Online Electron Reconstruction at CLAS12

TL;DR

This paper presents a machine learning method for real-time electron reconstruction and identification at CLAS12, achieving high purity and efficiency, enabling improved online analysis and triggering in high-rate nuclear physics experiments.

Contribution

It introduces a novel machine learning approach for online electron identification in CLAS12, capable of operating at high data rates for real-time reconstruction.

Findings

Electron identification purity exceeds 75%.

Efficiency remains close to 100%.

Method supports real-time processing at high data rates.

Abstract

Online reconstruction is key for monitoring purposes and real time analysis in High Energy and Nuclear Physics experiments. A necessary component of reconstruction algorithms is particle identification that combines information left by a particle passing through several detector components to identify the particle's type. Of particular interest to electroproduction Nuclear Physics experiments such as CLAS12 is electron identification which is used to trigger data recording. A machine learning approach was developed for CLAS12 to reconstruct and identify electrons by combining raw signals at the data acquisition level from several detector components. This approach achieves an electron identification purity above 75% whilst retaining an efficiency close to 100%. The machine learning tools are capable of running at high rates exceeding the data acquisition rates and will allow electron reconstruction in real time. This work enhances online analyses and monitoring and can contribute to improved triggering at CLAS12. This machine learning driven approach will also be crucial for experiments aiming to transition to streaming readout operations where online reconstruction will be a key component of the data taking paradigm.