Speaker
Description
QCD calculations that resum soft-collinear logarithms by a parton-shower algorithm can not currently be used in PDF fits. This is due to the high computational cost of generating Monte-Carlo events for each variation of the PDFs, and reduces the number of data points available for the fits. We propose an approximation based on training a NN to predict the effect of varying the shower input parameters and present proof-of-principle results for strong-coupling variations.
Another challenge in QCD Monte-Carlo simulations is effective phase-space sampling. The efficiency of generating unweighted events can easily drop to $10^{-4}$ and less for some state-of-the-art calculations. We present results for using ML to improve the generation of phase-space points both globally and in the local Markov-Chain steps of the $(\text{MC})^3$ method.
