Supermassive black hole (SMBH) mergers are one of the most dramatic phenomena in the Universe. For a few hours, they can emit as much power in gravitational waves as all the stars in the Universe produce in light. However, none has yet been caught in the act, partly because no one knows what sort of light they should emit along with the gravitational waves. This project combines astrophysical knowledge of SMBH binaries with detailed physical simulation of gas flows and explicit computation of time-dependent spacetimes, to predict the light observers should search for. Junior researchers will learn about this new field and about very large-scale computation, strengthening the technical workforce. Ancillary efforts by the PIs enhance the teaching program at each of their universities and contribute to public outreach.

This group has built the knowledge base and computational methods needed to make these predictions, including building a multi-patch simulation infrastructure to compute the changing spacetime and permit separate treatment of subregions. The code base includes post-processing tools that transform fluid simulation data into predictions of photon radiation, accounting for the principal radiation mechanisms, opacities, and photon propagation through dynamical spacetimes. This study starts at the immediate pre-merger state, to understand its physical mechanisms and to prepare realistic initial data. The second step will compute how matter behaves during the merger proper and the subsequent relaxation phase. The post-processing machinery then predicts EM spectra and light curves. This project advances the goals of the NSF Windows on the Universe Big Idea.

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.

Agency
National Science Foundation (NSF)
Institute
Division of Astronomical Sciences (AST)
Type
Standard Grant (Standard)
Application #
2009330
Program Officer
Nigel Sharp
Project Start
Project End
Budget Start
2020-09-01
Budget End
2023-08-31
Support Year
Fiscal Year
2020
Total Cost
$466,853
Indirect Cost
Name
Rochester Institute of Tech
Department
Type
DUNS #
City
Rochester
State
NY
Country
United States
Zip Code
14623