Estimates of the Planet Yield from Ground-Based High-Contrast Imaging Observations as a Function of Stellar Mass
Justin R. Crepp, John Asher Johnson (Caltech)

TL;DR
This study uses Monte Carlo simulations to estimate the number of directly detectable exoplanets around nearby stars with current and upcoming high-contrast imaging, highlighting the influence of stellar mass and observational biases.
Contribution
It provides a comprehensive model linking stellar properties, planet occurrence rates, and observational factors to predict exoplanet detection yields across different stellar masses.
Findings
High-mass stars like A-stars are prime targets due to higher planet occurrence.
Extrapolation of RV planet data aligns well with current high-contrast imaging results.
In young clusters, low-mass stars can dominate detections due to observational biases.
Abstract
We use Monte Carlo simulations to estimate the number of extrasolar planets that are directly detectable in the solar-neighborhood using current and forthcoming high-contrast imaging instruments. Our calculations take into account the important factors that govern the likelihood for imaging a planet, including the statistical properties of nearby stars, correlations between star and planet properties, observational effects, and selection criteria. We consider several different ground-based surveys and express the resulting yields as a function of stellar mass. Selecting targets based on their youth, visual brightness, and proximity to the Sun, we find that strong correlations between star mass and planet properties are required to reproduce high-contrast imaging results to date. Using the most recent empirical findings for the occurrence rate of planets from RV surveys, our simulations…
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