Evaluating the Impact of Detector Design on Jet Flavor Tagging for Future Colliders

TL;DR

This paper analyzes how different detector designs and configurations influence the effectiveness of jet flavor tagging algorithms at future electron-positron colliders, emphasizing the role of detector capabilities and energy scales.

Contribution

It introduces a comprehensive study of detector design impacts on jet tagging performance using a graph neural network approach for future collider experiments.

Findings

Detector design variations significantly affect tagging accuracy

Tracking and calorimeter system improvements enhance performance

Center-of-mass energy influences jet tagging effectiveness

Abstract

Jet flavor tagging is of utmost importance for unlocking the full physics potential of any future collider experiment. The performance of any jet flavor identification algorithm depends both on its underlying architecture and on the detector's design and capabilities. In this work, we present an analysis of the dependence of jet tagging algorithm performance on three detector designs being considered for future $e^{+}e^{-}$ colliders. To fully exploit the potential of these detector concepts, we utilize a graph neural network-based jet tagging algorithm. In addition, we evaluate the impact on the jet tagging performance of variations in the tracking and calorimeter systems for one of these detector concepts, the SiD detector, as well as the dependence on the center-of-mass energy.