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.

Failure detection for transport processes on networks

Diffusion on networks is a convenient framework to describe transport systems of different nature (from biological transport systems to urban mobility). The mathematical models are based on master equations that describe the diffusion processes by means of the weighted Laplacian matrix that connects the nodes. The link weight represent the coupling strength between the nodes. In this thesis we cope with the problem of localizing a single-edge failure that occurs in the network. An edge failure is meant to be as a sudden decrease of its transport capacities. An incomplete observation of the dynamical state of the network is available. An optimal clustering procedure based on the correlation properties among the node states is proposed. The network dimensionality is then reduced introducing representative nodes for each cluster, whose dynamical state is observed. We check the efficiency of the failure localization for our clustering method in comparison with more traditional techniques, using different graph configurations.

Neutrino interactions in the SAND LAr volume at the DUNE Near Detector site

The neutrino mass ordering and the leptonic CP violation phase are key parameters of the three-neutrino flavour mixing still to be determined. Measuring these parameters is the main goal of DUNE, a next generation Long Baseline neutrino experiment under construction in the United States. DUNE will feature a Near and a Far Detector site. An important component of the Near detector complex is the SAND apparatus, which will include GRAIN, a novel liquid Argon detector that aims at imaging neutrino interactions using scintillation light. For this purpose, an innovative optical readout system based on Coded Aperture Masks is under study. This thesis work is aimed at a first quantitative assessment of a 3D neutrino event reconstruction algorithm for GRAIN. The processing procedure is optimized and the reconstruction performance is evaluated. Promising results are obtained.

Multi-scalar critical theories: first steps for a non perturbative functional renormalization group analysis

In this work the fundamental ideas to study properties of QFTs with the functional Renormalization Group are presented and some examples illustrated. First the Wetterich equation for the effective average action and its flow in the local potential approximation (LPA) for a single scalar field is derived. This case is considered to illustrate some techniques used to solve the RG fixed point equation and study the properties of the critical theories in D dimensions. In particular the shooting methods for the ODE equation for the fixed point potential as well as the approach which studies a polynomial truncation with a finite number of couplings, which is convenient to study the critical exponents. We then study novel cases related to multi field scalar theories, deriving the flow equations for the LPA truncation, both without assuming any global symmetry and also specialising to cases with a given symmetry, using truncations based on polynomials of the symmetry invariants. This is used to study possible non perturbative solutions of critical theories which are extensions of known perturbative results, obtained in the epsilon expansion below the upper critical dimension.

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.

Evaluation of Th and U contamination in the CUORE experiment using a delayed coincidence analysis

The discovery of the neutrino mass is a direct evidence of new physics. Several questions arise from this observation, regarding the mechanism originating the neutrino masses and their hierarchy, the violation of lepton number conservation and the generation of the baryon asymmetry. These questions can be addressed by the experimental search for neutrinoless double beta (0\nu\beta\beta) decay, a nuclear decay consisting of two simultaneous beta emissions without the emission of two antineutrinos. 0\nu\beta\beta decay is possible only if neutrinos are identical to antineutrinos, namely if they are Majorana particles. Several experiments are searching for 0\nu\beta\beta decay. Among these, CUORE is employing 130Te embedded in TeO_2 bolometric crystals. It needs to have an accurate understanding of the background contribution in the energy region around the Q-value of 130Te. One of the main contributions is given by particles from the decay chains of contaminating nuclei (232Th, 235-238U) present in the active crystals or in the support structure. This thesis uses the 1 ton yr CUORE data to study these contamination by looking for events belonging to sub-chains of the Th and U decay chains and reconstructing their energy and time difference distributions in a delayed coincidence analysis. These results in combination with studies on the simulated data are then used to evaluate the contaminations. This is the first time this analysis is applied to the CUORE data and this thesis highlights the feasibility of it while providing a starting point for further studies. A part of the obtained results agrees with ones from previous analysis, demonstrating that delayed coincidence searches might improve the understanding of the CUORE experiment background. This kind of delayed coincidence analysis can also be reused in the future once the, CUORE upgrade, CUPID data will be ready to be analyzed, with the aim of improving the sensitivity to the 0\nu\beta\beta decay of 100Mo.

Amplification mechanisms of inflationary spectra in induced gravity

Inflation is the primordial stage of accelerated expansion of the Universe which solves the issues of the initial conditions of a decelerating Universe (horizon, flatness and entropy problems). Moreover, it is supposed that quantum fluctuations originated during the first moments after the Big Bang gave rise to the formation of galaxies and other structures of the Universe when inflation ends. Among these structures also primordial black holes (PBHs) may have been generated. The interest in PBHs relies on their possible connection with dark matter: they could constitute a portion or even the whole dark matter content of our Universe.\\ In this work we consider inflation in the Induced Gravity (IR) context and study possible mechanisms of amplification of the curvature perturbations generated during the cosmic acceleration. In particular we consider the possibility of a period of Constant Roll (CR). Starting from the previous work of Starobinsky et al. Our final purpose is to analyse the power spectrum of the scalar perturbations and to find in which conditions there is an enhancement of the power spectrum possibly leading to PBHs formation.

A study of brain functional connectivity and cognitive processes in long COVID-19 patients

In this work, a prospective study conducted at the IRCCS Istituto delle Scienze Neurologiche di Bologna is presented. The aim was to investigate the brain functional connectivity of a cohort of patients (N=23) suffering from persistent olfactory dysfunction after SARS-CoV-2 infection (Post-COVID-19 syndrome), as compared to a matching group of healthy controls (N=26). In particular, starting from individual resting state functional-MRI data, different analytical approaches were adopted in order to find potential alterations in the connectivity patterns of patients’ brains. Analyses were conducted both at a whole-brain level and with a special focus on brain regions involved in the processing of olfactory stimuli (Olfactory Network). Statistical correlations between functional connectivity alterations and the results of olfactory and neuropsychological tests were investigated, to explore the associations with cognitive processes. The three approaches implemented for the analysis were the seed-based correlation analysis, the group-level Independent Component analysis and a graph-theoretical analysis of brain connectivity. Due to the relative novelty of such approaches, many implementation details and methodologies are not standardized yet and represent active research fields. Seed-based and group-ICA analyses’ results showed no statistically significant differences between groups, while relevant alterations emerged from those of the graph-based analysis. In particular, patients’ olfactory sub-graph appeared to have a less pronounced modular structure compared to the control group; locally, a hyper-connectivity of the right thalamus was observed in patients, with significant involvement of the right insula and hippocampus. Results of an exploratory correlation analysis showed a positive correlation between the graphs global modularity and the scores obtained in olfactory tests and negative correlations between the thalamus hyper-connectivity and memory tests scores.

Experimental evaluation of the z-resolution in different clinical Digital Breast Tomosynthesis systems using commercial phantoms

Digital Breast Tomosynthesis (DBT) is an advanced mammography technique based on the reconstruction of a pseudo-volumetric image. To date, image quality represents the most deficient section of DBT quality control protocols. In fact, related tests are not yet characterized by either action levels or typical values. This thesis work focuses on the evaluation of one aspect of image quality: the z-resolution. The latter is studied in terms of Artifact Spread Function (ASF), a function that describes the signal spread of a detail along the reconstructed focal planes. To quantify the ASF numerically, its Full Width at Half Maximum (FWHM) is calculated and used as a representative index of z-resolution. Experimental measurements were acquired in 24 DBT systems, of 7 different models, currently in use in 20 hospital facilities in Italy. The analysis, performed on the clinical reconstructed images, of 5 different commercial phantoms, lead to the identification of characteristic FWHM values for each type of DBT system. The ASF clearly showed a dependence on the size of the detail, providing higher FWHM values for larger objects. The z-resolution was found to be positively influenced by the acquisition angle: Fujifilm sistematically showed wider ASF profiles in ST mode (15°) than in HR mode (40°). However, no clear relationship was found between angular range and ASF, among different DBT systems, due to the influence of the peculiarities of each reconstruction algorithm. The experimental approach shown in this thesis work can be proposed as a z-resolution quality control test procedure. Contextually, the values found could be used as a starting point for identifying typical values to be included in the test, in a DBT protocol. Clearly, a statistically significant number of images is needed to do this. The equipment involved in this work is located in hospitals and is not available for research purposes, so only a limited amount of data was acquired and processed.

Determination of local density of states in amorphous oxide semiconductors with Kelvin Probe Force Microscopy

Amorphous semiconductors are important materials as they can be deposited by physical deposition techniques on large areas and even on plastic substrates. Therefore, they are crucial for transistors in large active matrices for imaging and transparent wearable electronics. The most widely applied candidate for amorphous thin film transistors production is Indium Gallium Zinc Oxide (IGZO). It is attracting much interest because of its optical transparency, facile processing by sputtering deposition and notable improved charge carrier mobility with respect to hydrogenated amorphous silicon a-Si:H. Degradation of the device and long-term performance issues have been observed if IGZO thin film transistors are subjected to electrical stress, leading to a modification of IGZO channel properties and subthreshold slope. Therefore, it is of great interest to have a reliable and precise method to study the conduction band tail, and the density of states in amorphous semiconductors. The aim of this thesis is to develop a local technique using Kelvin Probe Force Microscopy to study the evolution of IGZO DOS properties. The work is divided into three main parts. First, solutions to the non-linear Poisson-Boltzmann equation of a metal-insulator-semiconductor junction describing the charge accumulation and its relation to DOS properties are elaborated. Second macroscopic techniques such as capacitance voltage (CV) measurements and photocurrent spectroscopy are applied to obtain a non-local estimate of band-tail DOS properties in thin film transistor samples. The third part of my my thesis is dedicated to the KPFM measurements. By fitting the data to the developed numerical model, important parameters describing the amorphous conduction band tail are obtained. The results are in excellent agreement with the macroscopic characterizations. KPFM result is comparable also with non-local optoelectronic characterizations, such as photocurrent spectroscopy.

Embedding Starobinsky inflation in string compactifications

A period of accelerated expansion of the primordial universe, known as inflation, represents the standard paradigm for the early universe cosmology. While inflation agrees with observational constraints, a complete understanding of its physical origin is not available yet. This suggests the necessity of an embedding into a more fundamental theory. String theory is arguably the best-developed candidate for an ultra-violet (UV) complete theory of gravity and string compactifications could provide a natural framework for addressing this issue. The aim of this thesis work is to investigate the potential embedding of Starobinsky inflation in effective field theories arising in string compactifications. In particular, we focus on two main objectives. The first one is the evaluation of Yukawa-like couplings in f (R)-theories of gravity with fermions, more specifically in the context of Starobinsky inflation. The second goal is understanding if any of the moduli which naturally arise in string compactifications has the right form of this coupling and displays the correct scalar potential, as needed for a possible identification with the scalar field driving Starobinsky inflation.

Study of high-quality ALD gate dielectrics in radiation sensitive oxide field effect transistors

Radiation dosimetry is crucial in many fields, where the exposure of ionizing radiation must be precisely controlled to avoid health and environmental safety issues. Radiotherapy and radioprotection are two examples in which fast and reliable detectors are needed. Compact and large area wearable detectors are being developed to address real-life radiation dosimetry applications, their ideal properties include flexibility, lightness, and low-cost. This thesis contributed to the development of Radiation sensitive OXide Field Effect Transistors (ROXFETs), which are detectors able to provide fast and real-time radiation read out. ROXFETs are based on thin film transistors fabricated with high-mobility amorphous oxide semiconductor, making them compatible with large area, flexible, and low cost production over plastic substrates. The gate dielectric material has high dielectric constant and high atomic number, which results in high performances and high radiation sensitivity, respectively. The aim of this work was to establish a stable and reliable fabrication process for ROXFETs made with atomic layer deposited gate dielectric. A study on the effect of gate dielectric materials was performed, focusing the attention on the properties of the dielectric-semiconductor interface. Single and multi layer dielectric structures were compared during this work. Furthermore, the effect of annealing temperature was studied. The device performances were tested to understand the underlying physical processes. In this way, it was possible to determine a reliable fabrication procedure and an optimal structure for ROXFETs. An outstanding sensitivity of (65±3)V/Gy was measured in detectors with a bi-layer Ta₂O₅-Al₂O₃ gate dielectric with low temperature annealing performed at 180°C.

Detectors in black hole quantum coherent states

The corpuscular model describes black holes as leaky bound states of gravitons. To account for the role of matter, a coherent state is built and a semiclassical description is given to the gravitational field by connecting the classical source with the quantum state for gravitons. The properties of this state can be analysed with the help of an Unruh-DeWitt detector, coupled to the quantum state of the system. The presence of a detector in general regularises the usual diverging behaviour of the field in the deep ultraviolet region, and will allow us to probe the coherent state structure and the graviton emission. In particular, a Newtonian analogue of the Unruh effect will be discussed and the coherent state will be modified to properly account for the spherical symmetry of the potential at the level of the quantum state. This correction will ensure that vacuum contributions responsible for the Unruh thermal spectrum are present in a coherent state emission process.

Characterization of in-gap electronic states in two-dimensional single crystal PEA2PbBr4 perovskite for X-ray detection

Hybrid Organic-Inorganic Halide Perovskites (HOIPs) include a large class of materials described with the general formula ABX3, where A is an organic cation, B an inorganic cation and X an halide anion. HOIPs show excellent optoelectronic characteristics such as tunable band gap, high adsorption coefficient and great mobility life-time. A subclass of these materials, the so-called two- dimensional (2D) layered HOIPs, have emerged as potential alternatives to traditional 3D analogs to enhance the stability and increase performance of perovskite devices, with particular regard in the area of ionizing radiation detectors, where these materials have reached truly remarkable milestones. One of the key challenges for future development of efficient and stable 2D perovskite X-ray detector is a complete understanding of the nature of defects that lead to the formation of deep states. Deep states act as non-radiative recombination centers for charge carriers and are one of the factors that most hinder the development of efficient 2D HOIPs-based X-ray detectors. In this work, deep states in PEA2PbBr4 were studied through Photo-Induced Current Transient Spectroscopy (PICTS), a highly sensitive spectroscopic technique capable of detecting the presence of deep states in highly resistive ohmic materials, and characterizing their activation energy, capture cross section and, under stringent conditions, the concentration of these states. The evolution of deep states in PEA 2 PbBr 4 was evaluated after exposure of the material to high doses of ionizing radiation and during aging (one year). The data obtained allowed us to evaluate the contribution of ion migration in PEA2PbBr4. This work represents an important starting point for a better understanding of transport and recombination phenomena in 2D perovskites. To date, the PICTS technique applied to 2D perovskites has not yet been reported in the scientific literature.

Quantum Monte Carlo study of effective masses in a polarized unitary Fermi gas

Ultracold gases provide an ideal platform for quantum simulations of many-body systems. Here we are interested in a particular system which has been the focus of most experimental and theoretical works on ultracold fermionic gases: the unitary Fermi gas. In this work we study with Quantum Monte Carlo simulations a two-component gas of fermionic atoms at zero temperature in the unitary regime. Specifically, we are interested in studying how the effective masses for the quasi-particles of the two components of the Fermi liquid evolve as the polarization is progressively reduced from full to lower values. A recent theoretical work, based on alternative diagrammatic methods, has indeed suggested that such effective masses should diverge at a critical polarization. To independently verify such predictions, we perform Variational Monte Carlo (VMC) calculations of the energy based on Jastrow-Slater wavefunctions after adding or subtracting a particle with a given momentum to a full Fermi sphere. In this way, we determine the quasi-particle dispersions, from which we extract the effective masses for different polarizations. The resulting effective masses turn out to be quite close to the non-interacting values, even though some evidence of an increase for the effective mass of the minority component appears close to the predicted value for the critical polarization. Preliminary results obtained for the majority component with the Fixed-node Diffusion Monte Carlo (DMC) method seem to indicate that DMC could lead to an increase of the effective masses in comparison with the VMC results. Finally, we point out further improvements of the trial wave-function and boundary conditions that would be necessary in future simulations to draw definite conclusions on the effective masses of the polarized unitary Fermi gas.