2 citations · 3 across the 5 of their papers we have counts for
7 papers · 1 filter
On the Codesign of Scientific Experiments and Industrial Systems
Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas +84
The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for in…
Physics Instrument Design with Reinforcement Learning
Shah Rukh Qasim, Patrick Owen, Nicola Serra
We present a case for the use of Reinforcement Learning (RL) for the design of physics instrument as an alternative to gradient-based instrument-optimization methods. It's applicab…
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…
Multi-particle reconstruction in the High Granularity Calorimeter using object condensation and graph neural networks
Shah Rukh Qasim, Kenneth Long, Jan Kieseler +2
The high-luminosity upgrade of the LHC will come with unprecedented physics and computing challenges. One of these challenges is the accurate reconstruction of particles in events…
Construction and commissioning of CMS CE prototype silicon modules
B. Acar, G. Adamov, C. Adloff +327
As part of its HL-LHC upgrade program, the CMS Collaboration is developing a High Granularity Calorimeter (CE) to replace the existing endcap calorimeters. The CE is a sampling cal…
The DAQ system of the 12,000 Channel CMS High Granularity Calorimeter Prototype
B. Acar, G. Adamov, C. Adloff +327
The CMS experiment at the CERN LHC will be upgraded to accommodate the 5-fold increase in the instantaneous luminosity expected at the High-Luminosity LHC (HL-LHC). Concomitant wit…