Improving estimates of msini by expanding RV datasets

TL;DR

This paper introduces new methods to improve the accuracy of exoplanet mass estimates from radial-velocity data, addressing systematic errors and predicting benefits of future observations.

Contribution

The authors develop techniques including Monte Carlo projection and chimera mitigation to enhance msini uncertainty estimates and assess future observational improvements.

Findings

Adding 10 new RV measurements reduces msini uncertainty by ~18%.

Systematic errors from chimeras can be identified and mitigated.

Future observations can significantly improve mass estimate precision.

Abstract

We develop new techniques for estimating the fractional uncertainty (F) in the projected planetary mass (msini) resulting from Keplerian fits to radial-velocity (RV) datasets of known Jupiter-class exoplanets. The techniques include (1) estimating the distribution of msini using Monte Carlo projection, (2) detecting and mitigating chimeras, a source of systematic error, and (3) estimating the reduction in the uncertainty in msini if hypothetical observations were made in the future. We demonstrate the techniques on a representative set of RV exoplanets, known as the Gang of 27, which are candidates for detection and characterization by a future astrometric direct imaging (ADI) mission. We estimate the improvements (reductions) in F due to additional, hypothetical RV measurements (RVMs) obtained in the future. We encounter and address a source of systematic error, chimeras, which can appear when multiple types of Keplerian solutions are compatible with a single dataset. We find that for n = 10 new, hypothetical RVMs obtained in the last planetary year before 2025, with the same accuracy as the current available RVMs, F is reduced by ~18%. From there, each plus-one increase in 2 log n - log q , where q is the factor by which RVM measurement uncertainty is reduced, further reduces F by factor 0.18.