Related papers: Pulse Shape Discrimination for Germanium Detectors…
A pulse-shape discrimination method based on artificial neural networks was applied to pulses simulated for different background, signal and signal-like interactions inside a germanium detector. The simulated pulses were used to investigate…
Experiments searching for rare processes like neutrinoless double beta decay heavily rely on the identification of background events to reduce their background level and increase their sensitivity. We present a novel machine learning based…
An essential metric for the quality of a particle-identification experiment is its statistical power to discriminate between signal and background. Pulse shape discrimination (PSD) is a basic method for this purpose in many nuclear,…
Point-contact p-type high-purity germanium detectors (PPC HPGe) are particularly suited for detection of sub-keV nuclear recoils from coherent elastic scattering of neutrinos or light dark matter particles. While these particles are…
Pulse-shape discrimination (PSD) in high-purity germanium (HPGe) detectors is central to rare-event searches such as neutrinoless double-beta decay (0vBB), yet conventional approaches compress each waveform into a small set of summary…
High-purity germanium detectors are widely used in rare-event searches due to their excellent energy resolution and extremely high intrinsic (radio)purity. In experiments searching for neutrinoless double beta decay in $^{76}$Ge such as…
$P$-type high-purity germanium (HPGe) detectors are widely used across many scientific domains, and current data analysis methods have served well in many use cases. However, applications like low-background experiments that search for rare…
This review presents a comprehensive survey and benchmark of pulse shape discrimination (PSD) algorithms for radiation detection, classifying nearly sixty methods into statistical (time-domain, frequency-domain, neural network-based) and…
Hybrid quantum-classical machine learning offers a promising direction for advancing automated quality control in industrial settings. In this study, we investigate two hybrid quantum-classical approaches for classifying defects in…
High-Purity Germanium (HPGe) detectors are a key technology for rare-event searches such as neutrinoless double-beta decay (\ensuremath{0\nu\beta\beta}) and dark matter experiments. Pulse shapes from these detectors vary with interaction…
We demonstrate the use of a convolutional neural network to perform neutron-gamma pulse shape discrimination, where the only inputs to the network are the raw digitised SiPM signals from a dual scintillator detector element made of…
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…
The paper deals with quantum pulse position modulation (PPM), both in the absence (pure states) and in the presence (mixed states) of thermal noise, using the Glauber representation of coherent laser radiation. The objective is to find…
Nuclear recoil measurements with high-purity Germanium detectors are very promising to directly detect dark matter candidates. The main background sources in such experiments are natural radioactivity and microphonic noise. Digital pulse…
We present a germanium phonon-to-charge transducer that integrates a slow-phonon phononic-crystal (PnC) region with radio-frequency quantum point-contact (RF-QPC) readout at 4 K, and we evaluate its dark-sector reach. A calibrated…
To reduce background in experiments looking for rare events, such as the GERDA double beta decay experiment, it is necessary to employ active background-suppression techniques. One of such techniques is the pulse shape analysis of signals…
The temporal-mode (TM) basis is a prime candidate to perform high-dimensional quantum encoding. Quantum frequency conversion has been employed as a tool to perform tomographic analysis and manipulation of ultrafast states of quantum light…
In the rare physics events search, liquid and solid detector plays an important role. Its analysis is based on the detection of excess events over the expected background or on the detection of an annual event rate modulation. Germanium…
High-Energy Physics (HEP) experiments, such as those at the Large Hadron Collider (LHC), generate massive datasets that challenge classical computational limits. Quantum Machine Learning (QML) offers a potential advantage in processing…
Deep metric learning has recently shown extremely promising results in the classical data domain, creating well-separated feature spaces. This idea was also adapted to quantum computers via Quantum Metric Learning(QMeL). QMeL consists of a…