Abstract
A novel method for identifying the nature of QCD transitions in heavy-ion collision experiments is introduced. PointNet based Deep Learning (DL) models are developed to classify the equation of state (EoS) that drives the hydrodynamic evolution of the system created in Au-Au collisions at 10 AGeV. The DL models were trained and evaluated in different hypothetical experimental situations. A decreased performance is observed when more realistic experimental effects (acceptance cuts and decreased resolutions) are taken into account. It is shown that the performance can be improved by combining multiple events to make predictions. The PointNet based models trained on the reconstructed tracks of charged particles from the CBM detector simulation discriminate a crossover transition from a first order phase transition with an accuracy of up to 99.8%. The models were subjected to several tests to evaluate the dependence of its performance on the centrality of the collisions and physical parameters of fluid dynamic simulations. The models are shown to work in a broad range of centralities (b=0–7 fm). However, the performance is found to improve for central collisions (b=0–3 fm). There is a drop in the performance when the model parameters lead to reduced duration of the fluid dynamic evolution or when less fraction of the medium undergoes the transition. These effects are due to the limitations of the underlying physics and the DL models are shown to be superior in its discrimination performance in comparison to conventional mean observables.
Highlights
CBM experiment includes the exploration of high density equation of states such as in neutron star cores and the search for phase transitions at finite baryon densities [9, 10]
PointNet based Deep Learning (DL) models are developed to classify the equation of state (EoS) that drives the hydrodynamic evolution of the system created in Au-Au collisions at 10 AGeV
We have developed PointNet based DL models that can extract very complex universal event features from basic event information of heavy ion collisions at the CBM
Summary
The CBM detector is designed to make fast and precise measurements of the hadrons, muons and electrons produced in nucleus-nucleus collisions. The experiment will exploit modern radiation hard detectors with self triggered read out electronics to achieve the desired performance. Among the key components of the CBM experiment are the Silicon Tracking System (STS) [21] and Micro Vertex Detector (MVD) [22] which are placed inside a superconducting dipole magnet with a magnetic field integral of 1 Tm. The MVD consists of 4 layers of Monolithic Active Pixel Sensors (MAPS) placed 5–20 cm downstream the target. The main purpose of the MVD is to reconstruct open charm decay vertices and has an excellent position resolution of 3.5–6 μm and secondary vertex resolution of about 50 μm. The STS comprises of 8 layers of silicon microstrip sensors placed 30–100 cm downstream the target. In this study we consider the data only from STS and MVD for the analyses
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