Construction and test of a cosmic ray telescope based on CMS Drift Tube chambers and data acquisition prototypes of the Phase-2 upgrade

In this thesis work, a cosmic-ray telescope was set up in the INFN laboratories in Bologna using smaller size replicas of CMS Drift Tubes chambers, called MiniDTs, to test and develop new electronics for the CMS Phase-2 upgrade. The MiniDTs were assembled in INFN National Laboratory in Legnaro, Italy. Scintillator tiles complete the telescope, providing a signal independent of the MiniDTs for offline analysis. The telescope readout is a test system for the CMS Phase-2 upgrade data acquisition design. The readout is based on the early prototype of a radiation-hard FPGA-based board developed for the High Luminosity LHC CMS upgrade, called On Board electronics for Drift Tubes. Once the set-up was operational, we developed an online monitor to display in real-time the most important observables to check the quality of the data acquisition. We performed an offline analysis of the collected data using a custom version of CMS software tools, which allowed us to estimate the time pedestal and drift velocity in each chamber, evaluate the efficiency of the different DT cells, and measure the space and time resolution of the telescope system.

Spectral distortions of the cosmic microwave background and small-scale gravitational waves as new windows on the Early Universe

In this thesis, we explore constraints which can be put on the primordial power spectrum of curvature perturbations beyond the scales probed by anisotropies of the cosmic microwave background and galaxy surveys. We exploit present and future measurements of CMB spectral distortions, and their synergy with CMB anisotropies, as well existing and future upper limits on the stochastic background of gravitational waves. We derive for the first time phenomenological templates that fit small-scale bumps in the primordial power spectrum generated in multi-field models of inflation. By using such templates, we study for the first time imprints of primordial peaks on anisotropies and spectral distortions of the cosmic microwave background and we investigate their contribution to the stochastic background of gravitational waves. Through a Monte Carlo Markov Chain analysis we infer for the first time the constraints on the amplitude, the width and the location of such bumps using Planck and FIRAS data. We also forecast how a future spectrometer like PIXIE could improve FIRAS boundaries. The results derived in this thesis have implications for the possibility of primordial black holes from inflation.

Properties of cosmic voids in dark energy simulations

The last decade has witnessed the establishment of a Standard Cosmological Model, which is based on two fundamental assumptions: the first one is the existence of a new non relativistic kind of particles, i. e. the Dark Matter (DM) that provides the potential wells in which structures create, while the second one is presence of the Dark Energy (DE), the simplest form of which is represented by the Cosmological Constant Λ, that sources the acceleration in the expansion of our Universe. These two features are summarized by the acronym ΛCDM, which is an abbreviation used to refer to the present Standard Cosmological Model. Although the Standard Cosmological Model shows a remarkably successful agreement with most of the available observations, it presents some longstanding unsolved problems. A possible way to solve these problems is represented by the introduction of a dynamical Dark Energy, in the form of the scalar field ϕ. In the coupled DE models, the scalar field ϕ features a direct interaction with matter in different regimes. Cosmic voids are large under-dense regions in the Universe devoided of matter. Being nearby empty of matter their dynamics is supposed to be dominated by DE, to the nature of which the properties of cosmic voids should be very sensitive. This thesis work is devoted to the statistical and geometrical analysis of cosmic voids in large N-body simulations of structure formation in the context of alternative competing cosmological models. In particular we used the ZOBOV code (see ref. Neyrinck 2008), a publicly available void finder algorithm, to identify voids in the Halos catalogues extraxted from CoDECS simulations (see ref. Baldi 2012 ). The CoDECS are the largest N-body simulations to date of interacting Dark Energy (DE) models. We identify suitable criteria to produce voids catalogues with the aim of comparing the properties of these objects in interacting DE scenarios to the standard ΛCDM model, at different redshifts. This thesis work is organized as follows: in chapter 1, the Standard Cosmological Model as well as the main properties of cosmic voids are intro- duced. In chapter 2, we will present the scalar field scenario. In chapter 3 the tools, the methods and the criteria by which a voids catalogue is created are described while in chapter 4 we discuss the statistical properties of cosmic voids included in our catalogues. In chapter 5 the geometrical properties of the catalogued cosmic voids are presented by means of their stacked profiles. In chapter 6 we summarized our results and we propose further developments of this work.

Study of cosmic nuclei fluxes with AMS-02: implication for dark matter search

L’Alpha Magnetic Spectrometer (AMS-02) é un rivelatore per raggi cosmici (CR) progettato e costruito da una collaborazione internazionale di 56 istituti e 16 paesi ed installato il 19 Maggio del 2011 sulla Stazione Spaziale Internazionale (ISS). Orbitando intorno alla Terra, AMS-02 sará in grado di studiare con un livello di accuratezza mai raggiunto prima la composizione dei raggi cosmici, esplorando nuove frontiere nella fisica delle particelle, ricercando antimateria primordiale ed evidenze indirette di materia oscura. Durante il mio lavoro di tesi, ho utilizzato il software GALPROP per studiare la propagazione dei CR nella nostra Galassia attraverso il mezzo interstellare (ISM), cercando di individuare un set di parametri in grado di fornire un buon accordo con i dati preliminari di AMS-02. In particolare, mi sono dedicata all’analisi del processo di propagazione di nuclei, studiando i loro flussi e i relativi rapporti. Il set di propagazione ottenuto dall’analisi é stato poi utilizzato per studiare ipotetici flussi da materia oscura e le possibili implicazioni per la ricerca indiretta attraverso AMS-02.

Monoidal categories for the physics of integrable models

Scopo di questa tesi é di evidenziare le connessioni tra le categorie monoidali, l'equazione di Yang-Baxter e l’integrabilità di alcuni modelli. Oggetto prinacipale del nostro lavoro é stato il monoide di Frobenius e come sia connesso alle C∗-algebre. In questo contesto la totalità delle dimostrazioni sfruttano la strumentazione dell'algebra diagrammatica. Nel corso del lavoro di tesi sono state riprodotte tali dimostrazioni tramite il più familiare linguaggio dell’algebra multilineare allo scopo di rendere più fruibili questi risultati ad un raggio più ampio di potenziali lettori.

Modelling the size distribution of cosmic voids

The Standard Cosmological Model is generally accepted by the scientific community, there are still an amount of unresolved issues. From the observable characteristics of the structures in the Universe,it should be possible to impose constraints on the cosmological parameters. Cosmic Voids (CV) are a major component of the LSS and have been shown to possess great potential for constraining DE and testing theories of gravity. But a gap between CV observations and theory still persists. A theoretical model for void statistical distribution as a function of size exists (SvdW) However, the SvdW model has been unsuccesful in reproducing the results obtained from cosmological simulations. This undermines the possibility of using voids as cosmological probes. The goal of our thesis work is to cover the gap between theoretical predictions and measured distributions of cosmic voids. We develop an algorithm to identify voids in simulations,consistently with theory. We inspecting the possibilities offered by a recently proposed refinement of the SvdW (the Vdn model, Jennings et al., 2013). Comparing void catalogues to theory, we validate the Vdn model, finding that it is reliable over a large range of radii, at all the redshifts considered and for all the cosmological models inspected. We have then searched for a size function model for voids identified in a distribution of biased tracers. We find that, naively applying the same procedure used for the unbiased tracers to a halo mock distribution does not provide success- full results, suggesting that the Vdn model requires to be reconsidered when dealing with biased samples. Thus, we test two alternative exten- sions of the model and find that two scaling relations exist: both the Dark Matter void radii and the underlying Dark Matter density contrast scale with the halo-defined void radii. We use these findings to develop a semi-analytical model which gives promising results.

Learning and accepting quantum physics. Re-analysis of a teaching proposal

Nella tesi è analizzata nel dettaglio una proposta didattica sulla Fisica Quantistica elaborata dal gruppo di ricerca in Didattica della Fisica dell’Università di Bologna, in collaborazione con il gruppo di ricerca in Fisica Teorica e con ricercatori del CNR di Bologna. La proposta è stata sperimentata in diverse classi V di Liceo scientifico e dalle sperimentazioni sono emersi casi significativi di studenti che non sono riusciti ad accettare la teoria quantistica come descrizione convincente ad affidabile della realtà fisica (casi di non accettazione), nonostante sembrassero aver capito la maggior parte degli argomenti e essersi ‘appropriati’ del percorso per come gli era stato proposto. Da questa evidenza sono state formulate due domande di ricerca: (1) qual è la natura di questa non accettazione? Rispecchia una presa di posizione epistemologica o è espressione di una mancanza di comprensione profonda? (2) Nel secondo caso, è possibile individuare precisi meccanismi cognitivi che possono ostacolare o facilitare l’accettazione della fisica quantistica? L’analisi di interviste individuali degli studenti ha permesso di mettere in luce tre principali esigenze cognitive (cognitive needs) che sembrano essere coinvolte nell’accettazione e nell’apprendimento della fisica quantistica: le esigenze di visualizzabilità, comparabilità e di ‘realtà’. I ‘cognitive needs’ sono stati quindi utilizzati come strumenti di analisi delle diverse proposte didattiche in letteratura e del percorso di Bologna, al fine di metterne in luce le criticità. Sono state infine avanzate alcune proposte per un suo miglioramento.

Cosmology from probe combination: synergies between the number counts of galaxy clusters and cosmic voids

This Thesis work concerns the complementary study of the abundance of galaxy clusters and cosmic voids identified in cosmological simulations, at different redshifts. In particular, we focus our analyses on the combination of the cosmological constraints derived from these probes, which can be considered statistically independent, given the different aspects of Universe density field they map. Indeed, we aim at showing the orthogonality of the derived cosmological constraints and the resulting impressive power of the combination of these probes. To perform this combination we apply three newly implemented algorithms that allow us to combine independent probes. These algorithms represent a flexible and user-friendly tool to perform different techniques for probe combination and are implemented within the environment provided by the large set of free software C++/Python CosmoBolognaLib. All the new implemented codes provide simple and flexible tools that will be soon applied to the data coming from currently available and next-generation wide-field surveys to perform powerful combined cosmological analyses.

Reti neurali "physics-informed" per modelli epidemiologici

La malattia COVID-19 associata alla sindrome respiratoria acuta grave da coronavirus 2 (SARS-CoV-2) ha rappresentato una grave minaccia per la salute pubblica e l’economia globale sin dalla sua scoperta in Cina, nel dicembre del 2019. Gli studiosi hanno effettuato numerosi studi ed in particolar modo l’applicazione di modelli epidemiologici costruiti a partire dai dati raccolti, ha permesso la previsione di diversi scenari sullo sviluppo della malattia, nel breve-medio termine. Gli obiettivi di questa tesi ruotano attorno a tre aspetti: i dati disponibili sulla malattia COVID-19, i modelli matematici compartimentali, con particolare riguardo al modello SEIJDHR che include le vaccinazioni, e l’utilizzo di reti neurali ”physics-informed” (PINNs), un nuovo approccio basato sul deep learning che mette insieme i primi due aspetti. I tre aspetti sono stati dapprima approfonditi singolarmente nei primi tre capitoli di questo lavoro e si sono poi applicate le PINNs al modello SEIJDHR. Infine, nel quarto capitolo vengono riportati frammenti rilevanti dei codici Python utilizzati e i risultati numerici ottenuti. In particolare vengono mostrati i grafici sulle previsioni nel breve-medio termine, ottenuti dando in input dati sul numero di positivi, ospedalizzati e deceduti giornalieri prima riguardanti la città di New York e poi l’Italia. Inoltre, nell’indagine della parte predittiva riguardante i dati italiani, si è individuato un punto critico legato alla funzione che modella la percentuale di ricoveri; sono stati quindi eseguiti numerosi esperimenti per il controllo di tali previsioni.

Pulse shape studies of neutral particles with a liquid scintillator

The hadrontherapy exploits beams of charged particles against deep cancers. These ions have a depth-dose profile in which there is a little release of energy at the beginning of their path, whereas there is a sharp maximum, the Bragg Peak, near its end path. However, if heavy ions are used, the fragmentation of the projectile can happen and the fragments can release some dose outside the treatment volume beyond the Bragg peak. The fragmentation process takes place also when the Galactic Cosmic Rays at high energy hit the spaceship during space missions. In both cases some neutrons can be produced and if they interact with the absorbing materials nuclei some secondary particles are generated which can release energy. For this reason, studies about the cross section measurements of the fragments generated during the collisions of heavy ions against the tissues nuclei are very important. In this context, the FragmentatiOn Of Target (FOOT) experiment was born, and aims at measuring the differential and double differential fragmentation cross sections for different kinetic energies relevant to hadrontherapy and space radioprotection with high accuracy. Since during fragmentation processes also neutrons are produced, tests of a neutron detection system are ongoing. In particular, recently a neutron detector made up of a liquid organic scintillator, BC-501A with neutrons/gammas discrimination capability was studied, and it represents the core of this thesis. More in details, an analysis of the data collected at the GSI laboratory, in Darmstadt, Germany, is effectuated which consists in discriminating neutral and charged particles and then to separate neutrons from gammas. From this analysis, a preliminary energy-differential reaction cross-section for the production of neutrons in the 16O + (C_2H_4)_(n) and 16O + C reactions was estimated.

Machine learning "as a service" for high energy physics (MLaaS4HEP): evolution of a framework for ML-based physics analyses

The scientific success of the LHC experiments at CERN highly depends on the availability of computing resources which efficiently store, process, and analyse the amount of data collected every year. This is ensured by the Worldwide LHC Computing Grid infrastructure that connect computing centres distributed all over the world with high performance network. LHC has an ambitious experimental program for the coming years, which includes large investments and improvements both for the hardware of the detectors and for the software and computing systems, in order to deal with the huge increase in the event rate expected from the High Luminosity LHC (HL-LHC) phase and consequently with the huge amount of data that will be produced. Since few years the role of Artificial Intelligence has become relevant in the High Energy Physics (HEP) world. Machine Learning (ML) and Deep Learning algorithms have been successfully used in many areas of HEP, like online and offline reconstruction programs, detector simulation, object reconstruction, identification, Monte Carlo generation, and surely they will be crucial in the HL-LHC phase. This thesis aims at contributing to a CMS R&D project, regarding a ML "as a Service" solution for HEP needs (MLaaS4HEP). It consists in a data-service able to perform an entire ML pipeline (in terms of reading data, processing data, training ML models, serving predictions) in a completely model-agnostic fashion, directly using ROOT files of arbitrary size from local or distributed data sources. This framework has been updated adding new features in the data preprocessing phase, allowing more flexibility to the user. Since the MLaaS4HEP framework is experiment agnostic, the ATLAS Higgs Boson ML challenge has been chosen as physics use case, with the aim to test MLaaS4HEP and the contribution done with this work.

Primordial black holes from multi-field inflation

In this master's thesis, the formation of Primordial Black Holes (PBHs) in the context of multi-field inflation is studied. In these models, the interaction of isocurvature and curvature perturbations can lead to a significant enhancement of the latter, and to the subsequent production of PBHs. Depending on their mass, these can account for a significant fraction (or, in some cases, the entirety) of the universe's Dark Matter content. After studying the theoretical framework of generic N-field inflationary models, the focus is restricted to the two-field case, for which a few concrete realisations are analysed. A numerical code (written in Wolfram Mathematica) is developed to make quantitative predictions for the main inflationary observables, notably the scalar power spectra. Parallelly, the production of PBHs due to the dynamics of 2-field inflation is examined: their mass, as well as the fraction of Dark Matter they represent, is calculated for the models considered previously.

A deep learning model via thermodynamics and condensed matter physics

Deep Learning architectures give brilliant results in a large variety of fields, but a comprehensive theoretical description of their inner functioning is still lacking. In this work, we try to understand the behavior of neural networks by modelling in the frameworks of Thermodynamics and Condensed Matter Physics. We approach neural networks as in a real laboratory and we measure the frequency spectrum and the entropy of the weights of the trained model. The stochasticity of the training occupies a central role in the dynamics of the weights and makes it difficult to assimilate neural networks to simple physical systems. However, the analogy with Thermodynamics and the introduction of a well defined temperature leads us to an interesting result: if we eliminate from a CNN the "hottest" filters, the performance of the model remains the same, whereas, if we eliminate the "coldest" ones, the performance gets drastically worst. This result could be exploited in the realization of a training loop which eliminates the filters that do not contribute to loss reduction. In this way, the computational cost of the training will be lightened and more importantly this would be done by following a physical model. In any case, beside important practical applications, our analysis proves that a new and improved modeling of Deep Learning systems can pave the way to new and more efficient algorithms.

Exploiting the clustering of cosmic voids as a novel cosmological probe

The investigations of the large-scale structure of our Universe provide us with extremely powerful tools to shed light on some of the open issues of the currently accepted Standard Cosmological Model. Until recently, constraining the cosmological parameters from cosmic voids was almost infeasible, because the amount of data in void catalogues was not enough to ensure statistically relevant samples. The increasingly wide and deep fields in present and upcoming surveys have made the cosmic voids become promising probes, despite the fact that we are not yet provided with a unique and generally accepted definition for them. In this Thesis we address the two-point statistics of cosmic voids, in the very first attempt to model its features with cosmological purposes. To this end, we implement an improved version of the void power spectrum presented by Chan et al. (2014). We have been able to build up an exceptionally robust method to tackle with the void clustering statistics, by proposing a functional form that is entirely based on first principles. We extract our data from a suite of high-resolution N-body simulations both in the LCDM and alternative modified gravity scenarios. To accurately compare the data to the theory, we calibrate the model by accounting for a free parameter in the void radius that enters the theory of void exclusion. We then constrain the cosmological parameters by means of a Bayesian analysis. As far as the modified gravity effects are limited, our model is a reliable method to constrain the main LCDM parameters. By contrast, it cannot be used to model the void clustering in the presence of stronger modification of gravity. In future works, we will further develop our analysis on the void clustering statistics, by testing our model on large and high-resolution simulations and on real data, also addressing the void clustering in the halo distribution. Finally, we also plan to combine these constraints with those of other cosmological probes.

Back-in-time void finder: dynamical detection of cosmic voids for precision cosmology

Cosmic voids are vast and underdense regions emerging between the elements of the cosmic web and dominating the large-scale structure of the Universe. Void number counts and density profiles have been demonstrated to provide powerful cosmological probes. Indeed, thanks to their low-density nature and they very large sizes, voids represent natural laboratories to test alternative dark energy scenarios, modifications of gravity and the presence of massive neutrinos. Despite the increasing use of cosmic voids in Cosmology, a commonly accepted definition for these objects has not yet been reached. For this reason, different void finding algorithms have been proposed during the years. Voids finder algorithms based on density or geometrical criteria are affected by intrinsic uncertainties. In recent years, new solutions have been explored to face these issues. The most interesting is based on the idea of identify void positions through the dynamics of the mass tracers, without performing any direct reconstruction of the density field. The goal of this Thesis is to provide a performing void finder algorithm based on dynamical criteria. The Back-in-time void finder (BitVF) we present use tracers as test particles and their orbits are reconstructed from their actual clustered configuration to an homogeneous and isotropic distribution, expected for the Universe early epoch. Once the displacement field is reconstructed, the density field is computed as its divergence. Consequently, void centres are identified as local minima of the field. In this Thesis work we applied the developed void finding algorithm to simulations. From the resulting void samples we computed different void statistics, comparing the results to those obtained with VIDE, the most popular void finder. BitVF proved to be able to produce a more reliable void samples than the VIDE ones. The BitVF algorithm will be a fundamental tool for precision cosmology, especially with upcoming galaxy-survey.