WoU-MMA: The GRavitatiOnal-Wave Paleontology CataLog (GROWL): Breaking Simulation Silos To Study Compact Object Mergers Across Cosmic Time

The coming decade of gravitational-wave (GW) astronomy will transform our understanding of massive stars and compact objects: black holes, and neutron stars. As detections from the currently operating GW telescope called LIGO–Virgo–KAGRA and from the next-generation observatories grow from hundreds to thousands and eventually hundreds of thousands per year, we are entering an era of GW “paleontology”—using compact object mergers as fossil records of stellar evolution across cosmic time. Yet, theoretical interpretation of these observations remains severely limited by what scientists call “simulation silos”: models that differ in physical assumptions, data formats, and parameter coverage, hindering meaningful comparison and reproducibility. A project at the University of California San Diego (UCSD) establishes GROWL (GRavitational-Wave paleontLogy CataLog)—a unified, publicly accessible framework to standardize, compare, and visualize more than 10,000 models that investigate mergers of compact objects across cosmic time. The project will also create hands-on demonstrations—such as binary-evolution activities using scaled stellar models, “craft-your-own-compact-object” stations, and fabric spacetime demonstrations—featured at UCSD outreach events and in high school classrooms. These activities use storytelling and active learning to improve conceptual understanding and spark interest in science. GROWL will (1) integrate existing simulations from leading codes (e.g., COMPAS, StarTrack, SEVN, POSYDON, BPASS, COSMIC); (2) quantify and visualize model-dependent uncertainties; (3) extend predictions through cosmic integration with metallicity-dependent star-formation histories; (4) quantify the various merger properties across cosmic time, and (5) release interactive visualization and analysis tools through the GW Landscape dashboard. The GROWL project will enable transparent, quantitative comparisons across codes, physical assumptions, and evolutionary pathways. By systematically mapping model dependencies, GROWL will reveal which features of predicted GW populations—such as merger rates, delay times, and mass distributions—are robust across models and which reflect uncertain stellar-physics prescriptions. This framework will connect stellar evolution to GW observations across redshift, help constraining processes such as common-envelope evolution, mass transfer, and supernova mechanisms. Its standardized data structure and metadata schema will accelerate reproducible research, support machine-learning–based inference, and foster community collaboration across population-synthesis efforts. 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.