A new approach in modeling the response of RPC detectors
L. Benussi (1), S. Bianco (1), S.Colafranceschi (1, 2, 3), F.L., Fabbri (1), M. Giardoni (1), L. Passamonti (1), D. Piccolo (1), D. Pierluigi, (1), A. Russo (1), G. Saviano (1, 2), S. Buontempo (4), A. Cimmino (4 and, 5), M. de Gruttola (4, 5), F Fabozzi (4), A.O.M. Iorio (4, 5)
TL;DR
This paper introduces a novel neural network-based model to predict RPC detector responses under various environmental conditions, enhancing understanding and monitoring during system commissioning.
Contribution
It presents a new AI-driven modeling approach specifically designed for RPC detectors, improving accuracy over traditional methods.
Findings
Effective modeling of RPC responses across different conditions
Successful deployment on CMS RPC gas gain monitoring system
Potential for improved detector calibration and monitoring
Abstract
The response of RPC detectors is highly sensitive to environmental variables. A novel approach is presented to model the response of RPC detectors in a variety of experimental conditions. The algorithm, based on Artificial Neural Networks, has been developed and tested on the CMS RPC gas gain monitoring system during commissioning.
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