Neural fake factor estimation using data-based inference

Gavranovič, Jan (author), Čalić, Lara (author), Debevc, Jernej (author), Lytken, Else (author), Kerševan, Borut Paul (author)

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.

• Language: English
• Keywords: high energy physics, fake factor, electroweak precision physics, jets and jet substructure, left-right models
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FMF - Faculty of Mathematics and Physics
• Publication status: Published
• Publication version: Version of Record
• Year: 2026
• Number of pages: 28 str.
• Numbering: Vol. 2026, iss. 4, art. 188
• PID: 20.500.12556/RUL-182296
• UDC: 539.1
• ISSN on article: 1029-8479
• DOI: 10.1007/JHEP04(2026)188
• COBISS.SI-ID: 276535043
• Publication date in RUL: 21.05.2026

Record is a part of a journal

• Title: The journal of high energy physics
• Shortened title: J. high energy phys.
• Publisher: SISSA
• ISSN: 1029-8479
• COBISS.SI-ID: 1314148

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: fizika visokih energij, hadronski trkalnik, trkalniki