Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
This addresses the challenge of computationally expensive inference in physics simulations, offering a more efficient method for researchers in particle physics and related fields, though it appears incremental as it builds on existing simulation and inference techniques.
The authors tackled the problem of performing efficient probabilistic inference in complex scientific simulations by developing a novel probabilistic programming framework that interfaces with existing simulators, enabling interpretable posterior inference. They demonstrated this on a particle physics simulation of tau lepton decay, achieving inference at a fraction of the computational cost of a Markov chain Monte Carlo baseline.
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way. The execution of existing simulators as probabilistic programs enables highly interpretable posterior inference in the structured model defined by the simulator code base. We demonstrate the technique in particle physics, on a scientifically accurate simulation of the tau lepton decay, which is a key ingredient in establishing the properties of the Higgs boson. Inference efficiency is achieved via inference compilation where a deep recurrent neural network is trained to parameterize proposal distributions and control the stochastic simulator in a sequential importance sampling scheme, at a fraction of the computational cost of a Markov chain Monte Carlo baseline.