Related papers: HPGe-Compton Net: A Physics-Guided CNN for Fast Ga…
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…
We describe a new high-purity germanium (HPGe) detector setup for low-background {\gamma}-ray spectrometry. The GeMSE facility (Germanium Material and meteorite Screening Experiment) is dedicated to material screening for rare event…
A novel Compton Scanner setup has been built, commissioned and operated at the Max-Planck-Institute for Physics in Munich to collect pulses from bulk events in high-purity germanium detectors for pulse shape studies. In this fully automated…
We present extensive simulation studies on the performance of algorithms for the Compton sequence reconstruction used for the development of a portable spectroscopic instrument (COCAE), with the capability to localize and identify…
We report on an hybrid in-beam PET and prompt-gamma Compton imaging system aimed at quasi real-time ion-range verification in proton-therapy treatments. Proof-of-concept experiments were carried out at the radiobiology beam line of the CNA…
Using only global image-class labels, weakly-supervised learning methods, such as class activation mapping, allow training CNNs to jointly classify an image, and locate regions of interest associated with the predicted class. However,…
The neutrons emitted in heavy-ion fusion-evaporation (HIFE) reactions together with the gamma-rays cause unwanted backgrounds in gamma-ray spectra. Especially in the nuclear reactions, where relativistic ion beams (RIBs) are used, these…
We present a custom implementation of a 2D Convolutional Neural Network (CNN) as a viable application for real-time data selection in high-resolution and high-rate particle imaging detectors, making use of hardware acceleration in high-end…
We investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid…
We have measured the energy dependence of the liquid xenon (LXe) scintillation yield of electrons with energy between 2.1 and 120.2keV, using the Compton coincidence technique. A LXe scintillation detector with a very high light detection…
GRETA, the Gamma-Ray Energy Tracking Array, is an array of highly-segmented HPGe detectors designed to track gamma-rays emitted in beam-physics experiments. Its high detection efficiency and state-of-the-art position resolution make it…
Chest radiographs are used for the diagnosis of multiple critical illnesses (e.g., Pneumonia, heart failure, lung cancer), for this reason, systems for the automatic or semi-automatic analysis of these data are of particular interest. An…
High-purity germanium (HPGe) crystals are required to be well-characterized before being fabricated into Ge detectors. The characterization of HPGe crystals is often performed with the Hall Effect system, which measures the carrier…
Convolutional Neural Networks (CNNs) combine large amounts of parallelizable computation with frequent memory access. Field Programmable Gate Arrays (FPGAs) can achieve low latency and high throughput CNN inference by implementing dataflow…
Large, high-purity, germanium (HPGe) detectors are needed for neutrinoless double-beta decay and dark matter experiments. Currently, large (> 4 inches in diameter) HPGe crystals can be grown at the University of South Dakota (USD). We…
The full-energy-peak efficiency of HPGe detectors at $\gamma$-ray energies around 10 MeV is not easily accessible with experimental methods. Monte-Carlo simulations with Geant4 can provide these efficiencies. G4Horus is a ready-to-use…
Neutrinoless double beta decay ($0\nu\beta\beta$) offers a sensitive probe of neutrino mass and its Majorana nature. Orthogonal-strip high-purity germanium (HPGe) detectors with high spatial resolution provide a promising approach for…
In this paper, we optimize a faster region-based convolutional neural network (FRCNN) for 1-dimensional (1D) signal processing and electromagnetic spectrum sensing. We target a cluttered radio frequency (RF) environment, where multiple RF…
This paper introduces a robust stress detection system utilizing a Convolutional Neural Network (CNN) designed for the analysis of Photoplethysmogram (PPG) signals. Employing the WESAD dataset, we applied Continuous Wavelet Transform (CWT)…
We are evaluating the performance of a Passive Gamma Emission Tomography (PGET) device equipped with 3D position-sensitive cadmium zinc telluride (CZT) gamma-ray detectors when used for inspecting spent nuclear fuel assemblies (SFAs).…