SysVar: Consistent and scalable treatment of systematic uncertainties in template-based analyses
Abstract
Propagating systematic uncertainties from correction weights into templates generated from simulated data while preserving correlations in the signal extraction variables becomes increasingly challenging as analyses scale in size. SysVar's lightweight API hides most of the bookkeeping complexity of applying correction weights, building systematic variations, and histogramming nominal and varied templates. This streamlines workflows that are otherwise error-prone but essential for high-precision measurements. Correlations are preserved by propagating the full covariance across the analysis and compressing it into a small set of orthogonal eigenvariations, each defining an ordinary shape nuisance parameter. In this work, we demonstrate SysVar's workflow and impact by combining two independent pseudo-measurements that benefit from consistently correlated systematic uncertainties encoded in the template shapes. SysVar is open-source, pip-installable, and experiment-agnostic.
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BibTeX
@misc{tsaklidis2026sysvar,
title = {{SysVar: Consistent and scalable treatment of systematic uncertainties in template-based analyses}},
author = {Ilias Tsaklidis and Agrim Aggarwal and Georgios Alexandris and Tristan Fillinger and Giacomo De Pietro and Daniel Ivanov and Melisa-Melek Akdag and Markus Prim and Florian Bernlochner},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.06117},
eprint = {2610.06117},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.06117},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - SysVar: Consistent and scalable treatment of systematic uncertainties in template-based analyses
AU - Ilias Tsaklidis
AU - Agrim Aggarwal
AU - Georgios Alexandris
AU - Tristan Fillinger
AU - Giacomo De Pietro
AU - Daniel Ivanov
AU - Melisa-Melek Akdag
AU - Markus Prim
AU - Florian Bernlochner
PY - 2026
DA - 2026/10/05
PB - arXiv
DO - 10.48550/arxiv.2610.06117
UR - https://arxiv.org/abs/2610.06117
AB - Propagating systematic uncertainties from correction weights into templates generated from simulated data while preserving correlations in the signal extraction variables becomes increasingly challenging as analyses scale in size. SysVar's lightweight API hides most of the bookkeeping complexity of applying correction weights, building systematic variations, and histogramming nominal and varied templates. This streamlines workflows that are otherwise error-prone but essential for high-precision measurements. Correlations are preserved by propagating the full covariance across the analysis and compressing it into a small set of orthogonal eigenvariations, each defining an ordinary shape nuisance parameter. In this work, we demonstrate SysVar's workflow and impact by combining two independent pseudo-measurements that benefit from consistently correlated systematic uncertainties encoded in the template shapes. SysVar is open-source, pip-installable, and experiment-agnostic.
ER -