Online Event Selection for Mu3e using GPUs

TL;DR

This paper presents a GPU-based online event selection algorithm for the Mu3e experiment, significantly reducing data rates while accurately reconstructing signal events at high muon rates.

Contribution

It introduces a three-step GPU algorithm that efficiently filters and reconstructs events, enabling real-time data reduction for high-rate muon experiments.

Findings

Reduces data rate by a factor of 200

Reconstructs over 97% of signal tracks

Identifies over 94% of signal events accurately

Abstract

In the search for physics beyond the Standard Model the Mu3e experiment tries to observe the lepton flavor violating decay $\mu^+ \rightarrow e^+ e^- e^+$. By observing the decay products of $1 \cdot 10^8\mu$/s it aims to either observe the process, or set a new upper limit on its estimated branching ratio. The high muon rates result in high data rates of $80$\,Gbps, dominated by data produced through background processes. We present the Online Event Selection, a three step algorithm running on the graphics processing units (GPU) of the $12$ Mu3e filter farm computers. By using simple and fast geometric selection criteria, the algorithm first reduces the amount of possible event candidates to below $5\%$ of the initial set. These candidates are then used to reconstruct full particle tracks, correctly reconstructing over $97\%$ of signal tracks. Finally a possible decay vertex is reconstructed using simple geometric considerations instead of a full reconstruction, correctly identifying over $94\%$ of signal events. We also present a full implementation of the algorithm, fulfilling all performance requirements at the targeted muon rate and successfully reducing the data rate by a factor of $200$.