Roberto Moretti

A bstract: We present a comprehensive phenomenological analysis of the calorimetric electron capture (EC) decay spectrum of ¹⁶³ Ho as measured by the HOLMES experiment. Using high-statistics data, we unfold the instrumental energy resolution from the measured spectrum and model it as a sum of Breit-Wigner resonances and shake-off continua, providing a complete set of parameters for each component. Our approach enables the identification and tentative interpretation of all observed spectral features, including weak and overlapping structures, in terms of atomic de-excitation processes. We compare our phenomenological model with recent ab initio theoretical calculations, finding good agreement for both the main peaks and the spectral tails, despite the limitations of current theoretical and experimental precision. The model delivers an accurate description of the endpoint region, which is crucial for neutrino mass determination, and allows for a realistic treatment of backgrounds such as pile-up and tails of low-energy components. Furthermore, our decomposition facilitates the generation of Monte Carlo toy spectra for sensitivity studies and provides a framework for investigating systematic uncertainties related to solid-state and detector effects. This work establishes a robust foundation for future calorimetric neutrino mass experiments employing ¹⁶³ Ho, supporting both data analysis and experimental design.

This study presents the design, simulation, and experimental characterization of a superconducting transmon qubit circuit prototype for potential applications in dark matter detection experiments. We describe a planar circuit design featuring two noninteracting transmon qubits, one with fixed frequency and the other flux tunable. Finite-element simulations were employed to extract key Hamiltonian parameters and optimize component geometries. The qubit was fabricated and then characterized at 20 mK, allowing for a comparison between simulated and measured qubit parameters. Good agreement was found for transition frequencies and anharmonicities (within 1% and 10%, respectively) while coupling strengths exhibited larger discrepancies (30%). We discuss potential causes for measured coherence times falling below expectations ($T_{1}\sim \,$1–2 μs) and propose strategies for future design improvements. Notably, we demonstrate the application of a hybrid 3D–2D simulation approach for energy participation ratio evaluation, yielding a more accurate estimation of dielectric losses. This work represents an important first step in developing planar quantum nondemolition single-photon counters for dark matter searches, particularly for axion and dark photon detection schemes.

The determination of the absolute neutrino mass scale remains a fundamental open question in particle physics, with profound implications for both the standard model and cosmology. Direct kinematic measurements, independent of model-dependent assumptions, provide the most robust approach to address this challenge. Here we present the most stringent upper bound on the effective electron neutrino mass ever obtained with a calorimetric measurement of the electron capture decay of Ho 163 . The HOLMES experiment employs an array of ion-implanted transition-edge sensor (TES) microcalorimeters, achieving an average energy resolution of 6 eV FWHM with a scalable, multiplexed readout technique. With a total of 7 × 10 7 decay events recorded over two months and a Bayesian statistical analysis, we derive an upper limit of m β < 27 eV / c 2 at 90% credibility. These results validate the feasibility of Ho 163 calorimetry for next-generation neutrino mass experiments and demonstrate the potential of a scalable TES-based microcalorimetric technique to push the sensitivity of direct neutrino mass measurements beyond the current state of the art.

The steady progress of quantum hardware is motivating the search for novel quantum algorithm optimization strategies for near-term, real-world applications. In this study, we propose a novel feature map optimization strategy for quantum support vector machines (QSVMs), designed to enhance binary classification while taking into account backend-specific parameters, including qubit connectivity, native gate sets, and circuit depth, which are critical factors in noisy intermediate scale quantum devices. The dataset we utilised belongs to the neutrino physics domain, with applications in the search for neutrinoless double beta decay. A key contribution of this work is the parallelization of the classification task to commercially available superconducting quantum hardware to speed up the genetic search processes. The study was carried out by partitioning each quantum processing unit (QPU) into several sub-units with the same topology to implement individual QSVM instances. We conducted parallelization experiments with three IBM backends with more than 100 qubits, ranking the sub-units based on their susceptibility to noise. Data-driven simulations show how, under certain restrictions, parallelized genetic optimization can occur with the tested devices when retaining the top 20% ranked sub-units in the QPU.

High Purity Germanium (HPGe) detectors are powerful detectors for gamma-ray spectroscopy. The sensitivity to low-intensity gamma-ray peaks is often hindered by the presence of Compton continuum distributions, originated by gamma-rays emitted at higher energies. This study explores novel, pulse shape-based, machine learning-assisted techniques to enhance Compton background discrimination in Broad Energy Germanium (BEGe TM) detectors. We introduce two machine learning models: an autoencoder-MLP (Multilayer Perceptron) and a Gaussian Mixture Model (GMM). These models differentiate single-site events (SSEs) from multi-site events (MSEs) and train on signal waveforms produced in the detector. The GMM method differs from previous machine learning efforts in that it is fully unsupervised, hence not requiring specific data labelling during the training phase. Being both label-free and simulation-agnostic makes the unsupervised approach particularly advantageous for tasks where realistic, high-fidelity labeling is challenging or where biases introduced by simulated data must be avoided. In our analysis, the full-energy Peak-to-Compton ratio of the 137-Cs, a radionuclide contained in a cryoconite sample, exhibits an improvement from 0.238 in the original spectrum to 0.547 after the ACM data filtering and 0.414 after the GMM data filtering, demonstrating the effectiveness of these methods. The results also showcase an enhancement in the signal-to-background ratio across many regions of interest, enabling the detection of lower concentrations of radionuclides.

The physics potential of massive liquid argon TPCs in the low-energy regime is still to be fully reaped because few-hits events encode information that can hardly be exploited by conventional classification algorithms. Machine learning (ML) techniques give their best in these types of classification problems. In this paper, we evaluate their performance against conventional (deterministic) algorithms. We demonstrate that both Convolutional Neural Networks (CNN) and Transformer-Encoder methods outperform deterministic algorithms in one of the most challenging classification problems of low-energy physics (single- versus double-beta events). We discuss the advantages and pitfalls of Transformer-Encoder methods versus CNN and employ these methods to optimize the detector parameters, with an emphasis on the DUNE Phase II detectors (“Module of Opportunity”).