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Learning to reconstruct quirky tracks

Qiyu Sha1,2, Daniel Murnane3,4, Max Fieg5, Shelley Tong5, Mark Zakharyan6, Yaquan Fang1,2, and Daniel Whiteson5

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Phys. Rev. D 114, 015005 – Published 6 July, 2026

DOI: https://doi.org/10.1103/dvzc-lgj1

Abstract

Analysis of data from particle physics experiments traditionally sacrifices some sensitivity to new particles for the sake of practical computability, effectively ignoring some potentially striking signatures. However, recent advances in machine learning (ML)–based tracking allow for new inroads into previously inaccessible territory, such as reconstruction of tracks that do not follow helical trajectories. This paper presents a demonstration of the capacity of ML-based tracking to reconstruct the oscillating trajectories of quirks, particles charged under an unbroken QCD-like gauge symmetry. The technique used is not specific to quirks, and opens the door to a program of searching for many kinds of nonstandard tracks.

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Phys. Rev. D 114, 015005– Published 6 July, 2026
  • Received 19 October 2025
  • Accepted 9 June 2026
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