The ever-growing amounts of data produced by the High Energy Physics experiments create a need for fast and efficient track reconstruction algorithms. When storing all incoming information is not feasible, online algorithms need to provide reconstruction quality similar to their offline counterparts. To achieve it, novel techniques need to be developed, utilizing acceleration offered by the highly parallel hardware platforms, like GPUs. Artificial Neural Networks are a natural candidate here, thanks to their good pattern recognition abilities, non-iterative execution, and easy implementation on hardware accelerators. The MUonE experiment, which aims to search for signs of new physics through a precision measurement of the anomalous magnetic moment of the muon, is exploring the application of machine learning (ML) techniques for data analysis and processing. Works related to the ML-based track reconstruction are described in the presented document. This first attempt used a deep multilayer perceptron network to predict parameters of the tracks in the detector. Neural network was used as the base of the algorithm that proved to be as accurate as the classical approach but substituting the tedious step of iterative, highly time-consuming CPU-based pattern recognition.
Zdybał et al. (Wed,) studied this question.