Work

SysVar: Consistent and scalable treatment of systematic uncertainties in template-based analyses

Year: 2026

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.06117

Authors: Tsaklidis, Ilias, Aggarwal, Agrim, Alexandris, Georgios, Fillinger, Tristan, De Pietro, Giacomo, Ivanov, Daniel, Akdag, Melisa-Melek, Prim, Markus, Bernlochner, Florian

Institutions: University of Bonn, High Energy Accelerator Research Organization, Karlsruhe Institute of Technology, Nagoya University

Language: English

Topic: Computational Physics and Python Applications

Subfield: Software

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7220738170

Cites: 4

Cited by:

Related to: 10