Related papers: Physics-informed machine learning approaches to re…
A first study of neutron tagging is conducted in Super--Kamiokande, a 50,000-ton water Cherenkov detector. The tagging efficiencies of thermal neutrons are evaluated in a 0.2 % GdCl$_{3}$-water solution and pure water. They are determined…
Radiogenic neutrons emitted by detector materials are one of the most challenging backgrounds for the direct search of dark matter in the form of weakly interacting massive particles (WIMPs). To mitigate this background, the XENONnT…
A major task in particle physics is the measurement of rare signal processes. Even modest improvements in background rejection, at a fixed signal efficiency, can significantly enhance the measurement sensitivity. Building on prior research…
We describe a neural network for predicting the background hit rate in the Belle II detector produced by the SuperKEKB electron-positron collider. The neural network, BGNet, learns to predict the individual contributions of different…
In a conventional light water reactor loaded with a range of uranium and plutonium-based fuel mixtures, the variation in antineutrino production over the cycle reflects both the initial core fissile inventory and its evolution. Under the…
We propose modifying large water \v{C}erenkov detectors by the addition of 0.2% gadolinium trichloride, which is highly soluble, newly inexpensive, and transparent in solution. Since Gd has an enormous cross section for radiative neutron…
In this paper, we present a new approach to learning cascaded classifiers for use in computing environments that involve networks of heterogeneous and resource-constrained, low-power embedded compute and sensing nodes. We present a…
Antineutrinos stream freely from rapidly decaying fission products within the cores of nuclear reactors and from long-lived natural radioactivity within the rocky layers of the Earth. These global antineutrinos produce detectable signals in…
Even though convolutional neural networks can classify objects in images very accurately, it is well known that the attention of the network may not always be on the semantically important regions of the scene. It has been observed that…
An aboveground 60-kg reactor-antineutrino detector prototype, comprised of a 2-dimensional array of 36 $^{6}$Li-doped pulse shape sensitive plastic scintillator bars, is described. Each bar is 50~cm long with a square cross section of…
We present a new technique for observing low energy neutrinos with the aim of detecting the cosmic neutrino background using ion storage rings. Utilising high energy targets exploits the quadratic increase in the neutrino capture cross…
We discuss the possibility of forecasting earthquakes by means of (anti)neutrino tomography. Antineutrinos emitted from reactors are used as a probe. As the antineutrinos traverse through a region prone to earthquakes, observable variations…
Nuclear reactors have long been a favored source for antineutrino measurements for estimates of power and burnup. With appropriate detector parameters and background rejection, an estimate of the reactor power can be derived from the…
Identification of Cherenkov light generated by muons has been suggested as a promising way to dramatically improve the background rejection power of Imaging Atmospheric Cherenkov Telescope (IACT) arrays at high energies. However, muon…
Ring Imaging Cherenkov (RICH) detectors are a key component of particle identification systems in many particle, nuclear and astroparticle physics experiments. Their ultimate performance depends not only on detector design and hardware…
In modern nuclear physics experiments, identifying events of interest is challenging for nuclear reaction studies with the active target Time Projection Chamber (TPC). In this work, machine learning techniques are employed to analyze the…
Jet measurements in heavy ion collisions can provide constraints on the properties of the quark gluon plasma, but the kinematic reach is limited by a large, fluctuating background. We present a novel application of symbolic regression to…
With the flourishing development of nanophotonics, Cherenkov radiation pattern can be designed to achieve superior performance in particle detection by fine-tuning the properties of metamaterials such as photonic crystals (PCs) surrounding…
In machine learning Trojan attacks, an adversary trains a corrupted model that obtains good performance on normal data but behaves maliciously on data samples with certain trigger patterns. Several approaches have been proposed to detect…
Imaging atmospheric Cherenkov telescopes (IACTs) are sensitive to rare gamma-ray photons, buried in the background of charged cosmic-ray (CR) particles, the flux of which is several orders of magnitude greater. The ability to separate gamma…