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arXiv · 1404.7495

Synthesizing Exoplanet Demographics from Radial Velocity and Microlensing Surveys, I: Methodology

Abstract

Motivated by the order-of-magnitude difference in the frequency of giant planets orbiting M dwarfs inferred by microlensing and radial velocity (RV) surveys, we present a method for comparing the statistical constraints on exoplanet demographics inferred from these methods. We first derive the mapping from the observable parameters of a microlensing-detected planet to those of an analogous planet orbiting an RV-monitored star. Using this mapping, we predict the distribution of RV observables for the planet population inferred from microlensing surveys, taking care to adopt reasonable priors for, and properly marginalize over, the unknown physical parameters of microlensing-detected systems. Finally, we use simple estimates of the detection limits for a fiducial RV survey to predict the number and properties of analogs of the microlensing planet population such an RV survey should detect. We find that RV and microlensing surveys have some overlap, specifically for super-Jupiter mass planets ($m_p \gtrsim 1~M_{\rm Jup}$) with periods between $\sim 3-10~$years. However, the steeply falling planetary mass function inferred from microlensing implies that, in this region of overlap, RV surveys should infer a much smaller frequency than the overall giant planet frequency ($m_p\gtrsim 0.1~M_{\rm Jup}$) inferred by microlensing. Our analysis demonstrates that it is possible to statistically compare and synthesize data sets from multiple exoplanet detection techniques in order to infer exoplanet demographics over wider regions of parameter space than are accessible to individual methods. In a companion paper, we apply our methodology to several representative microlensing and RV surveys to derive the frequency of planets around M dwarfs with orbits of $\lesssim 30~$years.

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Christian Clanton, Scott Gaudi. 2014-04-29. Synthesizing Exoplanet Demographics from Radial Velocity and Microlensing Surveys, I: Methodology. https://doi.org/10.1088/0004-637x/791/2/90

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