29 citations · 89 across the 12 of their papers we have counts for
12 papers
High-granularity Dual-readout Calorimeter: Evolution of a Classic Prototype
N. Akchurin, J. Cash, J. Damgov +9
The original dual-readout calorimeter prototype (DREAM), constructed two decades ago, has proven instrumental in advancing our understanding of calorimetry. It has facilitated a mu…
Vertex Imaging Hadron Calorimetry Using AI/ML Tools
N. Akchurin, J. Cash, J. Damgov +9
The fluctuations in energy loss to processes that do not generate measurable signals, such as binding energy losses, set the limit on achievable hadronic energy resolution in tradi…
Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter
M. Aamir, G. Adamov, T. Adams +568
A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transv…
Deep learning applications for quality control in particle detector construction
N. Akchurin, J. Damgov, S. Dugad +7
The growing complexity of particle detectors makes their construction and quality control a new challenge. We present studies that explore the use of deep learning-based computer v…
Perspectives on the Calibration of CNN Energy Reconstruction in Highly Granular Calorimeters
N. Akchurin, C. Cowden, J. Damgov +2
We present a study which shows encouraging stability of the response linearity for a simulated high granularity calorimeter module reconstructed by a CNN model to miscalibration, b…
On the Use of Neural Networks for Energy Reconstruction in High-granularity Calorimeters
N. Akchurin, C. Cowden, J. Damgov +2
We contrasted the performance of deep neural networks - Convolutional Neural Network (CNN) and Graph Neural Network (GNN) - to current state of the art energy regression methods in…