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  • Open Access

Data-driven predictions for dark photon and millicharged particle production

Elizabeth Allison1,2 and Nikita Blinov2,*

  • 1Neuroscience Graduate Program, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
  • 2Department of Physics and Astronomy, York University, Toronto, Ontario, Canada

  • *Contact author: nblinov@yorku.ca
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Phys. Rev. D 113, 095030 – Published 22 May, 2026

DOI: https://doi.org/10.1103/vzmz-jbck

Abstract

Accurate signal predictions are essential for interpreting and optimizing fixed-target searches for new physics. Even in minimal models such as the dark photon (A′) or millicharged particles (mCPs), theoretical uncertainties in hadronic production can be substantial. We introduce a data-driven framework that predicts both the rate and kinematic distributions of A′ and mCP production directly from measured dilepton events, without relying on specific theoretical production models. This method uses the close correspondence between amplitudes for emission of A′ or mCPs, and for off-shell Standard Model photon production, the latter being experimentally measurable in full differential form. We demonstrate that normalizing flow models can learn these distributions from data and serve as a fast, realistic Monte Carlo generator for dark sector signal simulations.

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Phys. Rev. D 113, 095030– Published 22 May, 2026
  • Received 12 December 2025
  • Accepted 1 May 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.

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