Within the framework of the exploration of the phase diagram of nuclear matter, the susceptibilities are useful tools to probe the existence of a 1st order phase transition and a possible critical endpoint. In this context, STAR collaboration recently published some results of variances and 2nd order susceptibility ratios for electric charge (Q), protons and kaons (the last 2 being used as proxies for baryonic number B and strangeness S). Hence, we plan to simulate Au+Au collisions with the event generator EPOS, to reproduce STAR analyses and, in particular, study the impact of hadronisation and hadronic cascades on these quantities. We show here our first results for some BES program reactions, obtained with a preliminary version of EPOS 4 using the latest equation of state from the BEST collaboration.

Powerful accelerators make head on collisions between massive ions to explore the phase diagram of nuclear matter. The Large Hadron Collider (LHC) at CERN is the world’s largest and most powerful particle accelerator. Inside the accelerator, collisions occur between two high-energy particle beams composed mainly of protons and lead nuclei. ALICE (A Large Ion Collider Experiment) is the experiment designed for the study of heavy-ion collisions at the LHC. Presently, the LHC is in a period known as the long shutdown, during which an upgrade of the detectors takes place to prepare for the next run (RUN 3), scheduled in 2022 until 2024. In order to improve the ALICE experiment, a new detector, the Muon Forward Tracker (MFT), is going to be installed near the interaction point to complement the current muon spectrometer. Measurements in the muon spectrometer are affected by the presence of a thick absorber to shield the spectrometer from background. The MFT, placed in front of this absorber, will increase the physics potential of the muon spectrometer and allow new measurements of general interest for the whole ALICE physics. To further understand the physics motivations and impact of the MFT, the detector’s hardware and software implementations will be presented, with an emphasis on the current commissioning phase. Analyses tools based on Machine Learning, for RUN 3 data, will be introduced.