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

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 $K_S\to {\pi }^{+}{\pi }^{-}$ 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.

24 pages, 11 figures

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• Role of polarizations and spin-spin correlations of $W^{\prime }s$ in $e^{-}{e}^{+}\to W^{-}{W}^{+}$ at $\sqrt{s}=250$ GeV to probe anomalous $W^{-}{W}^{+}Z/\gamma$ couplings
• From strange-quark tagging to fragmentation tagging with machine learning
• Improving the Direct Determination of $|V_{ts}|$ using Deep Learning