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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.06972 |
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| _version_ | 1866914284738445312 |
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| author | Gavranovič, Jan Čalić, Lara Debevc, Jernej Lytken, Else Kerševan, Borut Paul |
| author_facet | Gavranovič, Jan Čalić, Lara Debevc, Jernej Lytken, Else Kerševan, Borut Paul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06972 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Fake Factor Estimation Using Data-Based Inference Gavranovič, Jan Čalić, Lara Debevc, Jernej Lytken, Else Kerševan, Borut Paul High Energy Physics - Phenomenology 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. |
| title | Neural Fake Factor Estimation Using Data-Based Inference |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2511.06972 |