Collaborative Research: CDS&E: AI-Enhanced Exascale Simulations Of The Earliest Galaxies

University Of California-San Diego, La Jolla CA

Investigators

Abstract

Impressive recent advances in accelerating physics-based scientific simulations using deep neural network-trained surrogate models have stimulated this project, to use such models to accelerate cosmological simulations of the formation of the first galaxies, including chemical pre-enrichment by Population III stars. The surrogate model will be trained on high-resolution adaptive mesh refinement (AMR) simulations and validated by incorporating it into AMR simulations of lower spatial resolution. Once validated, this approach will become part of Enzo-E, the highly scalable version of the widely used Enzo code. A principal goal is to establish the validity and usefulness of this pathfinding approach, addressing a frontier problem in astronomy by combining numerical simulations with machine learning. This technical methodology can be customized for other astrophysical situations where a vast range of scales inhibits a direct numerical assault. It will also help to stimulate adoption of surrogate models in multiple disciplines, and lead to improved science-informed deep learning neural network designs. The surrogate model, to be called Pop3Net, is composed of several convolutional deep neural networks of custom design which predict when and where Pop III stars form, and other networks that emulate radiative and chemical feedback effects. Pop3Net will be trained using state-of-the-art full-physics simulations of the formation of the first galaxies. The hybrid Enzo-E+Pop3Net code will be used to simulate the formation of large ensembles of high redshift galaxies, computing synthetic observations using the Powderday package, to compare with future data from the James Webb Space Telescope and the Nancy Grace Roman Space Telescope. This part of the work will examine how sensitive galaxy properties are to the chemical enrichment history of the first galaxies, including supersonic baryon-dark matter streaming velocity effects. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.