pion chemical potential ($\mu_\pi = 0$), With non-equilibrium pion chemical potential ($\mu_\pi$ treated as a free model parameter). For each scenario, the dataset provides the design points used to generate emulator training and validation data with the ThermalBlastMC code, the corresponding model predictions, the prior parameter ranges used in the Bayesian analysis, and posterior samples obtained from the final Markov-chain Monte Carlo analysis. All files are stored in CSV format and can be read directly by standard software such as pandas, NumPy, R, MATLAB or spreadsheet applications. Please consult the provided README file to familiarise yourself with the conventions and repository structure before using this dataset. This research is part of the project No. 2021/43/P/ST2/03319 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339. For the purpose of Open Access, the author has applied a CC-BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission. This research was funded in whole or in part by the National Science Centre, Poland, under Grant No. 2022/45/N/ST2/02391. For the purpose of Open Access, the author has applied a CC-BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission.