{'fraction_non_alphanumeric': 0.024287222808870117, 'fraction_numerical': 0.0021119324181626186, 'mean_word_length': 4.851851851851852, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Deep neural network applications for particle tracking at the BM@N and SPD experiments

Particle tracking is an essential part of any high-energy physics experiment. Well-known tracking algorithms based on the Kalman filter are not scaling well with the amounts of data being produced in modern experiments. In our work we present a particle tracking approach based on deep neural networks for the BM@N experiment and future SPD experiment. We have already applied similar approaches for BM@N RUN 6 and BES-III Monte-Carlo simulation data. This work is the next step in our ongoing study of tracking with the help of machine learning. Revised algorithms - combination of Recurrent Neural Network (RNN) and Graph Neural Network (GNN) for the BM@N RUN 7 Monte-Carlo simulation data, and GNN for the preliminary SPD Monte-Carlo simulation data are presented. Results of the track efficiency and processing speed for both experiments are demonstrated.