# 1. Introduction¶

## 1.1. Background¶

Collider experiments have long been the central tools to look for new physics beyond the Standard Model (SM). The number of new physics events in colliders are given by the product of production cross section and an integrated luminosity, where the former is theoretically calculated based on a hypothesized theory and the latter is measured by the experiment collaboration with, e.g., at the ATLAS or CMS experiments at the LHC, 2%-level precision [ATLA16][CMS18]. Therefore, cross sections of new physics processes are the most important values in any new-physics theory and should be calculated with similar precision.

Cross-section calculation with such precision is not a simple task because the leading order (LO) calculation, which usually includes only the tree-level contributions, will not give such precision and we have to include loop-level calculations. Especially, if colored particles are involved in the process, the large QCD couplings worsen convergence of the perturbation series and even the next-to-leading-order (NLO) calculation may give uncertainties more than 10%, and we have to include the next-to-NLO (NNLO) diagrams and/or soft-gluon resummation.

For SUSY processes, several tools are published for precise cross-section calculations. Prospino [BHS96] is one of the pioneer works. It is upgraded to Prospino 2 [Pro], with which we can calculate NLO cross sections of most SUSY processes within a few minutes. For soft-gluon resummation, Resummino [FKLR13] is available, which allows us to calculate the resummation at the accuracy level of next-to-leading-log (NLL) or the next-to-NLL (NNLL).

NNLL-fast [BBK+16] (and its preceding work NLL-fast) is another type of tools for SUSY cross sections; it provides, rather than calculation programs, grid-format data tables of predicted cross sections together with interpolators. The data tables are based on their very precise calculations with the accuracy level of approximated-NNLO plus NNLL in NNLL-fast (and NLO plus NLL in NLL-fast). By interpolation, users can obtain accurate predictions of cross sections in simplified scenarios within one second.

Grid tables for SUSY cross sections are provided by other collaborations as well [1].
The most nominal set is the one provided by LHC SUSY Cross Section Working Group [LHC], which is obtained by compiling the results from the above calculators.
They provide cross-section grid tables for various simplified models and collision energies.
However, the grid tables provided by various collaborations are (of course) in various formats.
This package `susy_cross_section`

aims to handle those grid data, including ones appearing in future, in an unified manner.

## 1.2. This Package¶

`susy_cross_section`

is a Python package to handle cross-section grid tables regardless of their format.
With this package, one can import any table-like grid files as a pandas DataFrame, once an annotation file (`info`

file) is provided in JSON format [ECMAInternational17].
Selected grid tables from above-introduced collaborations are pre-installed with annotation files.
Using several types of interpolators provided in this package, users can interpolate those grid tables to obtain the central values together with (possibly asymmetric) uncertainties.
A quick start guide is provided in Section 2.

For simple use-cases, a command-line script `susy-xs`

is provided.
You can get interpolated cross sections for simplified scenarios with the sub-command `susy-xs get`

on your terminal, based on simple log-log interpolators.
More information on the script is available in Section 3.

You can include this package in your Python code for more customization. For example, you may use the cross section values in your code, or interpolate the grid tables with other interpolators, including your own ones. Section 4 of this document is devoted to such use-cases, and the full API reference of this package is provided in Section 5. In Section 6 we propose two methods to validate the interpolation procedure.

Footnotes

[1] | DeepXS [ORC+18] is another tool for precise SUSY cross section, which utilizes deep learning technique for cross-section estimation. |

[LHC] | LHC SUSY Cross Section Working Group. https://twiki.cern.ch/twiki/bin/view/LHCPhysics/SUSYCrossSections, Retrieved Feb. 2019. |

[Pro] | Prospino2. https://www.thphys.uni-heidelberg.de/~plehn/index.php?show=prospino, Retrieved Feb. 2019. |

[ATLA16] | ATLAS Collaboration. Luminosity determination in pp collisions at \(\sqrt{s}\) = 8 TeV using the ATLAS detector at the LHC. Eur. Phys. J. C76 (2016) 653 [1608.03953]. |

[BHS96] | W. Beenakker, R. Hopker, and M. Spira. PROSPINO: A Program for the production of supersymmetric particles in next-to-leading order QCD. arXiv:hep-ph/9611232. |

[BBK+16] | W. Beenakker, C. Borschensky, M. Krämer, A. Kulesza, and E. Laenen. NNLL-fast: predictions for coloured supersymmetric particle production at the LHC with threshold and Coulomb resummation. JHEP 12 (2016) 133 [1607.07741]. |

[CMS18] | CMS Collaboration. CMS luminosity measurement for the 2017 data-taking period at \(\sqrt{s} = 13~\mathrm{TeV}\). CMS-PAS-LUM-17-004, CERN, 2018. |

[FKLR13] | B. Fuks, M. Klasen, D. R. Lamprea, and M. Rothering. Precision predictions for electroweak superpartner production at hadron colliders with Resummino. Eur. Phys. J. C 73 (2013) 2480 [1304.0790]. |

[ORC+18] | S. Otten, K. Rolbiecki, S. Caron, J.-S. Kim, R. Ruiz De Austri, and J. Tattersall. DeepXS: Fast approximation of MSSM electroweak cross sections at NLO. arXiv:1810.08312. (GitHub) |

[ECMAInternational17] | ECMA International. The JSON Data Interchange Format. Standard ECMA-404 2nd Edition, ECMA International, December 2017. |