Statistical modelling for the time projection chamber signal processing: how can statistics improve detector performances?

The aim of this contribution is to investigate on how to improve the extraction of physical information from the signals coming from a liquid Argon (LAr) time projection chamber (TCP), a particle detector technique characterized by good tracking and energy measurement capabilities. We present here the results obtained from the analysis of test pulse data, i.e. the electronic impulses that, on purpose of calibration and testing, stimulate the electronics simulating a known charge value as if it was released by a particle within the LAr. Starting from the analysis of those calibration data, we focused on getting a better modelling of the electronic noise, which results far from a white noise process. As a subsequent step, we identified a more suitable theoretical analytical function to perform the nonlinear least-squares fit of the signal, used to recover the parameters which are relevant for the physical analysis.