The Optimal Use of Silicon Pixel Charge Information for Particle Identification

TL;DR

This paper demonstrates that using the full charge information from silicon pixel detectors with an optimal linear combination greatly improves particle identification over traditional truncated mean methods.

Contribution

It introduces an optimal scheme leveraging charge in multiple layers, outperforming the conventional truncated mean approach for particle identification.

Findings

Optimal charge-based classifier outperforms truncated mean.

Truncation does not significantly reduce performance.

Linear combination of truncated mean and variance suffices.

Abstract

Particle identification using the energy loss in silicon detectors is a powerful technique for probing the Standard Model (SM) as well as searching for new particles beyond the SM. Traditionally, such techniques use the truncated mean of the energy loss on multiple layers, in order to mitigate heavy tails in the charge fluctuation distribution. We show that the optimal scheme using the charge in multiple layers significantly outperforms the truncated mean. Truncation itself does not significantly degrade performance and the optimal classifier is well-approximated by a linear combination of the truncated mean and truncated variance.