Development and optimization of techniques and design parameters for the engineering of atmospheric pressure plasma devices for biomedical applications and plasma medicine

In the last decades, the possibility to generate plasma at atmospheric pressure gave rise to a new emerging field called plasma medicine; it deals with the application of cold atmospheric pressure plasmas (CAPs) or plasma-activated solutions on or in the human body for therapeutic effects. Thanks to a blend of synergic biologically active agents and biocompatible temperatures, different CAP sources were successfully employed in many different biomedical applications such as dentistry, dermatology, wound healing, cancer treatment, blood coagulation, etc.… Despite their effectiveness has been verified in the above-mentioned biomedical applications, over the years, researchers throughout the world described numerous CAP sources which are still laboratory devices not optimized for the specific application. In this perspective, the aim of this dissertation was the development and the optimization of techniques and design parameters for the engineering of CAP sources for different biomedical applications and plasma medicine among which cancer treatment, dentistry and bioaerosol decontamination. In the first section, the discharge electrical parameters, the behavior of the plasma streamers and the liquid and the gas phase chemistry of a multiwire device for the treatment of liquids were performed. Moreover, two different plasma-activated liquids were used for the treatment of Epithelial Ovarian Cancer cells and fibroblasts to assess their selectivity. In the second section, in accordance with the most important standard regulations for medical devices, were reported the realization steps of a Plasma Gun device easy to handle and expected to be mounted on a tabletop device that could be used for dental clinical applications. In the third section, in relation to the current COVID-19 pandemic, were reported the first steps for the design, realization, and optimization of a dielectric barrier discharge source suitable for the treatment of different types of bioaerosol.

Application of Deep Learning techniques in the search for BSM Higgs bosons in the $\mu\mu$ final state in CMS

The Standard Model (SM) of particle physics predicts the existence of a Higgs field responsible for the generation of particles' mass. However, some aspects of this theory remain unsolved, supposing the presence of new physics Beyond the Standard Model (BSM) with the production of new particles at a higher energy scale compared to the current experimental limits. The search for additional Higgs bosons is, in fact, predicted by theoretical extensions of the SM including the Minimal Supersymmetry Standard Model (MSSM). In the MSSM, the Higgs sector consists of two Higgs doublets, resulting in five physical Higgs particles: two charged bosons $H^{\pm}$, two neutral scalars $h$ and $H$, and one pseudoscalar $A$. The work presented in this thesis is dedicated to the search of neutral non-Standard Model Higgs bosons decaying to two muons in the model independent MSSM scenario. Proton-proton collision data recorded by the CMS experiment at the CERN LHC at a center-of-mass energy of 13 TeV are used, corresponding to an integrated luminosity of $35.9\ \text{fb}^{-1}$. Such search is sensitive to neutral Higgs bosons produced either via gluon fusion process or in association with a $\text{b}\bar{\text{b}}$ quark pair. The extensive usage of Machine and Deep Learning techniques is a fundamental element in the discrimination between signal and background simulated events. A new network structure called parameterised Neural Network (pNN) has been implemented, replacing a whole set of single neural networks trained at a specific mass hypothesis value with a single neural network able to generalise well and interpolate in the entire mass range considered. The results of the pNN signal/background discrimination are used to set a model independent 95\% confidence level expected upper limit on the production cross section times branching ratio, for a generic $\phi$ boson decaying into a muon pair in the 130 to 1000 GeV range.

Effective field theories and their phenomenological applications

Effective field theories (EFTs) are ubiquitous in theoretical physics and in particular in field theory descriptions of quantum systems probed at energies much lower than one or few characterizing scales. More recently, EFTs have gained a prominent role in the study of fundamental interactions and in particular in the parametriasation of new physics beyond the Standard Model, which would occur at scales Λ, much larger than the electroweak scale. In this thesis, EFTs are employed to study three different physics cases. First, we consider light-by-light scattering as a possible probe of new physics. At low energies it can be described by dimension-8 operators, leading to the well-known Euler-Heisenberg Lagrangian. We consider the explicit dependence of matching coefficients on type of particle running in the loop, confirming the sensitiveness to the spin, mass, and interactions of possibly new particles. Second, we consider EFTs to describe Dark Matter (DM) interactions with SM particles. We consider a phenomenologically motivated case, i.e., a new fermion state that couples to the Hypercharge through a form factor and has no interactions with photons and the Z boson. Results from direct, indirect and collider searches for DM are used to constrain the parameter space of the model. Third, we consider EFTs that describe axion-like particles (ALPs), whose phenomenology is inspired by the Peccei-Quinn solution to strong CP problem. ALPs generically couple to ordinary matter through dimension-5 operators. In our case study, we investigate the rather unique phenomenological implications of ALPs with enhanced couplings to the top quark.

Search for same-sign top quark pairs with the ATLAS detector at the LHC

In this thesis, a search for same-sign top quark pairs produced according to the Standard Model Effective Field Theory (SMEFT) is presented. The analysis is carried out within the ATLAS Collaboration using collision data at a center-of-mass energy of $\sqrt{s} = 13$ TeV, collected by the ATLAS detector during the Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of $140$ fb$^{-1}$. Three SMEFT operators are considered in the analysis, namely $\mathcal{O}_{RR}$, $\mathcal{O}_{LR}^{(1)}$, and $\mathcal{O}_{LR}^{(8)}$. The signal associated to same-sign top pairs is searched in the dilepton channel, with the top quarks decaying via $t \longrightarrow W^+ b \longrightarrow \ell^+ \nu b$, leading to a final state signature composed of a pair of high-transverse momentum same-sign leptons and $b$-jets. Deep Neural Networks are employed in the analysis to enhance sensitivity to the different SMEFT operators and to perform signal-background discrimination. This is the first result of the ATLAS Collaboration concerning the search for same-sign top quark pairs production in proton-proton collision data at $\sqrt{s} = 13$ TeV, in the framework of the SMEFT.