Bonn 2020 – scientific programme
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T: Fachverband Teilchenphysik
T 71: Supersymmetry: Theory and searches
T 71.4: Talk
Thursday, April 2, 2020, 17:15–17:30, H-HS VI
An application of neural networks in the phenomenological minimal supersymmetric standard model — Bein Samuel1, Mrowietz Malte1, Schleper Peter1, Prosper Harrison2, and •Wiederspan Bogdan1 — 1University Hamburg, Germany — 2Florida State University, United States of America
The simplified model approach to interpreting searches for supersymmetry (SUSY) has been extensively used so far in both CMS and ATLAS. In recent times, interpretation have also been done in terms of full models, like the phenomenological minimal supersymmetric standard model (pMSSM). The pMSSM offers the advantages of interpretations in full models, while it contains a manageable number of just 19 degrees of freedom, in contrast to e.g. the over 100 degrees of freedom in the minimal supersymmetric standard model. Neural networks in particle physics have shown their usefulness in the recent past, especially in higher-dimensional problems, where it is hard to detect patterns. A feasibility study for using a neural network to regress on the acceptance of a complex SUSY search is presented.