发表机构
University of Bonn; High Energy Accelerator Research Organization (KEK); Karlsruhe Institute of Technology (KIT); Nagoya University（波恩大学; 高能加速器研究机构; 卡尔斯鲁厄理工学院; 名古屋大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SysVar通过轻量API简化模板分析中系统不确定性的传播，保持相关性，压缩为特征变体，开源且实验无关。
AI 中文摘要
随着分析规模的扩大，将校正权重中的系统不确定性传播到由模拟数据生成的模板中，同时保持信号提取变量中的相关性，变得越来越具有挑战性。SysVar的轻量级API隐藏了应用校正权重、构建系统变体和绘制标称及变体模板直方图的大部分簿记复杂性。这简化了工作流程，否则这些工作流程容易出错，但对于高精度测量至关重要。通过在整个分析中传播全协方差并将其压缩为一小组正交特征变体（每个变体定义一个普通的形状干扰参数），保持了相关性。在这项工作中，我们通过结合两个独立的伪测量来展示SysVar的工作流程和影响，这些测量受益于模板形状中编码的一致相关的系统不确定性。SysVar是开源的，可通过pip安装，且与实验无关。
英文摘要
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.
Comments8 pages, 5 figures, 1 table, Submitted to the proceedings of CHEP 2026 at EPJ Web of Conferences
