Neural Fake Factor Estimation Using Data-Based Inference
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 $p_\text{T}$ 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.