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Simulations play a key role for inference in collider physics. We explorevarious approaches for enhancing the precision of simulations using machinelearning, including interventions at the end of the simulation chain(reweighting), at the beginning of the simulation chain (pre-processing), andconnections between the end and beginning (latent space refinement). To clearlyillustrate our approaches, we use W+jets matrix element surrogate simulationsbased on normalizing flows as a prototypical example. First, weights in thedata space are derived using machine learning classifiers. Then, we pull backthe data-space weights to the latent space to produce unweighted examples andemploy the Latent Space Refinement (LASER) protocol using Hamiltonian MonteCarlo. An alternative approach is an augmented normalizing flow, which allowsfor different dimensions in the latent and target spaces. These methods arestudied for various pre-processing strategies, including a new and generalmethod for massive particles at hadron colliders that is a tweak on thewidely-used RAMBO-on-diet mapping. We find that modified simulations canachieve sub-percent precision across a wide range of phase space.

ELSA -- Enhanced latent spaces for improved collider simulations