Triple-GEM discharge probability studies at CHARM: simulations and experimental results

Abbas, M.; Abbrescia, M.; Abdalla, H.; Abdelalim, A. A.; AbuZeid, S.; Agapitos, A.; Ahmad, A.; Ahmed, A.; Ahmed, W.; Aimè, Chiara; Asghar, Ikram; Aspell, P.; Ávila, C.; Babbar, J.; Ban, Y.; Band, R.; Bansal, S.; Benussi, L.; Bhatnagar, V.; Bianco, M.; Bianco, S.; Black, K. M.; Borgonovi, L.; Bouhali, O.; Braghieri, A.; Braibant, S.; Butalla, S.; Calzaferri, S.; Caponero, M.; Cassese, Francesco Peluso; Cavallo, N.; Chauhan, S.; Colafranceschi, S.; Colaleo, A.; Garcia, A. Conde; Dalchenko, M.; Lentdecker, G. De; Olio, D. Dell; Robertis, G. De; Dharmaratna, W. G. D.; Dildick, S.; Dorney, B.; Erbacher, R.; Fabozzi, F.; Fallavollita, F.; Ferraro, Alberto; Fiorina, D.; Fontanesi, E.; Franco, M.; Galloni, C.; Giacomelli, P.; Gigli, S.; Gilmore, J.; Gola, M.; Gruchala, M.; Gutiérrez, A.; Hadjiiska, R.; Hakkarainen, T.; Hauser, J.; Höepfner, K.; Hohlmann, M.; Hoorani, H. R.; Huang, T.; Iaydjiev, P.; Irshad, A.; Iorio, A. De; Jaramillo, J.; Jeong, Deog‐Kyoon; Jha, V.; Juodagalvis, A.; Juska, E.; Kailasapathy, B.; Kamon, T.; Karchin, P. E.; Kaur, A.; Kaur, Harpreet; Keller, H.; Kim, H.; Kim, Jihye; Kumar, Ashok; Kumar, S.; Kumawat, H.; Lacalamita, N.; Lee, J. S. H.; Levin, A.; Li, Q.; Licciulli, F.; Lista, L.; Liyanage, K.; Loddo, F.; Lohan, M.; Luhach, M.; Maggi, M.; Majumdar, N.; Malagalage, Kithsiri; Malhorta, Shanti; Martiradonna, S.; Mccoll, N.; Mclean, C.; Merlin, Jeremie Alexandre; Mishra, D. K.; Mocellin, G.; Moureaux, L.; Muhammad, A.; Muhammad, S.; Mukhopadhyay, Supratik; Naimuddin, M.; Netrakanti, P. K.; Nuzzo, S.; Oliveira, Rui de; Pant, L. M.; Paolucci, P.; Park, I. C.; Passamonti, L.; Passeggio, G.; Peck, A.; Perera, N.; Pétré, L.; Petrow, H.; Piccolo, D.; Pierluigi, D.; Raffone, G.; Rahmani, M.; Ramírez, F.; Ranieri, A.; Rashevski, G.; Ressegotti, M.; Riccardi, C.; Rodozov, M.; Romano, E.; Roskas, C.; Rossi, B.; Rout, P. K.; Ruiz, J.D.; Russo, A.; Safonov, A.; Saltzberg, D.; Saviano, G.; Shah, Aashaq; Sharma, A.; Sharma, R.; Shopova, M.; Simonetto, F.; Singh, J. B.; Soldani, E.; Sonnadara, U.; Starling, E.; Stone, B.; Sturdy, J.; Sultanov, G.; Szillási, Z.; Teague, D.; Teyssier, D.; Tuuva, T.; Tytgat, M.; Vai, I.; Vanegas, N.; Venditti, R.; Verwilligen, P.; Vetens, W.; Virdi, A. K.; Vitulo, P.; Wajid, Abdul; Wang, D.; Wang, K.; Watson, I. J.; Wickramage, N.; Wickramarathna, D. D. C.; Yang, Yifan; Yang, U. K.; Yongho, J.; Yoon, I.; Zhou, You; Yu, I.; Zaleski, S.

doi:10.1088/1748-0221/15/10/p10013

Cited by 4 publications

(13 citation statements)

References 8 publications

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“…It is important to notice that the sensitivity results were obtained without constraints on particular radiation environment, the only information considered was the energy range for the different type of particles. This means that the results can be used in any experimental facilities in which similar triple-GEM detectors are installed [9]. The results presented in figure 3 left for single triple-GEM detector with basic geometry are qualitatively similar and quantitatively compatible with those presented in ref.…”

Section: Simulationsupporting

confidence: 86%

“…To study the radiation environment and its impact on detector performance dedicated simulation tools are used, those involve a description of complex physics processes and particle decay chains. The present study extends our previous work [7,9] by discussing in depth the simulation strategy and methodology, improvement in the detector description and the estimation of detector response for a single triple-GEM detector as a function of incident particle's kinematical properties (energy, momentum direction). This work could also be useful for other muon systems simulation studies to estimate background response [10,11].…”

Section: Introductionmentioning

confidence: 53%

“…The results presented in figure 3 left for single triple-GEM detector with basic geometry are qualitatively similar and quantitatively compatible with those presented in ref. [9]. The main difference is observed in neutron sensitivity at low energies which is largely attributed to the fact that Geant4 version in ref.…”

Section: Simulationmentioning

confidence: 99%

“…Detector response is evaluated using the sensitivity variable. Sensitivity is defined as the probability for a charged particle to deposit energy in the sensitive volume (e.g., Ar/CO 2 gas), producing primary ionized electrons [7,9]. Those electrons are multiplied, so the charge is large enough to be detected by a readout system with charge thresholds.…”

Section: Simulationmentioning

confidence: 99%

“…It should be emphasized here that instead of counting secondary charge particles in the gaps we require energy deposits which is equivalent to requiring minimum one simulated hit in the gaps and is also used in ref. [9]. The counting of secondary charges is not used due to the fact that Geant4 only reports them if they pass the default production cut threshold of 0.7 mm for track length [16].…”

Section: Simulationmentioning

confidence: 99%

See 4 more Smart Citations

Modeling the triple-GEM detector response to background particles for the CMS Experiment

Abbas,

Abbrescia,

Abdalla

et al. 2021

Preprint

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Section: Simulationsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 53%

Section: Simulationmentioning

confidence: 99%

Section: Simulationmentioning

confidence: 99%

Section: Simulationmentioning

confidence: 99%

See 3 more Smart Citations

Modeling the triple-GEM detector response to background particles for the CMS Experiment

Abbas,

Abbrescia,

Abdalla

et al. 2021

Preprint

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Numerical estimation of discharge probability in GEM-based detectors

Rout¹,

Kanishka²,

Datta³

et al. 2021

J. Inst.

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Discharge probability in GEM-based gaseous detectors has been numerically estimated using an axisymmetric hydrodynamic model. Initial primary charge configurations in the drift region, obtained using Heed and Geant4, are found to have significant effect on the subsequent evolution of detector response. Simulation of energy resolution has been performed to establish the capability of the hydrodynamic model to capture statistical nature of the experimental situation. Finally, single and triple GEM configurations exposed to alpha sources have been simulated to estimate discharge probability which have been compared with available experimental data. Despite the simplifying and drastic assumptions in the numerical model, the comparisons are encouraging.

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Benchmarking LHC background particle simulation with the CMS triple-GEM detector

et al. 2021

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In 2018, a system of large-size triple-GEM demonstrator chambers was installed in the CMS experiment at CERN's Large Hadron Collider (LHC). The demonstrator's design mimicks that of the final detector, installed for Run-3. A successful Monte Carlo (MC) simulation of the collision-induced background hit rate in this system in proton-proton collisions at 13 TeV is presented. The MC predictions are compared to CMS measurements recorded at an instantaneous luminosity of 1.5 ×1034 cm-2 s-1. The simulation framework uses a combination of the FLUKA and GEANT4 packages. FLUKA simulates the radiation environment around the GE1/1 chambers. The particle flux by FLUKA covers energy spectra ranging from 10-11 to 104 MeV for neutrons, 10-3 to 104 MeV for γ's, 10-2 to 104 MeV for e±, and 10-1 to 104 MeV for charged hadrons. GEANT4 provides an estimate of the detector response (sensitivity) based on an accurate description of the detector geometry, the material composition, and the interaction of particles with the detector layers. The detector hit rate, as obtained from the simulation using FLUKA and GEANT4, is estimated as a function of the perpendicular distance from the beam line and agrees with data within the assigned uncertainties in the range 13.7-14.5%. This simulation framework can be used to obtain a reliable estimate of the background rates expected at the High Luminosity LHC.

show abstract

Triple-GEM discharge probability studies at CHARM: simulations and experimental results

Cited by 4 publications

References 8 publications

Modeling the triple-GEM detector response to background particles for the CMS Experiment

Modeling the triple-GEM detector response to background particles for the CMS Experiment

Numerical estimation of discharge probability in GEM-based detectors

Benchmarking LHC background particle simulation with the CMS triple-GEM detector

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