Online Track Vertex Reconstruction Method Based on an Artificial Neural Network for MPGD

Hao Liu,Yi Zhang

doi:10.3390/app10196760

Abstract

In this paper, an online vertex reconstruction algorithm based on an artificial neural network (ANN) was proposed for the micro-pattern gaseous detector (MPGD). A simulation based on Geant4 was performed to generate the training and testing samples for the two cascade neural networks. Compared with a center-of-mass reconstruction, the proposed method shows better precision and much higher efficiency. Furthermore, a scheme for implementing the proposed algorithm on a Field-Programmable Gate Array (FPGA) chip is also presented to demonstrate that the algorithm could be integrated in a modern data acquisition (DAQ) system for online imaging techniques.

Highlights

The micro-pattern gaseous detector (MPGD) was originally developed for high-energy physics experiments [1], and has several advantages such as high spatial resolution, fast response time, and a large sensitive area
The large number of readout electrodes lends itself well to parallel computation, whereas the traditional track vertex reconstruction algorithms run by personal computers (PCs) are mostly serial
The results show that the centroid method has a larger reconstruction error when the particles are not perpendicularly incident

Summary

Introduction

The micro-pattern gaseous detector (MPGD) was originally developed for high-energy physics experiments [1], and has several advantages such as high spatial resolution, fast response time, and a large sensitive area. Micromegas and gas electrons multiplier (GEM) detectors appeared and were equipped with larger and more complex readout devices [2,3,4,5] This results in a large amount of data from one single event, which means that data acquisition and analysis of the detector is challenging. The signal generated by the incident particle does not always have a linear correlation with the position of the incident particle [6], especially when the incidental angle is large. This relationship is affected by the detector structure and readout device, which makes it more difficult to use an analytical function to describe these relationships. An artificial neural network (ANN) can be used as a mature algorithm to solve difficult problems in the field of nuclear detection and other challenging projects [7]

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Methods

Conclusion