| Naslov: | Neural fake factor estimation using data-based inference |
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| Avtorji: | ID Gavranovič, Jan, Institut "Jožef Stefan" (Avtor)
ID Čalić, Lara (Avtor)
ID Debevc, Jernej, Institut "Jožef Stefan" (Avtor)
ID Lytken, Else (Avtor)
ID Kerševan, Borut Paul, Institut "Jožef Stefan" (Avtor) |
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| Datoteke: |  URL - Izvorni URL, za dostop obiščite https://link.springer.com/article/10.1007/JHEP04(2026)188
 PDF - Predstavitvena datoteka, prenos (2,87 MB)
MD5: 788F56699E95853A4E0BE91B91D1DD25
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| Jezik: | Angleški jezik |
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| Tipologija: | 1.01 - Izvirni znanstveni članek |
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| Organizacija: |  IJS - Institut Jožef Stefan
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| Povzetek: | 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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| Ključne besede: | high energy physics, fake factor, electroweak precision physics |
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| Status publikacije: | Objavljeno |
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| Verzija publikacije: | Objavljena publikacija |
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| Poslano v recenzijo: | 11.11.2025 |
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| Datum sprejetja članka: | 28.02.2026 |
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| Datum objave: | 23.04.2026 |
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| Založnik: | SISSA |
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| Leto izida: | 2026 |
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| Št. strani: | str. 1-27 |
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| Številčenje: | Vol. 2026, article no. 188 |
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| Izvor: | Italija |
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| PID: | 20.500.12556/DiRROS-29233  |
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| UDK: | 539.1 |
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| ISSN pri članku: | 1029-8479 |
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| DOI: | 10.1007/JHEP04(2026)188 |
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| COBISS.SI-ID: | 276535043 |
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| Avtorske pravice: | © The Authors |
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| Opomba: | Nasl. z nasl. zaslona;
Soavtorja iz Slovenije: Jernej Debevc, Borut Paul Kerševan;
Opis vira z dne 24. 4. 2026;
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| Datum objave v DiRROS: | 29.04.2026 |
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| Število ogledov: | 45 |
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| Število prenosov: | 25 |
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| Metapodatki: |    |
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