Description
Rare event search in the 0nuBB and Dark Matter experiments
Imaging is one of the main pillars of clinical protocols for cancer care that provides essential non-invasive biomarkers for detection, diagnosis and response assessment. The development of Artificial Intelligence (AI) tools, and Machine Learning (ML) in particular, have proven potential to transform the analysis of radiological images, by significantly reducing processing time, by increasing...
The NEXT experiment is an international collaboration that searches for the neutrinoless double-beta decay using $^{136}\mathrm{Xe}$. It features an entirely gaseous TPC, which allows for the resolution of individual electron tracks. This opens up the possibility to employ machine learning techniques to distinguish between signal and background events based on their topological signature. In...
The LEGEND experiment is dedicated to the search for neutrinoless double beta decay using $^{76}Ge$-enriched High Purity Germanium detectors. While LEGEND has excellent energy resolution and ultra-low background levels, noise from readout electronics can make identifying events of interest more challenging. An efficient signal denoising algorithm can further enhance LEGEND’s energy resolution,...
The XENONnT detector is a dual-phase xenon time projection chamber to search for rare low-energy events. While its main purpose is the direct detection of Dark Matter, XENONnT is also sensitive to neutrino interactions for example from solar 8B neutrinos. To fully utilize the fiducialization and background reduction capabilities of the XENONnT detector, it is important to know the exact...
At the forefront of investigating neutrinoless double beta decay (0νββ) using 76Ge-enriched detectors, the LEGEND experiment is driven by the quest to unravel the mysteries of neutrinos and explore physics beyond the Standard Model. In its initial phase, LEGEND-200 deploys 200 kg of germanium at INFN Gran Sasso National Laboratory, aiming for a discovery half-life sensitivity surpassing...
