Deep Machine Learning for the PANDA Software Trigger

TL;DR

This paper demonstrates that deep machine learning methods can significantly improve the efficiency and background reduction of the PANDA experiment's software trigger system, using simulated data and neural network architectures.

Contribution

It introduces a novel deep learning-based approach for the PANDA software trigger, showing improved performance over traditional methods with a residual CNN architecture.

Findings

Up to 200% efficiency gain compared to conventional methods

Background reduction factor maintained at 1/1000

Additional input variables enhance data quality

Abstract

Deep machine learning methods have been studied for the software trigger of the future PANDA experiment at FAIR, using Monte Carlo simulated data from the GEANT-based detector simulation framework PandaRoot. Ten physics channels that cover the main physics topics, including electromagnetic, exotic, charmonium, open charm, and baryonic reaction channels, have been investigated at four different anti-proton beam momenta. Binary and multi-class classification together with seven different network architectures have been studied. Finally a residual convolutional neural network with four residual blocks in a binary classification scheme has been chosen due to its extendability, performance and stability. The presented study represents a feasibility study of a completely software-based trigger system. Compared to a conventional selection method, the deep machine learning approach achieved a significant efficiency gain of up to 200\%, while keeping the background reduction factor at the required level of 1/1000. Furthermore, it is shown that the use of additional input variables can improve the data quality for subsequent analysis. This study shows that the PANDA software trigger can benefit greatly from the deep machine learning methods.