Measurement of K(892)*0 resonance production in Pb-Pb collisions with the ALICE experiment at the LHC

The analysis of the K(892)*0 resonance production in Pb–Pb collisions at √sNN = 2.76 TeV with the ALICE detector at the LHC is presented. The analysis is motivated by the interest in the measurement of short-lived resonances production that can provide insights on the properties of the medium produced in heavy-ion collisions both during its partonic (Quark-Gluon Plasma) and hadronic phase. This particular analysis exploits particle identification of the ALICE Time-Of-Flight detector. The ALICE experiment is presented, with focus on the performance of the Time-Of-Flight system. The aspects of calibration and data quality controls are discussed in detail, while illustrating the excellent and very stable performance of the system in different collision environments at the LHC. A full analysis of the K*0 resonance production is presented: from the resonance reconstruction to the determination of the efficiency and the systematic uncertainty. The results show that the analysis strategy discussed is a valid tool to measure the K∗0 up to intermediate momenta. Preliminary results on K*0 resonance production at the LHC are presented and confirmed to be a powerful tool to study the physics of ultra-relativistic heavy-ion collisions.

Vector meson photoproduction in ultra-peripheral heavy ion collisions with ALICE at the LHC

Ultra-relativistic heavy ions generate strong electromagnetic fields which offer the possibility to study γ-γ and γ-nucleus processes at the LHC in the so called ultra-peripheral collisions (UPC). The photoproduction of J/ψ vector mesons in UPC is sensitive to the gluon distribution of the interacting nuclei. In this thesis the study of coherent and incoherent J/ψ production in Pb-Pb collisions at √sNN = 2.76 TeV is described. The J/ψ has been measured via its leptonic decay in the rapidity range -0.9 < y < 0.9. The cross section for coherent and incoherent J/ψ are given. The results are compared to theoretical models for J/ψ production and the coherent cross section is found to be in good agreement with those models which include nuclear gluon shadowing consistent with EPS09 parametrization. In addition the cross section for the process γ γ→ e+e− has been measured and found to be in agreement with the STARLIGHT Monte Carlo predictions. The analysis has been published by the ALICE Collaboration in the European Physical Journal C, with one of its main plot depicted on the cover-front of the November 2013 issue.

Directly training spiking neural networks for cyber-physical systems: from supervised to reinforcement learning

Spiking Neural Networks (SNNs) are bio-inspired Artificial Neural Networks (ANNs) utilizing discrete spiking signals, akin to neuron communication in the brain, making them ideal for real-time and energy-efficient Cyber-Physical Systems (CPSs). This thesis explores their potential in Structural Health Monitoring (SHM), leveraging low-cost MEMS accelerometers for early damage detection in motorway bridges. The study focuses on Long Short-Term SNNs (LSNNs), although their complex learning processes pose challenges. Comparing LSNNs with other ANN models and training algorithms for SHM, findings indicate LSNNs' effectiveness in damage identification, comparable to ANNs trained using traditional methods. Additionally, an optimized embedded LSNN implementation demonstrates a 54% reduction in execution time, but with longer pre-processing due to spike-based encoding. Furthermore, SNNs are applied in UAV obstacle avoidance, trained directly using a Reinforcement Learning (RL) algorithm with event-based input from a Dynamic Vision Sensor (DVS). Performance evaluation against Convolutional Neural Networks (CNNs) highlights SNNs' superior energy efficiency, showing a 6x decrease in energy consumption. The study also investigates embedded SNN implementations' latency and throughput in real-world deployments, emphasizing their potential for energy-efficient monitoring systems. This research contributes to advancing SHM and UAV obstacle avoidance through SNNs' efficient information processing and decision-making capabilities within CPS domains.