Related papers: ML for the hKLM at the 2nd Detector
This study presents a deep learning approach to predicting structural and electronic properties of materials using Graph Neural Networks (GNNs). Leveraging data from the Materials Project database, we construct graph representations of…
Ultra-high-energy photons have long been sought as tracers of the most energetic processes in the Universe. Several sources can contribute to a diffuse photon flux, including interactions of cosmic rays with Galactic matter and radiation…
Neutrino detectors are among the largest photon detection instruments, built to capture scarce photons upon energy deposition. Many discoveries in neutrino physics, including the neutrino itself, are inseparable from the advances in photon…
Discovering new phenomena at the Large Hadron Collider (LHC) involves the identification of rare signals over conventional backgrounds. Thus binary classification tasks are ubiquitous in analyses of the vast amounts of LHC data. We develop…
In high-granularity calorimetry, as proposed for detectors at future Higgs factories, the requirements on electronics can have a strong impact on the design of the detector, especially via the cooling and acquisition systems. This project…
A neutron detector based on EJ301 liquid scintillator has been employed at EAST to measure the neutron energy spectrum for D-D fusion plasma. The detector was carefully characterized in different quasi-monoenergetic neutron fields generated…
The beam calorimeter, located in the very forward and backward region of the next generation electron-positron linear accelerator, will be an important apparatus to search for or identify new particles beyond Standard Model. One of its key…
Representing and learning from graphs is essential for developing effective machine learning models tailored to non-Euclidean data. While Graph Neural Networks (GNNs) strive to address the challenges posed by complex, high-dimensional graph…
Accurate thermal emission models of neutron stars are essential for constraining the dense matter equation of state. However, incorporating realistic magnetic field structures is computationally prohibitive, severely constraining feasible…
Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN…
Aiming to reduce the computational cost of numerical simulations, a convolutional neural network (CNN) and a multi-layer perceptron (MLP) are introduced to build a surrogate model to approximate radiative heat transfer solutions in a 2-D…
The last few years have seen an increasing wave of attacks with serious economic and privacy damages, which evinces the need for accurate Network Intrusion Detection Systems (NIDS). Recent works propose the use of Machine Learning (ML)…
Detectors based on liquid or gas xenon have been used and are in use for a number of applications, in particular for the detection of gamma rays. Xenon is a well-suited medium for gamma spectroscopy thanks to its high atomic number and,…
Conceiving the possibility of using plastic scintillator bars as robust detectors for antineutrino detection for the remote reactor monitoring and nuclear safeguard application we study expected basic performance by Monte Carlo simulation.…
The CALICE collaboration has constructed highly granular electromagnetic and hadronic calorimeter prototypes to evaluate technologies for the use in detector systems at the future International Linear Collider. These calorimeters have been…
An excellent hadron to electron discrimination is a crucial aspect of calorimeter-based experiments in astroparticle physics. Standard discrimination techniques require full shower development and fine granularity but in space detectors…
The high accuracy of detector simulation is crucial for modern particle physics experiments. However, this accuracy comes with a high computational cost, which will be exacerbated by the large datasets and complex detector upgrades…
In this paper, we aim at improving the computational efficiency of graph convolutional networks (GCNs) for learning on point clouds. The basic graph convolution that is typically composed of a $K$-nearest neighbor (KNN) search and a…
Parameter Estimation (PE) and State Estimation (SE) are the most wide-spread tasks in the system engineering. They need to be done automatically, fast and frequently, as measurements arrive. Deep Learning (DL) holds the promise of tackling…
The Industrial Internet of Things (IIoT) is reshaping manufacturing, industrial processes, and infrastructure management. By fostering new levels of automation, efficiency, and predictive maintenance, IIoT is transforming traditional…