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arXiv · 2304.10806

Cluster counting algorithms for particle identification at future colliders

Brunella D'Anzi·Gianluigi Chiarello·Alessandro Corvaglia·Nicola De Filippis·Walaa Elmetenawee·Francesco De Santis·Edoardo Gorini·Francesco Grancagnolo·Marcello Maggi·Alessandro Miccoli·Marco Panareo·Margherita Primavera·Andrea Ventura·Shuiting Xin·Fangyi Guo·Shuaiyi Liu
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Abstract

Recognition of electron peaks and primary ionization clusters in real data-driven waveform signals is the main goal of research for the usage of the cluster counting technique in particle identification at future colliders. The state-of-the-art open-source algorithms fail in finding the cluster distribution Poisson behavior even in low-noise conditions. In this work, we present cutting-edge algorithms and their performance to search for electron peaks and identify ionization clusters in experimental data using the latest available computing tools and physics knowledge.

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Brunella D'Anzi, Gianluigi Chiarello, Alessandro Corvaglia, Nicola De Filippis, Walaa Elmetenawee, Francesco De Santis, Edoardo Gorini, Francesco Grancagnolo, Marcello Maggi, Alessandro Miccoli, Marco Panareo, Margherita Primavera, Andrea Ventura, Shuiting Xin, Fangyi Guo, Shuaiyi Liu. 2023-04-21. Cluster counting algorithms for particle identification at future colliders. https://arxiv.org/abs/2304.10806

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