Related papers: Hadron Identification Prospects With Granular Calo…
The lateral and longitudinal profiles of the hadronic showers detected by iron-scintillator tile hadron calorimeter with longitudinal tile configuration have been investigated. The results are based on 100 GeV pion beam data. Due to the…
The accurate and precise extraction of information from a modern particle physics detector, such as an electromagnetic calorimeter, may be complicated and challenging. In order to overcome the difficulties we propose processing the detector…
Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of particles produced in high-energy physics collisions. We train neural networks…
The precise modeling of subatomic particle interactions and propagation through matter is paramount for the advancement of nuclear and particle physics searches and precision measurements. The most computationally expensive step in the…
The paper describes a method of the charged particle identification, developed for the \mbox{CMD-3} detector, installed at the VEPP-2000 $e^{+}e^{-}$ collider. The method is based on the application of the boosted decision trees…
A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is…
The present research applies Graph Neural-Networks (GNNs) for energy measurement and particle identification tasks for a proposed second detector at the future Electron Ion Collider (EIC). In particular, an iron-scintillator sampling…
The pursuit of understanding fundamental particle interactions has reached unparalleled precision levels. Particle physics detectors play a crucial role in generating low-level object signatures that encode collision physics. However,…
We performed a Geant4 simulation study on showers generated by electrons and hadrons in a large homogeneous calorimeter. We found that the energy deposit can be expressed as a linear function of the track length. The line does not pass…
Precision physics at future multi-TeV lepton colliders such as CLIC requires excellent jet energy resolution. The detectors need deep calorimeter systems to limit the energy leakage also for very highly energetic particles and jets. At the…
Next-generation high-energy electron-positron colliders, operating as Higgs factories, require an unprecedented jet energy resolution for precision measurements of Higgs and Z/W bosons. To address this challenge, a conceptual design is…
Collider experiments, such as those at the Large Hadron Collider, use the Geant4 toolkit to simulate particle-detector interactions with high accuracy. However, these experiments increasingly require larger amounts of simulated data,…
We study whether machine-learning models for fast calorimeter simulations can learn meaningful representations of calorimeter signatures that account for variations in the full particle detector's configuration. This may open new…
A detailed study of hadronic interactions is presented using data recorded with the highly granular CALICE silicon-tungsten electromagnetic calorimeter. Approximately 350,000 selected negatively charged pion events at energies between 2 and…
High density electromagnetic sandwich calorimeters with high readout granularity are considered for many future collider and fix-target experiments. Optimization of the calorimeter structure from the point of view of the electromagnetic…
Materials data, especially those related to high-temperature properties, pose significant challenges for machine learning models due to extreme skewness, wide feature ranges, modality, and complex relationships. While traditional models…
Power and thermal management are critical components of High-Performance-Computing (HPC) systems, due to their high power density and large total power consumption. The assessment of thermal dissipation by means of compact models directly…
In the present work, accurate determination of single-particle ignition is focused on using high-speed optical diagnostics combined with machine learning approaches. Ignition of individual particles in a laminar flow reactor are visualized…
Gas cooling and heating functions play a crucial role in galaxy formation. But, it is computationally expensive to exactly compute these functions in the presence of an incident radiation field. These computations can be greatly sped up by…
We derive a new method to improve the statistics of identified particles at high transverse momentum (\pt) using online-triggered events by the Barrel Electro-Magnetic-Calorimeter (BEMC) detector. The BEMC is used to select hadronic…