Learning to Reconstruct Quirky Tracks

TL;DR

This paper demonstrates how machine learning can reconstruct unconventional particle tracks, like quirks, in physics experiments, enabling the detection of non-standard signatures previously difficult to analyze.

Contribution

It introduces a machine learning-based method for reconstructing non-helical, oscillating particle trajectories, expanding the scope of detectable signals in particle physics.

Findings

ML-based tracking successfully reconstructs quirks' oscillating tracks

The method is adaptable to various non-standard particle trajectories

Potential to discover new particles with unconventional signatures

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 ML-based tracking allow for new inroads into previously inaccessible territory, such as reconstruction of tracks which do not follow helical trajectories. This paper presents a demonstration of the capacity of ML-based tracking to reconstruct the oscillating trajectories of quirks. The technique used is not specific to quirks, and opens the door to a program of searching for many kinds of non-standard tracks.