| Title: | Neural fake factor estimation using data-based inference |
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| Authors: | ID Gavranovič, Jan, Institut "Jožef Stefan" (Author)
ID Čalić, Lara (Author)
ID Debevc, Jernej, Institut "Jožef Stefan" (Author)
ID Lytken, Else (Author)
ID Kerševan, Borut Paul, Institut "Jožef Stefan" (Author) |
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| Files: |  URL - Source URL, visit https://link.springer.com/article/10.1007/JHEP04(2026)188
 PDF - Presentation file, download (2,87 MB)
MD5: 788F56699E95853A4E0BE91B91D1DD25
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  IJS - Jožef Stefan Institute
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| Abstract: | 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 pT 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. |
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| Keywords: | high energy physics, fake factor, electroweak precision physics |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 11.11.2025 |
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| Article acceptance date: | 28.02.2026 |
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| Publication date: | 23.04.2026 |
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| Publisher: | SISSA |
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| Year of publishing: | 2026 |
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| Number of pages: | str. 1-27 |
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| Numbering: | Vol. 2026, article no. 188 |
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| Source: | Italija |
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| PID: | 20.500.12556/DiRROS-29233  |
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| UDC: | 539.1 |
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| ISSN on article: | 1029-8479 |
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| DOI: | 10.1007/JHEP04(2026)188 |
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| COBISS.SI-ID: | 276535043 |
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| Copyright: | © The Authors |
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| Note: | Nasl. z nasl. zaslona;
Soavtorja iz Slovenije: Jernej Debevc, Borut Paul Kerševan;
Opis vira z dne 24. 4. 2026;
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| Publication date in DiRROS: | 29.04.2026 |
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| Views: | 39 |
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| Downloads: | 21 |
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