PreprintCC BY 4.0arXiv
Defining a Minimum Resolution for Unbinned Analyses
Abstract
Collider analyses combine rigorous statistical techniques with state-of-the-art Machine Learning models. However, when the latter are used directly to estimate the likelihood function of the background, hard to quantify systematic effects may bias the estimation of the relevant signal parameters. To address this problem, we present the Minimum Resolution Likelihood (MRL) method, which defines a Fiducial Signal Region that effectively turns the systematic effects into statistical uncertainties. We show with examples that the resulting signal strength estimation has a reduced bias. We consider both toy examples and a realistic application based on the HI-SIGMA technique applied to di-Higgs searches.
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BibTeX
@misc{szewc2026defining,
title = {{Defining a Minimum Resolution for Unbinned Analyses}},
author = {Manuel Szewc},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2606.28498},
eprint = {2606.28498},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.28498},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Defining a Minimum Resolution for Unbinned Analyses
AU - Manuel Szewc
PY - 2026
DA - 2026/10/09
PB - arXiv
DO - 10.48550/arxiv.2606.28498
UR - https://arxiv.org/abs/2606.28498
AB - Collider analyses combine rigorous statistical techniques with state-of-the-art Machine Learning models. However, when the latter are used directly to estimate the likelihood function of the background, hard to quantify systematic effects may bias the estimation of the relevant signal parameters. To address this problem, we present the Minimum Resolution Likelihood (MRL) method, which defines a Fiducial Signal Region that effectively turns the systematic effects into statistical uncertainties. We show with examples that the resulting signal strength estimation has a reduced bias. We consider both toy examples and a realistic application based on the HI-SIGMA technique applied to di-Higgs searches.
ER -