Deep learning based tracking reconstruction and magnetic field measurement research in the muon g-2 experiment

Abstract

The Run1 result of the Fermilab muon g-2 experiment have shown a 4.2 standard deviation between the experimental measurement and theoretical prediction of $a_{\mu }$, strongly indicating a new physics signal. The Fermilab experiment already accumulated 21 times more data compared to the BNL experiment. The J-PARC muon g-2 experiment will collect 3.5 times the statistics compared to Fermilab. With the increases in the collected data volume, and limited by the speed and accuracy, the existing tracking reconstruction and magnetic field measurement method may not fully satisfy the requirement of the experiment. The breakthrough of the deep learning inspires new analysis method in the muon g-2 experiment. In this proceeding, we will present some preliminary research on the tracking reconstruction based on Recurrent Neural Network (RNN), Graph Neural Network (GNN) and the magnetic field measurement based on Physics Informed Neural Network (PINN). The preliminary results show that the deep learning method has enormous potential in these topics.