Speaker
Description
The nuclear equation of state (EoS) describes the properties of dense nuclear matter, governing the behavior of nuclei, neutron stars, and energetic astrophysical phenomena.
However, our knowledge of the EoS at high densities remains limited due to the lack of direct experimental constraints.
This study combines the analysis of heavy-ion collision data with Bayesian inference to provide improved constraints on the nuclear EoS. Two phenomenological models, ELIE and HIPSE, are employed. A precise machine learning-based method has been developed to reconstruct the centrality (impact parameter) of Ni+Ni and Xe+Sn collisions from INDRA data in the energy range of 32 to 100 MeV/nucleon, enabling selection of the most central, potentially supra-saturation density events. Bayesian inference is then performed on these central collisions to extract constraints on the nucleon-nucleon cross-section and the nuclear incompressibility in the Fermi energy domain.
The extracted nucleon-nucleon cross-sections are compared against theoretical predictions and phenomenological analyses by D. Coupland & al. (PRC 84, 054603 (2011)) and M. Henry & al. (PRC 101, 064622 (2020)). The incompressibility modulus, a key parameter governing the stiffness of the EoS at high densities, is benchmarked against the compilation of theoretical model values by J. Margueron (PRC 97, 025805 (2018) and PRC 97, 025806 (2018)). This allows us to constrain the maximum densities reached in the heavy-ion collisions.
These empirical comparisons and constraints pave the way toward an accurate determination of the nuclear EoS, crucial for modeling extreme astrophysical phenomena such as neutron stars and supernovae. Our results establish heavy-ion collisions as a powerful terrestrial probe of the high-density EoS, shedding light on fundamental nuclear physics and astrophysics.