Neural fake factor estimation using data-based inference

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

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.

• Jezik: Angleški jezik
• Ključne besede: high energy physics, fake factor, electroweak precision physics, jets and jet substructure, left-right models
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FMF - Fakulteta za matematiko in fiziko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2026
• Št. strani: 28 str.
• Številčenje: Vol. 2026, iss. 4, art. 188
• PID: 20.500.12556/RUL-182296
• UDK: 539.1
• ISSN pri članku: 1029-8479
• DOI: 10.1007/JHEP04(2026)188
• COBISS.SI-ID: 276535043
• Datum objave v RUL: 21.05.2026

Gradivo je del revije

• Naslov: The journal of high energy physics
• Skrajšan naslov: J. high energy phys.
• Založnik: SISSA
• ISSN: 1029-8479
• COBISS.SI-ID: 1314148

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: fizika visokih energij, hadronski trkalnik, trkalniki