# 26th International Conference on Supersymmetry and Unification of Fundamental Interactions (SUSY2018)

23-27 July 2018
Barcelona
Europe/Zurich timezone

## Fast approximation of SUSY NLO cross-sections using Deep Learning

24 Jul 2018, 14:54
24m
Plaza 1

Talk (closed)

### Speaker

Sydney Otten (Radboud University Nijmegen and University of Amsterdam)

### Description

Although deep learning might appear as a magic black box one only needs to throw data at to receive a solution, the practical reality is always different and often difficult. This talk shall guide through an example for the process of constructing an AI for predicting a physical quantity, namely the pMSSM-19 NLO electroweakino production cross-section at the LHC for 13 TeV. Naively, one could assume that this is merely a simple regression task but as I will demonstrate, classifiers, active learning and feature engineering including an injection of deeper expert knowledge played a crucial role for solving this task and will, if not necessary, at least be beneficial for many other tasks. While the available Monte Carlo methods take several minutes per cross-section, the resulting AI is able to deliver $\approx\,10^5$ NLO cross-sections per second with an average error of about $0.1\,\%$ and a maximum error below uncertainties.

Parallel Session Precision Calculations and MC tools

### Primary authors

Sydney Otten (Radboud University Nijmegen and University of Amsterdam) Krzysztof Rolbiecki (University of Warsaw) Jamie Tattersall (RWTH Aachen) Sascha Caron (Nikhef National institute for subatomic physics (NL))

### Presentation materials

 SUSY18_SydneyOtten.pdf SUSY18_SydneyOtten.pptx