A New Dark Matter and Early Universe Grand Science Campaign
William Dawson | 22-ERD-037
Executive Summary
We aim to determine the fraction of dark matter composed of primordial black holes and provide the first early Universe constraints using primordial black holes, which will enable investigators to better understand the nature of black holes and the early Universe. We will leverage a confluence of state-of-the-art gravitational lensing and wave astronomical observatories and their associated surveys of the cosmos, coupled with pioneering methods in photometric microlensing, to discover black holes in real time.
Publications, Presentations, and Patents
William Dawson, et al., “Snowmass 2021 Cosmic Frontier White Paper: Primordial Black Hole Dark Matter” (Presentation, Snowmass Community Summer Study Workshop, University of Washington, Seattle, WA, July 17-26, 2022).
William Dawson, et al., “Black Holes Hiding in Plain Sight” (Presentation, 25th International Microlensing Conference. Paris, France, Aug 31 – Sept 2, 2022).
Golovich, Nathan, William Dawson, Fran Bartolić, Casey Y. Lam, Jessica R. Lu, Michael S. Medford, Michael D. Schneider, George Chapline, Edward F. Schlafly, Alex Drlica-Wagner, and Kerianne Pruett. 2022. “A Reanalysis of Public Galactic Bulge Gravitational Microlensing Events from OGLE-III and -IV.” The Astrophysical Journal Supplement Series, vol. 260, no. 1. doi:10.3847/1538-4365/ac596.
Nathan Golovich, et al., “Astronomy Probes of PBH Dark Matter” (Presentation, 2022 Intermediate-mass Black Holes: New Science from Stellar Evolution to Cosmology, San Juan, Puerto Rico, April 30 – May 3, 2022).
Kerianne Pruett et al., “Primordial Black Hole Microlensing Simulations Using PopSyCLE” (Presentation, 25th International Microlensing Conference. Paris, France, Aug 31 – Sept 2, 2022).
W. A. Dawson, “Primordial Black Holes as Dark Matter and Window into the Earliest Moments of the Universe” (Presentation, 2023 LLNL PLS External Review Committee. Livermore, CA, June 21, 2023).
S. Perkins, “Bayesian Hierarchical Inference of Galactic Populations from Noisy Observations” (LLNL Postdoc Poster Session. April, 20, 2023).