%0 Generic %1 Kalogeropoulos2018Syscalc %A Kalogeropoulos, Alexis %A Alwall, Johan %D 2018 %K statistics %T The \syscalc\ code: A tool to derive theoretical systematic uncertainties %U http://arxiv.org/abs/1801.08401 %X 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.

@misc{Kalogeropoulos2018Syscalc, 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.}}, added-at = {2019-02-23T22:09:48.000+0100}, archiveprefix = {arXiv}, author = {Kalogeropoulos, Alexis and Alwall, Johan}, biburl = {https://www.bibsonomy.org/bibtex/246ba1097dddc57932cedff318b648a2e/cmcneile}, citeulike-article-id = {14523431}, citeulike-linkout-0 = {http://arxiv.org/abs/1801.08401}, citeulike-linkout-1 = {http://arxiv.org/pdf/1801.08401}, day = 25, eprint = {1801.08401}, interhash = {9176999a8964545a4c40d10f1260768d}, intrahash = {46ba1097dddc57932cedff318b648a2e}, keywords = {statistics}, month = jan, posted-at = {2018-01-26 09:09:35}, priority = {2}, timestamp = {2019-02-23T22:15:27.000+0100}, title = {{The \syscalc\ code: A tool to derive theoretical systematic uncertainties}}, url = {http://arxiv.org/abs/1801.08401}, year = 2018 }