Fast and accurate numerical solutions in some problems of particle and radiation transport: synthetic acceleration for the method of short characteristics, Doppler-broadened scattering kernel, remote sensing of the cryosphere

The aim of this work is to present various aspects of numerical simulation of particle and radiation transport for industrial and environmental protection applications, to enable the analysis of complex physical processes in a fast, reliable, and efficient way. In the first part we deal with speed-up of numerical simulation of neutron transport for nuclear reactor core analysis. The convergence properties of the source iteration scheme of the Method of Characteristics applied to be heterogeneous structured geometries has been enhanced by means of Boundary Projection Acceleration, enabling the study of 2D and 3D geometries with transport theory without spatial homogenization. The computational performances have been verified with the C5G7 2D and 3D benchmarks, showing a sensible reduction of iterations and CPU time. The second part is devoted to the study of temperature-dependent elastic scattering of neutrons for heavy isotopes near to the thermal zone. A numerical computation of the Doppler convolution of the elastic scattering kernel based on the gas model is presented, for a general energy dependent cross section and scattering law in the center of mass system. The range of integration has been optimized employing a numerical cutoff, allowing a faster numerical evaluation of the convolution integral. Legendre moments of the transfer kernel are subsequently obtained by direct quadrature and a numerical analysis of the convergence is presented. In the third part we focus our attention to remote sensing applications of radiative transfer employed to investigate the Earth's cryosphere. The photon transport equation is applied to simulate reflectivity of glaciers varying the age of the layer of snow or ice, its thickness, the presence or not other underlying layers, the degree of dust included in the snow, creating a framework able to decipher spectral signals collected by orbiting detectors.

Measurement of the $B^0$ - $\bar{B}^0$ and $B^0_s$ - $\bar{B}^0_s$ production asymmetries in pp collisions at $\sqrt{s}=7$ TeV and $\sqrt{s}=8$ TeV with the LHCb experiment

The production rate of $b$ and $\bar{b}$ hadrons in $pp$ collisions are not expected to be strictly identical, due to imbalance between quarks and anti-quarks in the initial state. This phenomenon can be naively related to the fact that the $\bar{b}$ quark produced in the hard scattering might combine with a $u$ or $d$ valence quark from the colliding protons, whereas the same cannot happen for a $b$ quark. This thesis presents the analysis performed to determine the production asymmetries of $B^0$ and $B^0_s$. The analysis relies on data samples collected by the LHCb detector at the Large Hadron Collider (LHC) during the 2011 and 2012 data takings at two different values of the centre of mass energy $\sqrt{s}=7$ TeV and at $\sqrt{s}=8$ TeV, corresponding respectively to an integrated luminosity of 1 fb$^{-1}$ and of 2 fb$^{-1}$. The production asymmetry is one of the key ingredients to perform measurements of $CP$ violation in b-hadron decays at the LHC, since $CP$ asymmetries must be disentangled from other sources. The measurements of the production asymmetries are performed in bins of $p_\mathrm{T}$ and $\eta$ of the $B$-meson. The values of the production asymmetries, integrated in the ranges $4 < p_\mathrm{T} < 30$ GeV/c and $2.5<\eta<4.5$, are determined to be: \begin{equation} A_\mathrm{P}(\B^0)= (-1.00\pm0.48\pm0.29)\%,\nonumber \end{equation} \begin{equation} A_\mathrm{P}(\B^0_s)= (\phantom{-}1.09\pm2.61\pm0.61)\%,\nonumber \end{equation} where the first uncertainty is statistical and the second is systematic. The measurement of $A_\mathrm{P}(B^0)$ is performed using the full statistics collected by LHCb so far, corresponding to an integrated luminosity of 3 fb$^{-1}$, while the measurement of $A_\mathrm{P}(B^0_s)$ is realized with the first 1 fb$^{-1}$, leaving room for improvement. No clear evidence of dependences on the values of $p_\mathrm{T}$ and $\eta$ is observed. The results presented in this thesis are the most precise measurements available up to date.

The use of satellite remote sensing for flood risk management

Over the last decades the impact of natural disasters to the global environment is becoming more and more severe. The number of disasters has dramatically increased, as well as the cost to the global economy and the number of people affected. Among the natural disaster, flood catastrophes are considered to be the most costly, devastating, broad extent and frequent, because of the tremendous fatalities, injuries, property damage, economic and social disruption they cause to the humankind. In the last thirty years, the World has suffered from severe flooding and the huge impact of floods has caused hundreds of thousands of deaths, destruction of infrastructures, disruption of economic activity and the loss of property for worth billions of dollars. In this context, satellite remote sensing, along with Geographic Information Systems (GIS), has become a key tool in flood risk management analysis. Remote sensing for supporting various aspects of flood risk management was investigated in the present thesis. In particular, the research focused on the use of satellite images for flood mapping and monitoring, damage assessment and risk assessment. The contribution of satellite remote sensing for the delineation of flood prone zones, the identification of damaged areas and the development of hazard maps was explored referring to selected cases of study.

Application of Pulsed Electric Fields for the Modulation of Chemico-Physical Properties of Different Foods

Pulsed electric field technology is one of the most attractive new non-thermal technology thanks to its lower energy consumption and short treatment times. It consists of an electric treatment of short duration (from several ns to several ms) with electric field strengths from 0.1 to 80 kV/cm that lead to an increase in the permeability of the cell membrane. In this PhD thesis, PEF technology was investigated with the aim of improving mass transfer in plant and animal foods by using it alone or in combination with conventional food processes. Different methods of evaluating electroporation for optimizing PEF processing parameters were investigated. In this respect, the degree of membrane permeabilization in plant and animal food matrices was investigated using electrical impedance spectroscopy, current-voltage measurements and magnetic resonance imaging. The research findings provided useful insights and calls for critical choice of electroporation assessment methods for the selection of adequate PEF treatment conditions. It was outlined that the effect of electroporation is highly dependent on the properties of the food matrix and secondary phenomena occurring in the cell structure undergoing PEF treatment, such as the water re-distribution in the tissue due to the exchange of fluids between intra- and extra-cellular environments. This study also confirmed the great potential of combining PEF technology with conventional food processes, with the main purpose of improving the quality of the food material and accelerating the kinetics of mass transfers, in both plant and animal tissues. Consistent reduction of acrylamide formation in potato crisps was achieved by monitoring key PEF process parameters and subsequent manufacturing steps. Kiwifruit snacks showed a significant reduction in drying kinetics when pre-treated with PEF, while their quality was well maintained. Finally, the research results showed that PEF pre-treatments can shorten the brine process as well as the rehydration kinetics of fish muscles.

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.

Towards a more sustainable aquaculture: diets effects and environmental impact of aquaculture from a microbiome perspective

There is a lot of interest to optimize aquaculture production due to its overexploitation of marine resources, ocean pollution and habitat destruction. Since feed production is one of the greatest issues in aquaculture, feeding strategy optimization is important. The study of several different feed additives or supplementation is important to secure optimal growth, gut health, and function in farmed fish. Feed additives are typically supplied to ensure good health and to help the animal ward off pathogens during both normal and challenging conditions, which could stress animals and promote insurgence of pathologies or pathogens invasions. In this context has an increasing interest the study of host associated microbiome to understand the influence of novel functional feed on the health and physiology of animals. To achieve a more sustainable aquaculture sector, show a great importance the understanding of the environmental impact of this human activity in terms of habitat destruction, ocean pollution and reduction marine environments biodiversity. Marine microbiomes, either free-living or associated with multicellular hosts, is acquiring an increasing interest because their role in supporting the functioning and biodiversity of marine ecosystems, providing essential ecological services. Becoming extremely important to understand how these activities can affect marine microbiomes by altering their function and diversity. In this thesis work, we were able to present a comprehensive evaluation of different functional feeds assessing their effects in terms of growth and gut health of three fish species, Rainbow Trout (Oncorhynchus mykiss), Gilthead seabream (Sparus aurata) and Zebrafish (Danio rerio). We also explored the impact of Aquaculture on the surrounding marine microbiomes, using Patella caerulea as a model holobionts. Finally, we provided a synoptical study on the microbiomes of the water column and surface sediments in North-Western Adriatic Sea (Italy), providing the finest-scale mapping of marine microbiomes in the Mediterranean Sea.

The interplay of multiplicity and effective energy for (multi) strange hadron production in pp collisions at the LHC

The enhanced production of strange hadrons in heavy-ion collisions relative to that in minimum-bias pp collisions is historically considered one of the first signatures of the formation of a deconfined quark-gluon plasma. At the LHC, the ALICE experiment observed that the ratio of strange to non-strange hadron yields increases with the charged-particle multiplicity at midrapidity, starting from pp collisions and evolving smoothly across interaction systems and energies, ultimately reaching Pb-Pb collisions. The understanding of the origin of this effect in small systems remains an open question. This thesis presents a comprehensive study of the production of $K^{0}_{S}$, $\Lambda$ ($\bar{\Lambda}$) and $\Xi^{-}$ ($\bar{\Xi}^{+}$) strange hadrons in pp collisions at $\sqrt{s}$ = 13 TeV collected in LHC Run 2 with ALICE. A novel approach is exploited, introducing, for the first time, the concept of effective energy in the study of strangeness production in hadronic collisions at the LHC. In this work, the ALICE Zero Degree Calorimeters are used to measure the energy carried by forward emitted baryons in pp collisions, which reduces the effective energy available for particle production with respect to the nominal centre-of-mass energy. The results presented in this thesis provide new insights into the interplay, for strangeness production, between the initial stages of the collision and the produced final hadronic state. Finally, the first Run 3 results on the production of $\Omega^{\pm}$ ($\bar{\Omega}^{+}$) multi-strange baryons are presented, measured in pp collisions at $\sqrt{s}$ = 13.6 TeV and 900 GeV, the highest and lowest collision energies reached so far at the LHC. This thesis also presents the development and validation of the ALICE Time-Of-Flight (TOF) data quality monitoring system for LHC Run 3. This work was fundamental to assess the performance of the TOF detector during the commissioning phase, in the Long Shutdown 2, and during the data taking period.