The Gravitational-Wave Paleontology Group studies the formation, lives, and explosive deaths of massive stars across cosmic time using gravitational waves as “cosmic fossils.” When black holes and neutron stars merge, they produce ripples in spacetime that carry information about the stars that formed them billions of years earlier. By studying these mergers, we aim to answer some of the biggest open questions in astrophysics today: How do black holes and neutron stars form? Which evolutionary pathways produce the gravitational-wave sources we observe? And what can these mergers teach us about the lives, environments, and deaths of massive stars throughout the history of the Universe? This emerging field — gravitational-wave paleontology — opens an entirely new way of studying stellar evolution, allowing us to probe populations of massive stars that are otherwise inaccessible to traditional light-based astronomy because they lived in the distant, early Universe or died long ago.
We are entering the Big Data era of gravitational-wave astronomy. Over the next decade, detectors such as LIGO, Virgo, Cosmic Explorer, the Einstein Telescope, and LISA will increase the number of observed compact-object mergers from hundreds today to millions per year, extending observations to the edge of the observable Universe. However, interpreting this wealth of data requires overcoming a major challenge: gravitational waves do not directly reveal the properties of the progenitor stars that formed them. Instead, connecting observations back to stellar evolution requires large-scale theoretical simulations that model how massive binaries evolve over billions of years. At present, these simulations are computationally expensive, highly uncertain, and often fragmented into isolated “simulation silos,” where different groups explore only narrow regions of parameter space within individual codes. These silos obscure uncertainties, bias physical interpretation, and limit our ability to learn robustly from gravitational-wave observations.
Our group tackles this challenge by combining astrophysics, AI/ML, statistics, and scientific computing to build new uncertainty-aware frameworks, simulation catalogs, and data-driven tools that can transform gravitational-wave detections into a quantitative fossil record of massive-star evolution across cosmic time. Central to this effort is the development of GROWL (GRavitatiOnal Wave paLeontology), a next-generation, community-driven simulation ecosystem that will unify thousands of simulations across formation channels, stellar evolution codes, and physical assumptions into a single interoperable framework. By breaking today’s fragmented “simulation silos,” GROWL aims to make it possible, for the first time, to robustly connect observed gravitational-wave populations back to their progenitor stars and directly test which physical processes — such as mass transfer, supernovae, stellar winds, metallicity, and dynamical interactions — shape the black holes and neutron stars we observe across the Universe.
The lab is deeply interdisciplinary and student-driven, bringing together researchers from astrophysics, data science, statistics, AI/ML, and scientific computing to develop both new scientific insight and new computational methodologies. Students in the group work at the frontier of gravitational-wave astronomy while gaining experience in high-performance computing, machine learning, visualization, statistical inference, and open-source scientific software development. By combining theory, computation, machine learning, and multi-messenger astrophysics, the lab aims not only to advance gravitational-wave astronomy, but to establish gravitational-wave paleontology as a foundational new frontier for understanding how massive stars shaped the Universe across cosmic history.
