Title: Accurate and Ultrafast Two-Loop Matrix Elements
Abstract: I will start by discussing an idea and proof-of-concept to replace the exact amplitudes in monte carlo event generators with very precise and approximate ones. This can be naturally achieved with machine learning algorithms. So I will (very briefly) discuss two ideally suited algorithms for the task: boosted decision trees and neural networks with skipped connections.
The focus of the talk, however, will be on the remarkable progress since the proof-of-concept towards implementing this idea into the monte carlo generation pipeline. In particular, for the $qq\to ZZ\to 4\ell$ process which is of phenomenological interest at the LHC. I will show that most of the necessary effort to achieve this goal centered around deconstructing the amplitude and leveraging its symmetries in order to build an optimal set of functions. The final result is that the two-loop virtual amplitude can be evaluated with percent or sub-percent precision in milliseconds. This translates to more than a thousandfold speedup over the exact amplitudes. Finally, I will discuss the extension of this work to the full di-boson set of processes and to gluon-initiated two-loop matrix elements.
Zoom Meeting ID: 948 7183 3595