Department of High Energy Physics, Wigner RCP
One of the main challenges in high energy heavy-ion collisions is to simultaneously determine the temperature and the energy density of the matter produced in a collision, and hence the number of thermodynamic degrees of freedom (DOF). We presented the extraction of the temperature by analyzing the charged particle transverse momentum spectra in lead-lead and proton-proton collisions at LHC energies from the ALICE Collaboration using the Color String Percolation Model (CSPM). From the measured energy density (ε) and the temperature (T) the dimensionless quantity ε/T4 is obtained to get the degrees of freedom of the deconfined phase. We observe for the first time a two-step behavior in the increase of DOF, characteristic of deconfinement, above the hadronization temperature, at a temperature ~210 MeV for both Pb-Pb and pp collisions, and a sudden increase of the DOF to the ideal gas value of ~47 corresponding to three quark flavors in the case of Pb-Pb collisions [1].
We implement a machine-learning-based regression technique via boosted decision trees to obtain a prediction of impact parameter and transverse spherocity in PbPb collisions at the LHC energies using AMPT model. We obtain the predictions for centrality dependent spherocity distributions from the training of minimum bias simulated data and find that the predictions from BDTs based ML technique matches with true simulated data. In the absence of experimental measurements, we propose to implement the machine learning based regression technique to obtain transverse spherocity from the known final state quantities in heavy-ion collisions [2].
We evaluated the effect of nuclear density profile on the global properties in O+O collisions at the Large Hadron Collider using a multi-phase transport model [3].
[1] Mishra A N, Eur. Phys. J. A 57 (2021) 245.
[2] Mallick N, Tripathy S, Mishra A N, Deb S, Sahoo R, Phys. Rev. D 103 (2021) 094031
[3] Behera D et al. incl. Mishra A N,arXiv:2110.04016 [hep-ph]