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Astrometric Microlensing of Primordial Black Holes with Gaia

Published 30 Aug 2022 in astro-ph.GA, astro-ph.CO, and hep-ph | (2208.14460v4)

Abstract: The Gaia space telescope allows for unprecedented accuracy for astrometric measurements of stars in the Galaxy. In this work, we explore the sensitivity of Gaia to detect primordial black hole (PBH) dark matter through the distortions that PBHs would create in the apparent trajectories of background stars, an effect known as astrometric microlensing (AML). We present a novel calculation of the lensing probability, and we combine this with the existing publicly released Gaia eDR3 stellar catalog to predict the expected rate of AML events that Gaia will see. We also compute the expected distribution of a few event observables, which will be useful for reducing backgrounds. Assuming that the astrophysical background rate of AML like events due to other sources is negligible, we then compute the potential exclusion that could be set on the parameter space of PBHs with a monochromatic mass function. We find that Gaia is sensitive to PBHs in the range of $0.4~M_\odot$ - $5\times107~M_\odot$, and has peak sensitivity to PBHs of $\sim 10~M_\odot$ for which it can rule out as little as a fraction $3\times10{-4}$ of dark matter composed of PBHs. With this exquisite sensitivity, Gaia has the potential to rule out a PBH origin for the gravitational wave signals seen at LIGO/Virgo. Our novel calculation of the lensing probability includes for the first time, the effect of intermediate duration lensing events, where the lensing event lasts for a few years, but for a period which is still shorter than the Gaia mission lifetime. The lower end of our predicted mass exclusion is especially sensitive to this class of events. As and when time-series data for Gaia is released, and once we have a better understanding of the backgrounds, our prediction of the lensing rate and event observable distributions will be useful to estimate the true exclusion/discovery of the PBH parameter space utilizing this data.

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