Weight-Based Representation Learning for Parameter Inference in Monte Carlo Simulations

We present a Machine Learning-based approach for parameter inference in physics models that exploits event-level weights provided by simulators. Individual observations may have weights assigned by a simulation framework that describe the change in probability with respect to the model parameters. As these assigned weights encode the sensitivity of the parameter, they can serve as a weak supervision signal for learning parameter-informative representations. In this work, our inference models are trained using simulator-provided weights to learn representations and their relations to the parameter-sensitive structures in the high-dimensional observations. The resulting representations are then discretised into summary statistics and the model parameter value is inferred using a likelihood-based inference procedure. We illustrate this approach by using simulated four-top-quark production to infer the top quark Yukawa coupling (the parameter of interest).