The SysCalc code: A tool to derive theoretical systematic uncertainties

TL;DR

SysCalc is a computational tool that efficiently derives theoretical systematic uncertainties in experimental analyses by utilizing existing generated events, reducing the need for additional simulations and resource expenditure.

Contribution

The paper introduces SysCalc, a novel code that estimates systematic uncertainties from theoretical sources using existing data, enhancing analysis robustness and efficiency.

Findings

Validates SysCalc with multiple tests and validation plots

Demonstrates minimal additional computational cost

Provides a practical guide for implementation

Abstract

Undisputedly, derivation of theoretical systematic uncertainties is an inseparable ingredient of any robust analysis dealing with experimental data. However, it is not uncommon, even for those analyses that use state of the art methods and tools to suffer from insufficient statistics when it comes to the simulated datasets used to estimate systematic uncertainties. This practically limits the power, and sometimes the robustness of the analysis. In this paper, we present SysCalc, a code which is able to derive weights for various important theoretical systematic uncertainties, including those related to the choice of the Parton Distribution Function sets and the various scale choices. SysCalc utilizes the central sample generated events to estimate the related systematic uncertainties, thus, omitting the need for generating dedicated systematics datasets, and with only a minimal added cost in terms of computing resources. In this paper we discuss the working principles of the code accompanied by various validation plots. We also discuss the structure of the code followed by a practical guide for how to use the tool.