Muon g-2 reconstruction and analysis framework for the muon anomalous precession frequency

TL;DR

This paper presents the development and current status of a robust, multi-threaded data reconstruction and analysis framework for the Muon g-2 experiment at Fermilab, designed to handle large data volumes and enable precise measurement of the muon anomalous magnetic moment.

Contribution

It introduces a novel, multi-threaded data processing framework based on Fermilab's art system, optimized for online monitoring and rapid data analysis during the Muon g-2 experiment.

Findings

Framework successfully processed commissioning data

Multi-threaded algorithms improved online data quality monitoring

Nearline analysis enabled rapid data turnaround

Abstract

The Muon g-2 experiment at Fermilab, with the aim to measure the muon anomalous magnetic moment to an unprecedented level of 140~ppb, has started beam and detector commissioning in Summer 2017. To deal with incoming data projected to be around tens of petabytes, a robust data reconstruction and analysis chain based on Fermilab's \textit{art} event-processing framework is developed. Herein, I report the current status of the framework, together with its novel features such as multi-threaded algorithms for online data quality monitor (DQM) and fast-turnaround operation (nearline). Performance of the framework during the commissioning run is also discussed.