Quality controls for the Mu2e electromagnetic calorimeter and R&D studies for the muon capture rate determination

According to the SM, while Lepton Flavour Violation is allowed in the neutral sector, Charged Lepton Flavour Violation (CLFV) processes are forbidden. The Mu2e Experiment at Fermilab will search for the CLFV process of neutrinoless conversion of a muon into an electron within the field of an Al nucleus. The Mu2e detectors and its state-of-the-art superconducting magnetic system are presented, with special focus put to the electromagnetic crystal calorimeter. The calorimeter is composed by two annular disks, each one hosting pure CsI crystals read-out by custom silicon photomultipliers (SiPMs). The SiPMs are amplified by custom electronics (FEE) and are glued to copper holders in group of 2 SiPMs and 2 FEE boards thus forming a crystal Readout Unit. These Readout Units are being tested at the Quality Control (QC) Station, whose design, realization and operations are presented in this work. The QC Station allows to determine the gain, the response and the photon detection efficiency of each unit and to evaluate the dependence of these parameters from the supply voltage and temperature. The station is powered by two remotely-controlled power supplies and monitored thanks to a Slow Control system which is also illustrated in this work. In this thesis, we also demonstrated that the calorimeter can perform its own measurement of the Mu2e normalization factor, i.e. the counting of the 1.8 MeV photon line produced in nuclear muon captures. A specific calorimeter sub-system called CAPHRI, composed by four LYSO crystals with SiPM readout, has been designed and tested. We simulated the capability of this system on performing this task showing that it can get a faster and more reliable measurement of the muon capture rates with respect to the current Mu2e detector dedicated to this measurement. The characterization of energy resolution and response uniformity of the four procured LYSO crystals are llustrated.

Near Real-Time Detection Of Pipe Bursts in Water Distribution Systems

The research work presented in the thesis describes a new methodology for the automated near real-time detection of pipe bursts in Water Distribution Systems (WDSs). The methodology analyses the pressure/flow data gathered by means of SCADA systems in order to extract useful informations that go beyond the simple and usual monitoring type activities and/or regulatory reporting , enabling the water company to proactively manage the WDSs sections. The work has an interdisciplinary nature covering AI techniques and WDSs management processes such as data collection, manipulation and analysis for event detection. Indeed, the methodology makes use of (i) Artificial Neural Network (ANN) for the short-term forecasting of future pressure/flow signal values and (ii) Rule-based Model for bursts detection at sensor and district level. The results of applying the new methodology to a District Metered Area in Emilia- Romagna’s region, Italy have also been reported in the thesis. The results gathered illustrate how the methodology is capable to detect the aforementioned failure events in fast and reliable manner. The methodology guarantees the water companies to save water, energy, money and therefore enhance them to achieve higher levels of operational efficiency, a compliance with the current regulations and, last but not least, an improvement of customer service.

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.

Calorimetric efficiency of the DUNE experiment

The Deep Underground Neutrino Experiment is a long-baseline neutrino experiment which is under construction in the United States. It will be composed of a Near Detector system located a few hundred meters from the neutrino source at Fermilab and a far detector system composed of four multi-kt LArTPCs at Sanford Underground Research Facility in South Dakota. The experiment will measure the leptonic CP violation phase of the PMNS matrix and discriminate the ordering of neutrino masses. Additional physics goals include detection of neutrinos from supernovae collapse and search for possible proton decay. One component of the Near detector complex is the System for on-Axis Neutrino Detection apparatus, which includes GRanular Argon for Interaction of Neutrinos, a novel liquid Argon detector that aims at imaging neutrino interactions using scintillation light collected by optical system and read-out by SIPM matrix. This thesis work aims at studying the GRAIN performances as a homogeneous calorimeter, able to measure the energy deposited by charged particles in LAr through scintillation photons emitted along their path inside the vessel. The energy calibration of the liquid argon volume required to write (and validate) an efficient software for the detector response simulation to the arrival of scintillation photons.