# A tagger for strange jets based on tracking information using long short-term memory

## TL;DR

This paper introduces a novel LSTM-based algorithm for identifying strange jets using tracking data, outperforming simple benchmarks and addressing the challenge of discriminating strange jets from up and down quark jets.

## Contribution

It presents a new LSTM neural network approach for strange jet tagging that improves over existing simple benchmark methods.

## Key findings

- Achieves 21% background efficiency at 30% signal efficiency.
- Achieves 63% background efficiency at 70% signal efficiency.
- Outperforms benchmark algorithms based on transverse-momentum fraction.

## Abstract

An algorithm for the identification of jets that originate from the hadronisation of strange quarks is presented, which complements existing algorithms for the identification of jets that originate from $b$-quarks and $c$-quarks. The algorithm is based on the properties of tracks and uses long short-term memory recurrent neural networks to discriminate between jets from strange quarks and jets from down and up quarks. The performance of the algorithm is compared to a simple benchmark algorithm that uses the transverse-momentum fraction carried by a reconstructed $K_S \rightarrow \pi^+\pi^-$ decay. While the benchmark algorithm is limited to signal efficiencies smaller than 13%, the proposed algorithm is not limited in efficiency. For signal efficiencies of 30% and 70%, background efficiencies of 21% and 63% are achieved, indicating the challenge of discriminating strange jets from jets that originate from first-generation quarks.