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Machine learning in materials science faces challenges due to limited experimental data, as generating synthesis data is costly and time-consuming, especially with in-house experiments. Mining data from existing literature introduces issues…
High-purity germanium (HPGe) detectors are widely used for the measurement of the low concentrated radioactivities. For the Super-Kamiokande-Gadolinium project, the concentration of radium in Gd$_2$(SO$_4$)$_3{\cdot}$8H$_2$O is determined…
We develop a new compressive sensing (CS) inversion algorithm by utilizing the Gaussian mixture model (GMM). While the compressive sensing is performed globally on the entire image as implemented in our lensless camera, a low-rank GMM is…
In an ideal germanium detector, fully-absorbed monoenergetic gamma-rays will appear in the measured spectrum as a narrow peak, broadened into a Gaussian of width determined only by the statistical properties of charge cloud generation and…
Particle identification in large high-energy physics experiments typically relies on classifiers obtained by combining many experimental observables. Predicting the probability density function (pdf) of such classifiers in the multivariate…
Quantum-inspired machine learning (QiML) employs mathematical principles from quantum theory, such as Hilbert-space representations and quantum state discrimination, to enhance classical learning algorithms. In this work, we investigate the…
Searches for new physics push experiments to look for increasingly rare interactions. As a result, detectors require increasing sensitivity and specificity, and materials must be screened for naturally occurring, background-producing…
Measurements with a bare p-type high purity germanium diode (HPGe) submerged in a 19 kg liquid argon (LAr) scintillation detector at MPIK Heidelberg are reported. The liquid argon--germanium system (LArGe) is operated as a 4$\pi$…
The development of germanium Compton telescopes for nuclear gamma-ray astrophysics (~0.2-20 MeV) requires new event reconstruction techniques to accurately determine the initial direction and energy of photon events, as well as to…
Unsupervised anomaly detection aims to identify anomalous samples from highly complex and unstructured data, which is pervasive in both fundamental research and industrial applications. However, most existing methods neglect the complex…
This study presents a detailed simulation-based analysis of the detection limits of multi-element monolithic Germanium (Ge) detectors to cadmium traces in environmental soil samples. Using the capabilities of the Geant4 Monte Carlo toolkit…
Learning a Gaussian Mixture Model (GMM) is hard when the number of parameters is too large given the amount of available data. As a remedy, we propose restricting the GMM to a Gaussian Markov Random Field Mixture Model (GMRF-MM), as well as…
Masked Autoencoders (MAEs) have emerged as a dominant strategy for self-supervised representation learning in natural images, where models are pre-trained to reconstruct masked patches with a pixel-wise mean squared error (MSE) between…
Various methods of identification of single events in the HPGe detector are compared on the basis of a program especially designed to simulate pulse shape in a semi-conductor germanium detector. Capabilities of three following methods are…
Photon imaging for MeV gammas has serious difficulties due to huge backgrounds and unclearness in images, which are originated from incompleteness in determining the physical parameters of Compton scattering in detection, e.g., lack of the…
Gravitational microlensing of gamma-ray bursts (GRBs) provides a unique opportunity to probe compact dark matter and small-scale structures in the Universe. However, identifying such microlensed GRBs within large data sets is a significant…
The supporting instrument on board the Fermi Gamma-ray Space Telescope, the Gamma-ray Burst Monitor (GBM) is a wide-field gamma-ray monitor composed of 14 individual scintillation detectors, with a field of view which encompasses the entire…
During the last decade, a large number of different numerical methods have been proposed to tackle the automatic identification and quantification in {\gamma}-ray spectrometry. However, the lack of common benchmarks, including datasets,…
Machine learning techniques are now well established in experimental particle physics, allowing detector data to be analysed in new and unique ways. The identification of signals in particle observatories is an essential data processing…
Semantic segmentation of electron microscopy (EM) images of biological samples remains a challenge in the life sciences. EM data captures details of biological structures, sometimes with such complexity that even human observers can find it…