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Microlensing Discovery and Characterization Efficiency in the Vera C. Rubin Legacy Survey of Space and Time
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The Vera C. Rubin Legacy Survey of Space and Time will discover thousands of microlensing events across the Milky Way Galaxy, allowing for the study of populations of exoplanets, stars, and compact objects. We evaluate numerous survey strategies simulated in the Rubin Operation Simulations (OpSims) to assess the discovery and characterization efficiencies of microlensing events. We have implemented three metrics in the Rubin Metric Analysis Framework: a discovery metric and two characterization metrics, where one estimates how well the lightcurve is covered and the other quantifies how precisely event parameters can be determined. We also assess the characterizability of microlensing parallax, critical for detection of free-floating black hole lenses. We find that, given Rubin's baseline cadence, the discovery and characterization efficiency will be higher for longer duration and larger parallax events. Microlensing discovery efficiency is dominated by the observing footprint, where more time spent looking at regions of high stellar density including the Galactic bulge, Galactic plane, and Magellanic clouds, leads to higher discovery and characterization rates. However, if the observations are stretched over too wide an area, including low-priority areas of the Galactic plane with fewer stars and higher extinction, event characterization suffers by > 10%. This could impact exoplanet, binary star, and compact object events alike. We find that some rolling strategies (where Rubin focuses on a fraction of the sky in alternating years) in the Galactic bulge can lead to a 15-20% decrease in microlensing parallax characterization, so rolling strategies should be chosen carefully to minimize losses.
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Cited by 3 Pith papers
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Microlensing Detection and Inference via Learned Bayes Factors
A unified transformer-based pipeline detects 99.9% of recoverable simulated microlensing events and outperforms literature hard cuts in the short-duration finite-source regime with amortized neural posterior inference.
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Assessing the Impact of Binary Systems on Microlensing Using SPISEA and PopSyCLE Population Simulations
Adding binary and triple systems to microlensing population simulations predicts that 55% of OGLE-like events involve a multiple system, most of which masquerade as single-star events.
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