Related papers: Deep learning based pulse shape discrimination for…

We train a deep neural network (DNN) to output rates of dark matter (DM) induced electron excitations in silicon and germanium detectors. Our DNN provides a massive speedup of around $5$ orders of magnitude relative to existing methods…

Neutrinoless double-beta decay is practically the only way to establish the Majorana nature of the neutrino mass and its decay rate provides a probe of an effective neutrino mass. Double beta experiments are long-running underground…

We present studies of quantum algorithms exploiting machine learning to classify events of interest from background events, one of the most representative machine learning applications in high-energy physics. We focus on variational quantum…

Distribution-level phasor measurement units, a.k.a, micro-PMUs, report a large volume of high resolution phasor measurements which constitute a variety of event signatures of different phenomena that occur all across power distribution…

Among the detectors used for rare event searches, such as neutrinoless Double Beta Decay (0$\nu$DBD) and Dark Matter experiments, bolometers are very promising because of their favorable properties (excellent energy resolution, high…

We study several simplified dark matter (DM) models and their signatures at the LHC using neural networks. We focus on the usual monojet plus missing transverse energy channel, but to train the algorithms we organize the data in 2D…

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…

We propose a deep learning approach for discovering kernels tailored to identifying clusters over sample data. Our neural network produces sample embeddings that are motivated by--and are at least as expressive as--spectral clustering. Our…

We present a novel unsupervised deep learning framework for anomalous event detection in complex video scenes. While most existing works merely use hand-crafted appearance and motion features, we propose Appearance and Motion DeepNet (AMDN)…

We report in situ neutron background measurements at the Kuo-Sheng Reactor Neutrino Laboratory (KSNL) by a hybrid neutron detector (HND) with a data size of 33.8 days under identical shielding configurations as during the neutrino physics…

The GERmanium Detector Array (GERDA) collaboration searched for neutrinoless double-$\beta$ decay in $^{76}$Ge with an array of about 40 high-purity isotopically-enriched germanium detectors. The experimental signature of the decay is a…

Alpha particle emission, even at extremely low levels, is a significant issue in the search for rare events (e.g., double beta decay, dark matter detection). Traditional measurement techniques require long counting times to measure low…

We present a new detection scheme for rejecting backgrounds in neutrino less double beta decay experiments. It relies on the detection of Cherenkov light emitted by electrons in the MeV region. The momentum threshold is tuned to reach a…

The study of neutrinoless double beta decay has attracted much attention as it can provide valuable information about the mass and the nature of the neutrino. The double beta decay (DBD) itself is also of interest in nuclear physics. While…

Exploring hit positions of recorded events can help to understand and suppress backgrounds in rare event searching experiments. In this study, we virtually segment a small contact P-type high purity germanium detector (HPGe) into two…

High-Purity Germanium~(HPGe) detectors have been a key technology for rare-event searches, such as neutrinoless double-beta decay and dark matter searches, for many decades. Pulse shape simulation is pivotal to improving the physics reach…

Of the many extensions to the standard model that could possibly generate neutrino mass, most necessitate the neutrino being a Majorana fermion. If this is the case, the rare process of neutrinoless double beta decay is predicted with half…

A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz-boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods…

Event-based cameras are bio-inspired sensors that capture brightness change of every pixel in an asynchronous manner. Compared with frame-based sensors, event cameras have microsecond-level latency and high dynamic range, hence showing…

Several neutrino detectors, KamLAND, Daya Bay, Double Chooz, RENO, and the forthcoming large-scale JUNO, rely on liquid scintillator to detect reactor antineutrino interactions. In this context, inverse beta decay represents the golden…