Digital Signal Analysis based on Convolutional Neural Networks for Active Target Time Projection Chambers

TL;DR

This paper introduces a CNN-based digital signal analysis algorithm for active target time projection chambers, significantly improving processing speed while maintaining accuracy, thus enabling more efficient data analysis in experimental physics.

Contribution

It presents a novel CNN-based method for signal analysis that is faster and equally accurate compared to traditional algorithms in active target experiments.

Findings

CNN achieves less than 6% relative error in signal processing

Algorithm is approximately 65 times faster than traditional methods

Enables more efficient analysis of large experimental data sets

Abstract

An algorithm for digital signal analysis using convolutional neural networks (CNN) was developed in this work. The main objective of this algorithm is to make the analysis of experiments with active target time projection chambers more efficient. The code is divided in three steps: baseline correction, signal deconvolution and peak detection and integration. The CNNs were able to learn the signal processing models with relative errors of less than 6\%. The analysis based on CNNs provides the same results as the traditional deconvolution algorithms, but considerably more efficient in terms of computing time (about 65 times faster). This opens up new possibilities to improve existing codes and to simplify the analysis of the large amount of data produced in active target experiments.