@article{d9dbe108-0ecb-4bab-b17d-1cde11a25606,
  abstract     = {{<p>In a high-energy physics data analysis, the term “fake” backgrounds refers to events that would formally not satisfy the (signal) process selection criteria, but are accepted nonetheless due to mis-reconstructed particles. This can occur, e.g., when leptons from secondary decays are incorrectly identified as originating from the hard-scatter interaction point (known as non-prompt leptons), or when other physics objects, such as hadronic jets, are mistakenly reconstructed as leptons (resulting in mis-identified leptons). These fake leptons are usually estimated using data-driven techniques, one of the most common being the Fake Factor method. This method relies on predicting the fake lepton contribution by reweighting data events, using a scale factor (i.e. fake factor) function. Traditionally, fake factors have been estimated by histogramming and computing the ratio of two data distributions, typically as functions of a few relevant physics variables such as the transverse momentum p<sub>T</sub> and pseudorapidity η. In this work, we introduce a novel approach of fake factor calculation, based on density ratio estimation using neural networks trained directly on data in a higher-dimensional feature space. We show that our method enables the computation of a continuous, unbinned fake factor on a per-event basis, offering a more flexible, precise, and higher-dimensional alternative to the conventional method, making it applicable to a wide range of analyses. A simple LHC open data analysis we implemented confirms the feasibility of the method and demonstrates that the ML-based fake factor provides smoother, more stable estimates across the phase space than traditional methods, reducing binning artifacts and improving extrapolation to signal regions.</p>}},
  author       = {{Gavranovič, Jan and Čalić, Lara and Debevc, Jernej and Lytken, Else and Kerševan, Borut Paul}},
  issn         = {{1029-8479}},
  keywords     = {{Electroweak Precision Physics; Jets and Jet Substructure; Left-Right Models}},
  language     = {{eng}},
  number       = {{4}},
  publisher    = {{Springer}},
  series       = {{Journal of High Energy Physics}},
  title        = {{Neural fake factor estimation using data-based inference}},
  url          = {{http://dx.doi.org/10.1007/JHEP04(2026)188}},
  doi          = {{10.1007/JHEP04(2026)188}},
  volume       = {{2026}},
  year         = {{2026}},
}