112 citations · 955 across the 37 of their papers we have counts for
41 papers
Response to cosmic muons of scintillator-SiPM assemblies measured at different temperatures
M. Chadeeva, A. Stifutkin
Highly segmented calorimeters represent a modern trend in experimental particle physics aimed at improving the energy resolution with the particle flow reconstruction. The widely u…
Software Compensation for Highly Granular Calorimeters using Machine Learning
S. Lai, J. Utehs, A. Wilhahn +61
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 in…
Timing Performance of the CMS High Granularity Calorimeter Prototype
CMS HGCAL collaboration
This paper describes the experience with the calibration, reconstruction and evaluation of the timing capabilities of the CMS HGCAL prototype in the beam tests in 2018. The calibra…
Performance of the CMS High Granularity Calorimeter prototype to charged pion beams of 20300 GeV/c
B. Acar, G. Adamov, C. Adloff +454
The upgrade of the CMS experiment for the high luminosity operation of the LHC comprises the replacement of the current endcap calorimeter by a high granularity sampling calorimete…
Design, Construction and Commissioning of a Technological Prototype of a Highly Granular SiPM-on-tile Scintillator-Steel Hadronic Calorimeter
CALICE Collaboration
The CALICE collaboration is developing highly granular electromagnetic and hadronic calorimeters for detectors at future energy frontier electron-positron colliders. After successf…
Machine-learning-based prediction of parameters of secondaries in hadronic showers using calorimetric observables
M. Chadeeva, S. Korpachev
The paper describes a novel neural-network-based approach to study the distributions of secondaries produced in hadronic showers using observables provided by highly granular calor…