Analisi del segnale temporale del sistema a tempo di volo dell'esperimento ALICE a LHC per le procedure di controllo di qualita dei dati

Come migliorare l'algoritmo di Quality Assurance per il rivelatore TOF dell'esperimento ALICE ad LHC? Nel corso di questo elaborato si passerà attraverso una breve introduzione del Modello Standard e della fisica di cui si occupa l'esperimento ALICE a LHC, che illustrerà secondo quali principi l'apparato è stato costruito e quali sono i fenomeni di cui si va alla ricerca: il QGP, mostrato attraverso le sue molteplici conseguenze, da "Quarkonia" al "Jet Quenching". Verrà introdotto l'acceleratore di particelle LHC che ospita l'esperimento ALICE e i suoi rivelatori, protagonisti assoluti nello studio sul QGP e si porterà il focus sulla descrizione del rivelatore a tempo di volo TOF, dei suoi principi di funzionamento e delle sue caratteristiche significative per questo lavoro di tesi. Quindi si entrerà nel vivo della ottimizzazione dell'algoritmo di Quality Assurance, se ne vedrà lo stato attuale e la sua evoluzione nel tentativo di produrre una sua versione migliorata e più efficiente. Infine si confronteranno i risultati del nuovo algoritmo con la sua versione iniziale per valutare l'entità del miglioramento apportato

Search for Beyond Standard Model neutral Higgs boson in the μμ channel with the CMS detector at LHC with a multivariate approach

Il lavoro descritto in questa tesi riguarda l’uso di tecniche di analisi multivariata applicate alla ricerca dei bosoni di Higgs neutri, previsti da teorie che estendono il Modello Standard, prodotti in associazione ad una coppia di quark b, che decadono in due muoni. Lo studio è stato svolto sui campioni Monte Carlo che riproducono le condizioni di presa dati del 2016 dell’esperimento CMS durante le collisioni protoneprotone di LHC a √s = 13 TeV. Sono state esaminate sette ipotesi di massa del suddetto segnale nell’intervallo di massa tra 140 e 1000 GeV. Sono stati utilizzati due diversi modelli di machine learning, Boosted Decision Trees (BDT) e Artificial Neural Networks, seguendo due diversi tipi di training, uno fatto su un campione inclusivo, l’altro fatto su due categorie indipendenti a partire dal campione inclusivo. Le prestazioni dei modelli esaminati sono state valutate in termini di significatività statistica. I risultati sono stati messi a confronto con quelli ottenuti da un’analisi simile pubblicata da CMS basata su tagli di selezione. L’uso di tecniche di analisi multivariata si è rivelato in generale migliore nella discriminazione segnale fondo. In particolare il modello BDT con training per categorie ha raggiunto il miglior risultato in termini di significatività in tutte le ipotesi di massa studiate

CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation

Fast simulation of the energy depositions in high-granular detectors is needed for future collider experiments with ever-increasing luminosities. Generative machine learning (ML) models have been shown to speed up and augment the traditional simulation chain in physics analysis. However, the majority of previous efforts were limited to models relying on fixed, regular detector readout geometries. A major advancement is the recently introduced CaloClouds model, a geometry-independent diffusion model, which generates calorimeter showers as point clouds for the electromagnetic calorimeter of the envisioned International Large Detector (ILD). In this work, we introduce CaloClouds II which features a number of key improvements. This includes continuous time score-based modelling, which allows for a 25-step sampling with comparable fidelity to CaloClouds while yielding a $6\times$ speed-up over Geant4 on a single CPU ($5\times$ over CaloClouds). We further distill the diffusion model into a consistency model allowing for accurate sampling in a single step and resulting in a $46\times$ ($37\times$ over CaloClouds) speed-up. This constitutes the first application of consistency distillation for the generation of calorimeter showers.Comment: 30 pages, 7 figures, 3 tables, code available at https://github.com/FLC-QU-hep/CaloClouds-

Shared Data and Algorithms for Deep Learning in Fundamental Physics

We introduce a collection of datasets from fundamental physics research -- including particle physics, astroparticle physics, and hadron- and nuclear physics -- for supervised machine learning studies. These datasets, containing hadronic top quarks, cosmic-ray induced air showers, phase transitions in hadronic matter, and generator-level histories, are made public to simplify future work on cross-disciplinary machine learning and transfer learning in fundamental physics. Based on these data, we present a simple yet flexible graph-based neural network architecture that can easily be applied to a wide range of supervised learning tasks in these domains. We show that our approach reaches performance close to state-of-the-art dedicated methods on all datasets. To simplify adaptation for various problems, we provide easy-to-follow instructions on how graph-based representations of data structures, relevant for fundamental physics, can be constructed and provide code implementations for several of them. Implementations are also provided for our proposed method and all reference algorithms.Comment: 13 pages, 5 figures, 5 table

Generative Modeling with Diffusion Neural Networks for Fast Simulation of Electromagnetic Showers in the International Large Detector

In high energy physics, detailed and time-consuming simulations are used for particle interactions with detectors. For future experiments and the upcoming High-Luminosity phase of the Large Hadron Collider (HL-LHC), the computational costs of conventional simulation tools are expected to exceed the projected computational resources.Generative neural networks (GNNs) have the potential to provide a fast and accurate alternative. So far most of the studies of GNNs for fast simulations have used data represented in the form of a regular grid since it is possible to apply modern machine learning algorithms from image processing that are well optimized and developed.In fast simulations with GNNs, it is crucial to be able to place GNNs into the simulation pipeline, and since many of today's detector systems are not regular in terms of the positions of the active cells, it is very hard to represent the data in a form suitable for training the GNN.This work focuses on the development of a GNN for speeding up the simulation of electromagnetic showers in the electromagnetic calorimeter of the International Large Detector (ILD). In particular, a Diffusion Model is trained on Geant4 steps, where the electromagnetic shower is presented as a 3D point cloud to avoid the irregularities of the detector geometry and thereby generate showers anywhere in the calorimeter

CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation

Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models would enable them to augment traditional simulations and alleviate a major computing constraint. This work achieves a major breakthrough in this task by, for the first time, directly generating a point cloud of a few thousand space points with energy depositions in the detector in 3D space without relying on a fixed-grid structure. This is made possible by two key innovations: i) using recent improvements in generative modeling we apply a diffusion model to generate ii) an initial even higher-resolution point cloud of up to 40,000 so-called Geant4 steps which is subsequently down-sampled to the desired number of up to 6,000 space points. We showcase the performance of this approach using the specific example of simulating photon showers in the planned electromagnetic calorimeter of the International Large Detector (ILD) and achieve overall good modeling of physically relevant distributions.Comment: 25 pages, 11 figure

CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation

Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics.Achieving high accuracy and speed with generative machine learning models would enable them to augment traditional simulations and alleviate a major computing constraint.This work achieves a major breakthrough in this task by for the first time directly generating a point-cloud of O(1000) space points with energy depositions in the detector in 3D-space without relying on a fixed-grid structure. This is made possible by two key innovations: i) using recent improvements in generative modelling we apply a diffusion model and ii) an initial even higher-resolution point-cloud of up to 40000 so-called GEANT4 steps which are subsequently down-sampled to the desired number of up to 6000 space points.We showcase the performance of this approach using the specific example of simulating photon showers in the planned electromagnetic calorimeter of the International Large Detector (ILD) and achieve overall good modelling of physically relevant distributions

CaloClouds: fast geometry-independent highly-granular calorimeter simulation

Simulating showers of particles in highly-granular detectorsis a key frontier in the application of machine learning to particlephysics. Achieving high accuracy and speed with generative machinelearning models would enable them to augment traditional simulationsand alleviate a major computing constraint. This work achieves amajor breakthrough in this task by, for the first time, directlygenerating a point cloud of a few thousand space points with energydepositions in the detector in 3D space without relying on afixed-grid structure. This is made possible by two key innovations:i) Using recent improvements in generative modeling we apply adiffusion model to generate photon showers as high-cardinality pointclouds. ii) These point clouds of up to 6,000 space points arelargely geometry-independent as they are down-sampled from initialeven higher-resolution point clouds of up to 40,000 so-calledGeant steps. We showcase the performance of this approachusing the specific example of simulating photon showers in theplanned electromagnetic calorimeter of the International LargeDetector (ILD) and achieve overall good modeling of physicallyrelevant distributions

CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation

Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models would enable them to augment traditional simulations and alleviate a major computing constraint. This work achieves a major breakthrough in this task by, for the first time, directly generating a point cloud of a few thousand space points with energy depositions in the detector in 3D space without relying on a fixed-grid structure. This is made possible by two key innovations: i) using recent improvements in generative modeling we apply a diffusion model to generate ii) an initial even higher-resolution point cloud of up to 40,000 so-called Geant4 steps which is subsequently down-sampled to the desired number of up to 6,000 space points. We showcase the performance of this approach using the specific example of simulating photon showers in the planned electromagnetic calorimeter of the International Large Detector (ILD) and achieve overall good modeling of physically relevant distributions