Development of systematic uncertainty-aware neural network trainings for binned-likelihood analyses at the LHC

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10512895" target="_blank" >RIV/00216208:11320/25:10512895 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=8vPjGAXrpt" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=8vPjGAXrpt</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1140/epjc/s10052-025-14713-w" target="_blank" >10.1140/epjc/s10052-025-14713-w</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Development of systematic uncertainty-aware neural network trainings for binned-likelihood analyses at the LHC

Popis výsledku v původním jazyce

We propose a neural network training method capable of accounting for the effects of systematic variations of the data model in the training process and describe its extension towards neural network multiclass classification. The procedure is evaluated on the realistic case of the measurement of Higgs boson production via gluon fusion and vector boson fusion in the tau tau decay channel at the CMS experiment. The neural network output functions are used to infer the signal strengths for inclusive production of Higgs bosons as well as for their production via gluon fusion and vector boson fusion. We observe improvements of 12 and 16% in the uncertainty in the signal strengths for gluon and vector-boson fusion, respectively, compared with a conventional neural network training based on cross-entropy.

Název v anglickém jazyce

Development of systematic uncertainty-aware neural network trainings for binned-likelihood analyses at the LHC

Popis výsledku anglicky

We propose a neural network training method capable of accounting for the effects of systematic variations of the data model in the training process and describe its extension towards neural network multiclass classification. The procedure is evaluated on the realistic case of the measurement of Higgs boson production via gluon fusion and vector boson fusion in the tau tau decay channel at the CMS experiment. The neural network output functions are used to infer the signal strengths for inclusive production of Higgs bosons as well as for their production via gluon fusion and vector boson fusion. We observe improvements of 12 and 16% in the uncertainty in the signal strengths for gluon and vector-boson fusion, respectively, compared with a conventional neural network training based on cross-entropy.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10300 - Physical sciences

Projekt

<a href="/cs/project/LM2023040" target="_blank" >LM2023040: Výzkumná infrastruktura pro experimenty v CERN</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

European Physical Journal C

ISSN

1434-6044

e-ISSN

1434-6052

Svazek periodika

85

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

37

Strana od-do

1360

Kód UT WoS článku

001639699600001

EID výsledku v databázi Scopus

2-s2.0-105024350483