# Deep learning based track reconstruction on CEPC luminometer

## TL;DR

This paper introduces a deep neural network-based track reconstruction method for the CEPC luminometer, significantly improving efficiency and resolution over traditional techniques, especially in problematic tile gap regions.

## Contribution

The paper presents a novel deep learning approach that replaces conventional methods for improved track reconstruction in the CEPC luminometer.

## Key findings

- Reconstruction efficiency improved significantly.
- Energy and direction resolutions enhanced.
- Effective in tile gap regions.

## Abstract

We study the track reconstruction algorithms of the CEPC luminometer. Depend on the current geometry design, the conventional track reconstruction method is applied, but it suffers the energy leakage problem when tracks falling into the tile gaps regions. To solve this problem, a novel reconstruction method based on deep neural networks has been investigated, and the reconstruction efficiency has been improved significantly, as well as the energy and direction resolutions. This new reconstruction method is proposed to replace the conventional one for the CEPC luminometer.