Future e+e- colliders are crucial to extend the search for new phenomena possibly related to the open questions that the Standard Model presently does not explain. Among the major physics programs, the flavor physics program requires particle identification (PID) performances well beyond that of most detectors designed for the current generation. The cluster counting, which measures the number of primary ionizations (dN/dx) instead of the energy loss (dE/dx) along the particle trajectory in a gaseous detector, represents the most promising breakthrough in PID. The Poissonian nature of the dN/dx offers a more statistically significant way of ionization measurement, which makes the dN/dx potentially has a resolution two times better than the dE/dx. Drift chamber (DC) with cluster counting has been proposed as the future advanced detector candidates for Circular Electron Positron Collider (CEPC) and Future Circular Collider (FCC).
Machine learning (ML) algorithms, which are designed to exploit large datasets to reduce complexity and find new features in data, are the state-of-the-art in PID. The reconstruction of dN/dx measurement needs to determine the number of peaks associated with the primary ionizations in the induced current waveform in a DC cell. The major challenges of the reconstruction are to detect peaks in the highly pileup and noisy situations, and to discriminate the peaks formed by the primary and secondary ionizations. Traditional method, such as taking derivatives, can hardly reach the required efficiency due to the inefficient use of the information. In this study, a two-step ML based algorithm is developed for the dN/dx reconstruction. The algorithm consists of an RNN-based peak finding model, and a CNN-based discrimination model. According to the simulated results, the performance of the ML algorithm surpasses the derivative algorithm in terms of detection efficiency and resolution. The algorithm is further demonstrated by analyzing the test beam data taken at CERN and preliminary results will be presented.
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