Machine learning-based simulations, especially calorimeter simulations, are
promising tools for approximating the precision of classical high energy
physics simulations with a fraction of the generation time. Nearly all methods
proposed so far learn neural networks that map a random variable with a known
probability density, like a Gaussian, to realistic-looking events. In many
cases, physics events are not close to Gaussian and so these neural networks
have to learn a highly complex function. We study an alternative approach:
Schr\"{o}dinger bridge Quality Improvement via Refinement of Existing
Lightweight Simulations (SQuIRELS). SQuIRELS leverages the power of
diffusion-based neural networks and Schr\"{o}dinger bridges to map between
samples where the probability density is not known explicitly. We apply
SQuIRELS to the task of refining a classical fast simulation to approximate a
full classical simulation. On simulated calorimeter events, we find that
SQuIRELS is able to reproduce highly non-trivial features of the full
simulation with a fraction of the generation time.Comment: 10 pages, 5 figure