Related papers: ML for the hKLM at the 2nd Detector
Scintillator-SiPM Particle Detectors (SSPDs) are compact, low-power devices with applications including particle physics, underground tomography, cosmic-ray studies, and space instrumentation. They are based on a prism-shaped scintillator…
We describe another detectora designed for the International Linear Collider based on several tested instrumentation innovations in order to achieve the necessary experi- mental goal of a detecter that is 2-to-10 times better than the…
Water Cherenkov detectors like Super-Kamiokande, and the next generation Hyper-Kamiokande are adding gadolinium to their water to improve the detection of neutrons. By detecting neutrons in addition to the leptons in neutrino interactions,…
We study the light output, light collection efficiency and signal timing of a variety of organic scintillators that are being considered for the upgrade of the hadronic calorimeter of the CMS detector. The experimental data are collected at…
We propose a new scintillation-type detector in which high-energy radiation produces electron-hole pairs in a direct-gap semiconductor material that subsequently recombine producing infrared light to be registered by a photo-detector. The…
This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise…
Electricity theft, or non-technical loss (NTL), presents a persistent threat to global power systems, driving significant financial deficits and compromising grid stability. Conventional detection methodologies, predominantly reactive and…
This paper presents a Temporal Graph Neural Network (TGNN) framework for detection and localization of false data injection and ramp attacks on the system state in smart grids. Capturing the topological information of the system through the…
The high energy physics (HEP) community has a long history of dealing with large-scale datasets. To manage such voluminous data, classical machine learning and deep learning techniques have been employed to accelerate physics discovery.…
The replacement of the existing endcap calorimeter in the Compact Muon Solenoid (CMS) detector for the high-luminosity LHC (HL-LHC), scheduled for 2027, will be a high granularity calorimeter. It will provide detailed position, energy, and…
Precise and timely fault diagnosis is a prerequisite for a distribution system to ensure minimum downtime and maintain reliable operation. This necessitates access to a comprehensive procedure that can provide the grid operators with…
Molecular Dynamics (MD) simulation is a powerful tool for understanding the dynamics and structure of matter. Since the resolution of MD is atomic-scale, achieving long time-scale simulations with femtosecond integration is very expensive.…
NuTeV is a neutrino-nucleon deep inelastic scattering experiment at Fermilab. The NuTeV detector is a traditional heavy target neutrino detector which consists of an iron/liquid scintillator sampling calorimeter followed by a muon…
The growing demand for the internet of things (IoT) makes it necessary to implement computer vision tasks such as object recognition in low-power devices. Convolutional neural networks (CNNs) are a potential approach for object recognition…
The purpose of the MIPP experiment is to study the inclusive production of photons, pions, kaons and nucleons in pi, K and p interactions on various targets using beams from the Main Injector at Fermilab. The function of the calorimeters is…
The reliable detection of neutrons in a harsh gamma-ray environment is an important aspect of establishing non-destructive methods for the characterization of spent nuclear fuel. In this study, we present results from extended in-situ…
The iron calorimeter (ICAL) detector at the India-based Neutrino Observatory (INO) will be used to measure neutrino mass hierarchy. The magnet in the ICAL detector will be used to distinguish the {\mu^-} and {\mu^+} events induced by…
Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off-detector logic where trigger decisions are made. We…
The Large Hadron Collider at CERN produces immense volumes of complex data from high-energy particle collisions, demanding sophisticated analytical techniques for effective interpretation. Neural Networks, including Graph Neural Networks,…
Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Compared with intrusive load monitoring, NILM (Non-intrusive load monitoring) is low cost,…