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Description
Resistive plate chambers (RPCs) are widely used in high-energy physics. The induced signal is a waveform correlated with the avalanche production. Waveform studies, besides allowing the measurement of induced electric charge and time quantities, can also serve as a probe for analyzing individual avalanche processes. Given new regulations that drastically limit greenhouse gas emissions, including those from RPCs, such studies are essential for guiding the search for eco-friendly gas mixtures and for refining simulation tools like Garfield++ and Magboltz. In this work, we present a study of the waveforms produced by improved Resistive Plate Chambers (iRPCs) during the Muon test beam at the CERN Gamma Irradiation Facility, including a machine-learning-based analysis for signal classification. This tool will be used as a complementary method in the search for alternative mixtures, to improve understanding of the discharge processes of the candidates and to complement traditional analysis methods.