Orateur
Joaquín Iturriza Ramirez
(Lpnhe)
Description
Fast and precise evaluations of scattering amplitudes even in the case of precision calculations is essential for event generation tools at the HL-LHC. We explore the scaling behavior of the achievable precision of neural networks in this regression problem for multiple architectures, including a Lorentz symmetry aware multilayer perceptron and a fully Lorentz equivariant transformer using Lorentz Local Canonicalization (LLoCa). This study addresses in particular the scaling behavior of uncertainty estimations using state of the art methods.