Related papers: ML for the hKLM at the 2nd Detector
The energy resolution of a highly granular 1 m3 analogue scintillator-steel hadronic calorimeter is studied using charged pions with energies from 10 GeV to 80 GeV at the CERN SPS. The energy resolution for single hadrons is determined to…
Two detectors for energy-resolved fast-neutron imaging in pulsed broad-energy neutron beams are presented. The first one is a neutron-counting detector based on a solid neutron converter coupled to a gaseous electron multiplier (GEM). The…
This study constructs a hybrid module with a multi-material collaborative detection architecture by integrating silicon pixel layers into the longitudinally segmented scintillating fiber sampling calorimeter module and optimizes the…
Position sensitive detectors based on gaseous scintillation proportional counters with Anger-type readout are being used in several research areas such as neutron detection, search for dark matter and neutrinoless double beta decay. Design…
To study the dependence of the equation of state of high density nuclear matter on the term characterizing the isospin (proton-neutron) asymmetry of nuclear matter, it is necessary to measure azimuthal flow of neutrons as well as azimuthal…
Particle identification using the energy loss in silicon detectors is a powerful technique for probing the Standard Model (SM) as well as searching for new particles beyond the SM. Traditionally, such techniques use the truncated mean of…
Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Circular Collider. We study scalable machine learning models…
Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models. While most existing GNN models for…
Accurately predicting adsorption properties in nanoporous materials using Deep Learning models remains a challenging task. This challenge becomes even more pronounced when attempting to generalize to structures that were not part of the…
Photonic computing shows great potential for signal processing and artificial intelligence (AI) acceleration due to its ultra-high speed, low energy consumption, and inherent parallelism. Existing photonic computing research has mainly…
The work function and cleavage energy of a surface are critical properties that determine the viability of materials in electronic emission applications, semiconductor devices, and heterogeneous catalysis. While first principles…
Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a…
We present a scintillator based detector able to measure both spatial and energy information at High repetition rate (HRR) with a relatively simple design. It has been built at the Center of Pulsed Laser (CLPU) in Salamanca and tested in…
A prototype Scintillator-Tungsten electromagnetic calorimeter (ScECAL) for the ILC detector was tested in 2008 at the Fermilab test beam. Data were collected with electron, pion and muon beams in the energy range 1 to 32GeV combined with…
We present the study of a fuzzy clustering algorithm for the Belle II electromagnetic calorimeter using Graph Neural Networks. We use a realistic detector simulation including simulated beam backgrounds and focus on the reconstruction of…
Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with…
A prototype for the instrumented decay tunnel of ENUBET was tested in 2018 at the CERN East Area facility with charged particles up to 5 GeV. This detector is a longitudinal sampling calorimeter with lateral scintillation light readout. The…
The recently proposed crystal graph convolutional neural network (CGCNN) offers a highly versatile and accurate machine learning (ML) framework by learning material properties directly from graph-like representations of crystal structures…
An iron- plastic-scintillator shashlik calorimeter with a 4.3 $X_0$ longitudinal segmentation was tested in November 2016 at the CERN East Area facility with charged particles up to 5 GeV. The performance of this detector in terms of…
Radar processing via spiking neural networks (SNNs) has recently emerged as a solution in the field of ultra-low-power wireless human-computer interaction. Compared to traditional energy- and area-hungry deep learning methods, SNNs are…