Since the discovery of the infamous Higgs boson in 2012 a large-scale campaign in search for hints for physics beyond the Standard Model of particle physics, and for particle explanations for Dark Matter has been sparked at the Large Hadron Collider (LHC). Such hints will first manifest as tiny variations with respect to Standard Model predictions in the production probabilities for particle scattering processes at the LHC.
The hunt for these hints represents the proverbial search for the needle in the haystack – with the important additional challenge: we don’t know how the needle looks like. Larger and larger accumulated data sets allow us to extend the search to higher and higher energy scales and precision. However, to fully harness these datasets, the experimental precision has to be matched by equally precise theoretical predictions in the Standard Model including intricate quantum corrections. I.e. we must understand every detail of our haystack to find the unknown needle! In this talk, I will explain how the language of Quantum Field Theory allows us to provide such precise theory predictions required for the search of the Unknown.
As a Senior Research Fellow at Sussex, Dr Lindert develops high-precision predictions and simulations for a broad range of Standard Model processes, including quantum corrections. The control of these corrections will allow to stress-test the Standard Model at unprecedented levels of precision. Corresponding predictions are computationally very complex and require the development of dedicated efficient algorithms and tools. Before joining Sussex in 2019 as an Ernest Rutherford Fellow, Dr Jonas worked as a postdoctoral researcher at Durham University and the University of Zurich. He obtained his PhD at the Max Planck Institute for Physics, Munich in 2013.
Sussex Universe is the public lecture series from the science schools at the University of Sussex. Lectures take place every two weeks during the Autumn and Spring teaching semesters. The lectures are completely free to attend and are accessible to a broad audience with no background knowledge.
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