Accelerating statistical inferences in astrophysics with Neural Networks and Hamiltonian Monte Carlo

Diego Gonzalez-Hernandez, Molly Wolfson, Joseph Hennawi

Keywords: Emulators, Hamiltonian Monte Carlo, Astrophysics, Epoch of Reionization

Abstract: We present an approach to accelerate statistical inferences in astrophysics by using a combination of neural networks and Hamiltonian Monte Carlo. The neural networks are used to create high-fidelity surrogates of computationally expensive models, while Hamiltonian Monte Carlo accelerates the inferences by more efficiently exploring the parameter space. We demonstrate the potential of this approach by applying it to a realistic model for the Epoch of Reionization.

Submission Number: 174