Speaker
Description
Widely adopted in high-energy physics experiments, Resistive Plate Chambers (RPCs) face a significant challenge due to new regulations that severely limit allowable greenhouse gas emissions — including those typically used in RPC operation. As a result, finding a mixture with low global warming potential has become a priority for the gas detector community. In this context, we present an investigation of iRPC waveforms obtained during a muon test beam at CERN’s Gamma Irradiation Facility conducted in 2026. Our approach incorporates waveform analysis and machine-learning algorithms to classify signals from iRPC chambers operating on an HFO1233ZD-based mixture, as a candidate to replace SF6. The machine learning model aims to improve the understanding of discharge mechanisms in candidate gases and to enrich insights from analytical methods