Toward Robust Models of Supermassive Black Hole Binary Populations for the Low-Frequency Gravitational Wave Era

University Of Florida, Gainesville FL

Investigators

Abstract

Supermassive black holes live in the centers of most galaxies, including our own Milky Way galaxy. Millions to billions of times more massive than the Sun, supermassive black holes provide crucial insight for understanding how galaxies formed and evolved. When two galaxies collide, their supermassive black holes can form a binary system that spirals together and merges. This cosmic dance creates extremely powerful “gravitational waves” (GWs), which are ripples in the fabric of spacetime. This project will use computer models to study how binary supermassive black holes evolve and to improve GW predictions. The project will also expand the Gator Artificial Intelligence (AI) Camp for high school students, which was created by the lead investigator and launched in Summer 2024. This program will create research and educational opportunities to undergraduate students. The research team will design an improved framework for binary SMBH population modeling for PTAs and the upcoming Laser Interferometer Space Antenna (LISA) mission. This work will address the following fundamental research questions: (1) Which observables and theoretical assumptions dominate the uncertainty in the SMBH population characteristics inferred from the gravitational wave background and future LISA events? (2) How efficiently do SMBH binaries inspiral and merge in different environments, and what is the best way to model this process for PTA and LISA data analysis? To address these questions, the team will (i) compare GW background predictions from simulations and semi-analytic models to design a new modeling approach that leverages the strengths of each method, (ii) develop an empirically based scheme for modeling SMBHB inspiral that connects binary evolution in galactic nuclei to key host galaxy properties, (iii) produce more robust constraints on SMBH binary population characteristics using PTA detections, and (iv) optimize this approach for the low-mass, high-redshift regime and make predictions for LISA event rates and source population characteristics. This project will integrate computational research with high school and undergrad education. The broader societal impacts of this projects include: (1) expanding the Gator AI Camp into a two-week program with a larger team of residential counselors and a program coordinator, (2) developing a virtual Gator AI Camp community to provide attendees with lasting support, (3) providing research opportunities for high school and undergraduate students, and (4) integrating computational skills into undergraduate physics courses. 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.