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Artificial intelligence in heavy-ion collisions : bridging the gap between theory and experiments
(2023)
Artificial Intelligence (AI) methods are employed to study heavy-ion collisions at intermediate collision energies, where high baryon density and moderate temperature QCD matter is produced. The experimental measurements of various conventional observables such as collective flow, particle number fluctuations, etc. are usually compared with expensive model calculations to infer the physics governing the evolution of the matter produced in the collisions. Various experimental effects and processing algorithms can greatly affect the sensitivity of these observables. AI methods are used to bridge this gap between theory and experiments of heavy-ion collisions. The problems with conventional methods of analyzing experimental data are illustrated in a comparative study of the Glauber MC model and the UrQMD transport model. It is found that the centrality determination and the estimated fluctuations of the number of participant nucleons suffer from strong model dependencies for Au-Au collisions at 1.23 AGeV. This can bias the results of the experimental analysis if the number of participant nucleons used is not consistent throughout the analysis and in the final model-to-data comparison. The measurable consequences of this model dependence of the number of participant nucleons are also discussed. In this context, PointNet-based AI models are developed to accurately reconstruct the impact parameter or the number of participant nucleons in a collision event from the hits and/or reconstructed track of particles in 10 AGeV Au-Au collisions at the CBM experiment. In the last part of the thesis, different AI methods to study the equation of state (EoS) at high baryon densities are discussed. First, a Bayesian inference is performed to constrain the density dependence of the EoS from the available experimental measurements of elliptical flow and mean transverse kinetic energy of mid rapidity protons in intermediate energy collisions. The UrQMD model was augmented to include arbitrary potentials (or equivalently the EoSs) in the QMD part to provide a consistent treatment of the EoS throughout the evolution of the system. The experimental data constrain the posterior constructed for the EoS for densities up to four times saturation density. However, beyond three times saturation density, the shape of the posterior depends on the choice of observables used. There is a tension in the measurements at a collision energy of about 4 GeV. This could indicate large uncertainties in the measurements, or alternatively the inability of the underlying model to describe the observables with a given input EoS. Tighter constraints and fully conclusive statements on the EoS require accurate, high statistics data in the whole beam energy range of 2-10 GeV, which will hopefully be provided by the beam energy scan programme of STAR-FXT at RHIC, the upcoming CBM experiment at FAIR, and future experiments at HIAF and NICA. Finally, it is shown that the PointNet-based models can also be used to identify the equation of state in the CBM experiment. Despite the uncertainties due to limited detector acceptance and biases in the reconstruction algorithms, the PointNet-based models are able to learn the features that can accurately identify the underlying physics of the collision. The PointNet-based models are an ideal AI tool to study heavy-ion collisions, not only to identify the geometric event features, such as the impact parameter or the number of participant nucleons, but also to extract abstract physical features, such as the EoS, directly from the detector outputs.
This thesis presents various algorithms which have been developed for on-line event reconstruction in the CBM experiment at GSI, Darmstadt and the ALICE experiment at CERN, Geneve. Despite the fact that the experiments are different — CBM is a fixed target experiment with forward geometry, while ALICE has a typical collider geometry — they share common aspects when reconstruction is concerned.
The thesis describes:
— general modifications to the Kalman filter method, which allows one to accelerate, to improve, and to simplify existing fit algorithms;
— developed algorithms for track fit in CBM and ALICE experiment, including a new method for track extrapolation in non-homogeneous magnetic field.
— developed algorithms for primary and secondary vertex fit in the both experiments. In particular, a new method of reconstruction of decayed particles is presented.
— developed parallel algorithm for the on-line tracking in the CBM experiment.
— developed parallel algorithm for the on-line tracking in High Level Trigger of the ALICE experiment.
— the realisation of the track finders on modern hardware, such as SIMD CPU registers and GPU accelerators.
All the presented methods have been developed by or with the direct participation of the author.
The equation of state (EoS) of matter at extremely high temperatures and densities is currently not fully understood, and remains a major challenge in the field of nuclear physics. Neutron stars harbor such extreme conditions and therefore serve as celestial laboratories for constraining the dense matter EoS. In this thesis, we present a novel algorithm that utilizes the idea of Bayesian analysis and the computational efficiency of neural networks to reconstruct the dense matter equation of state from mass-radius observations of neutron stars. We show that the results are compatible with those from earlier works based on conventional methods, and are in agreement with the limits on tidal deformabilities obtained from the gravitational wave event, GW170817. We also observe that the resulting squared speed of sound from the reconstructed EoS features a peak, indicating a likely convergence to the conformal limit at asymptotic densities, as expected from quantum chromodynamics. The novel algorithm can also be applied across various fields faced with computational challenges in solving inverse problems. We further examine the efficiency of deep learning methods for analyzing gravitational waves from compact binary coalescences in this thesis. In particular, we develop a deep learning classifier to segregate simulated gravitational wave data into three classes: signals from binary black hole mergers, signals from binary neutron star mergers, or white noise without any signals. A second deep learning algorithm allows for the regression of chirp mass and combined tidal deformability from simulated binary neutron star mergers. An accurate estimation of these parameters is crucial to constrain the underlying EoS. Lastly, we explore the effects of finite temperatures on the binary neutron star merger remnant from GW170817. Isentropic EoSs are used to infer the frequencies of the rigidly rotating remnant and are noted to be significantly lower compared to previous estimates from zero temperature EoSs. Overall, this thesis presents novel deep learning methods to constrain the neutron star EoS, which will prove useful in future, as more observational data is expected in the upcoming years.