A Domain Adaptive Position Reconstruction Method for Time Projection Chamber based on Deep Neural Network

TL;DR

This paper introduces a domain-adaptive deep learning method using cycle-consistent GANs to improve transverse position reconstruction in TPCs, significantly enhancing accuracy and resolution in experimental and simulated data.

Contribution

It presents a novel domain adaptation approach for TPC position reconstruction, addressing data-simulation mismatch with cycle-consistent GANs.

Findings

60.6% increase in reconstructed radial boundary in prototype detector

At least 27% resolution improvement in simulated 50-kg TPC

Effective mitigation of bias and resolution limitations

Abstract

Transverse position reconstruction in a Time Projection Chamber (TPC) is crucial for accurate particle tracking and classification, and is typically accomplished using machine learning techniques. However, these methods often exhibit biases and limited resolution due to incompatibility between real experimental data and simulated training samples. To mitigate this issue, we present a domain-adaptive reconstruction approach based on a cycle-consistent generative adversarial network. In the prototype detector, the application of this method led to a 60.6% increase in the reconstructed radial boundary. Scaling this method to a simulated 50-kg TPC, by evaluating the resolution of simulated events, an additional improvement of at least 27% is achieved.