Modern jet flavour tagging in hadronic Z decays with archived ALEPH data

TL;DR

This paper reanalyzes archived ALEPH data from LEP using modern deep learning-based jet flavour tagging techniques, significantly improving quark jet identification and introducing first-time strange quark tagging, with implications for electroweak measurements and future collider algorithms.

Contribution

It introduces advanced deep learning methods for jet flavour tagging in archived LEP data, including the first strange quark jet tagging implementation, enhancing analysis precision.

Findings

Up to tenfold reduction in misidentification rates for b- and c-quark jets.

First successful strange quark jet tagging with LEP data.

Good data-simulation agreement achieved through calibration.

Abstract

We present a reanalysis of archived data from the ALEPH experiment at LEP in the $\mathrm{Z \to q\bar{q}}$ final state. We apply modern jet flavour tagging techniques to improve the separation between the different hadronic decay channels of the Z boson, achieving up to one order of magnitude improvement in misidentification rate for b- and c-quark jets compared to the legacy algorithms used for the most recent ALEPH results, for the same identification efficiency. We also present the first implementation of strange quark jet tagging with LEP data, which allows for the selection of a $\mathrm{Z \to s\bar{s}}$ enriched event sample. These improvements in the flavour tagging performance are achieved by leveraging the lifetime, particle identification, and secondary vertex information, as well as modern classifiers based on a deep learning approach. We also demonstrate the calibration of the tagger in data using a tag-and-probe method, obtaining good data to simulation agreement for all quark flavours. These results pave the way for improved measurements of electroweak precision observables with LEP archived data, and can serve as a guidance for the development of detectors and algorithms for future electron-positron colliders.