Theoretical Astrophysicist · Carnegie Observatories
I am a Staff Scientist at the Carnegie Observatories. My research is focused on understanding the nature of dark matter and the process of galaxy formation — combining analytic models, numerical simulations, and large astronomical surveys.
Research focus
Three threads tie my research together: building a coherent theoretical model of galaxy formation; constraining the microphysics of dark matter; and designing the synthetic universes that next-generation surveys need to interpret their data.
Galaxy formation
An open-source semi-analytic model of galaxy formation used to interpret observations across cosmic time.
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Dark matter
Using halo substructure, gravitational lensing, and dwarf galaxies to test warm, self-interacting, and other non-CDM models.
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Surveys
Building galaxy mocks for the Roman GRS Project Infrastructure Team and the NASA Open Universe initiative.
Read more →Recent work
These cards are rebuilt automatically from my NASA ADS library on a weekly schedule. Summaries and figures are generated from the paper itself.
This study highlights the sensitivity of gravitational lensing to variations in the modeling of core-collapse in self-interacting dark matter subhalos. The findings indicate that small changes in halo evolution can significantly affect lensing predictions, which is crucial for future analyses in understanding dark matter.
This study reveals that the structure of galactic disks can be accurately predicted from the properties of their dark-matter halos, highlighting the influence of baryonic processes on halo characteristics. The findings provide valuable empirical relations for modeling galaxy structures, particularly emphasizing the differences in predictions based on halo mass and redshift.
DiffstarPop is a new model that accurately simulates the star formation histories of galaxies by linking them to the mass assembly of dark matter halos. This tool can efficiently generate large catalogs of synthetic galaxies, enhancing our understanding of galaxy formation and evolution in cosmological simulations.
This study calibrates the heat transfer parameter in self-interacting dark matter models using advanced simulations, revealing that this parameter is consistent across various halo conditions. The findings provide a reliable model for predicting dark matter halo evolution, streamlining comparisons with simulations and enhancing our understanding of dark matter dynamics.
This research develops a more accurate model for predicting gaps in stellar streams caused by dark matter substructures, finding that the number of expected gaps is significantly higher than previously estimated. This advancement enhances the potential of stellar streams as tools for probing dark matter properties.
This study establishes a direct link between baryonic processes and galaxy assembly bias, revealing that gas cooling and stellar feedback are key factors influencing galaxy clustering. The findings offer valuable insights for improving galaxy survey models and understanding the role of baryonic physics in galaxy formation.
Open source
Most of my modeling work happens inside Galacticus, an open-source semi-analytic model of galaxy formation that I wrote and continue to develop. It's used by groups around the world to study dark matter, galaxy evolution, and forecast observations for upcoming surveys. See the full software stack →