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  • Open Access

Reconstructing the invisible fraction of semivisible jets in ISR-boosted events via neural network regression

Yin Li, Jian Bin Wang, Jia Qi Xie, Kai Rong Xu, Rui Han Ye, and Zi Huan Huang

Bingxuan Liu*

  • *Contact author: liubx28@mail.sysu.edu.cn
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Phys. Rev. D 114, 056017 – Published 17 September, 2026

DOI: https://doi.org/10.1103/hbxx-5f3k

Abstract

Semivisible jets (SVJs) provide a characteristic collider signature of strongly interacting dark sectors, in which the key model parameter rinv controls the fraction of dark hadrons decaying to dark matter candidates. In this work, a regression model is developed to reconstruct rinv in SVJ events produced in association with an energetic photon. The model uses information from high-level physics objects only, and the training procedure is optimized to ensure applicability. The performance is found to be robust against varying signal parameters and rinv can be reconstructed at a much higher precision, compared to previously developed analytical method. It offers a new approach to conduct SVJ searches that can potentially unify both s-channel and t-channel productions, enhancing the sensitivities.

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Phys. Rev. D 114, 056017– Published 17 September, 2026
  • Received 23 April 2026
  • Accepted 27 July 2026
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