Related papers: Physics-informed machine learning approaches to re…
The possibilities of detecting high energy neutrinos through inclined showers produced in the atmosphere are addressed with an emphasis on the detection of air showers by arrays of particle detectors. Rates of inclined showers produced by…
The MeV region of antineutrino energy is of special interest for physics research and for monitoring nuclear nonproliferation. Whereas liquid scintillation detectors are typically used to detect the Inverse Beta Decay (IBD), it has recently…
Imaging atmospheric Cherenkov telescope (IACT) arrays record images from air showers initiated by gamma rays entering the atmosphere, allowing astrophysical sources to be observed at very high energies. To maximize IACT sensitivity,…
This study explores the use of charge-coupled devices (CCDs) for detecting low-energy beta particles from tritium decay - a critical signal for nuclear safety, nuclear nonproliferation, and environmental monitoring. We employ a dual…
Machine learning, through the use of convolutional and recurrent neural networks is a promising avenue for the improvement of background rejection performance in imaging atmospheric Cherenkov telescopes. However, it is of paramount…
This study developed a novel method for detecting hypernuclear events recorded in nuclear emulsion sheets using machine learning techniques. The artificial neural network-based object detection model was trained on surrogate images created…
We introduce a novel method for studying systematic trends in nuclear reaction data using generative adversarial networks. Libraries of nuclear cross section evaluations exhibit intricate systematic trends across the nuclear landscape, and…
There is a growing interest in very long baseline neutrino oscillation experimentation using accelerator produced neutrino beam as a machinery to probe the last three unmeasured neutrino oscillation parameters: the mixing angle theta_13,…
Atmospheric neutrino oscillations are important to the study of neutrino properties, including the neutrino mass ordering problem. A good capability to identify neutrinos' flavor and neutrinos against antineutrinos is crucial in such…
In this work, we explore the use of deep learning techniques to learn how nuclear cross sections change as we add or remove protons and neutrons. As a proof of principle, we focus on the neutron-induced reactions in the fast energy regime.…
We propose a new method for the discrimination of sub-micron nuclear recoil tracks from an instrumental background in fine-grain nuclear emulsions used in the directional dark matter search. The proposed method uses a 3D Convolutional…
Imaging Cherenkov detectors are largely used for particle identification (PID) in nuclear and particle physics experiments, where developing fast reconstruction algorithms is becoming of paramount importance to allow for near real time…
We leverage representation learning and the inductive bias in neural-net-based Standard Model jet classification tasks, to detect non-QCD signal jets. In establishing the framework for classification-based anomaly detection in jet physics,…
The precise measurement of the antineutrino spectra produced by isotope fission in reactors is of great significance for studying neutrino oscillations, refining nuclear databases, and addressing the reactor antineutrino anomaly. In this…
In the next 10 years medium baseline reactor neutrino experiments will attempt to determine the neutrino mass hierarchy and to precisely measure {\theta}_12. Both of these determinations will be more reliable if data from identical…
Weakly supervised methods have emerged as a powerful tool for model-agnostic anomaly detection at the Large Hadron Collider (LHC). While these methods have shown remarkable performance on specific signatures such as di-jet resonances, their…
Infrared target detection (IRSTD) tasks have critical applications in areas like wilderness rescue and maritime search. However, detecting infrared targets is challenging due to their low contrast and tendency to blend into complex…
Large mass ice/water Cherenkov experiments, optimized to detect low energy (1-20 GeV) atmospheric neutrinos, have the potential to discriminate between normal and inverted neutrino mass hierarchies. The sensitivity depends on several model…
Deep neural networks (DNNs) have recently been applied to inverse scattering problems (ISPs) due to their strong nonlinear mapping capabilities. However, supervised DNN solvers require large-scale datasets, which limits their generalization…
Recoil-imaging gaseous time projection chambers (TPCs) with directional sensitivity are attractive for dark matter (DM) searches. Detectors capable of reconstructing 3D nuclear recoil directions would be uniquely sensitive to the predicted…