Criteria for Sample Selection to Maximize Planet Sensitivity and Yield from Space-Based Microlens Parallax Surveys
Abstract
Space-based microlens parallax measurements are a powerful tool for understanding planet populations, especially their distribution throughout the Galaxy. However, if space-based observations of the microlensing events must be specifically targeted, it is crucial that microlensing events enter the parallax sample without reference to the known presence or absence of planets. Hence, it is vital to define objective criteria for selecting events where possible and to carefully consider and minimize the selection biases where not possible so that the final sample represents a controlled experiment. We present objective criteria for initiating observations and determining their cadence for a subset of events, and we define procedures for isolating subjective decision making from information about detected planets for the remainder of events. We also define procedures to resolve conflicts between subjective and objective selections. These procedures maximize planet sensitivity of the sample as a whole by allowing for planet detections even if they occur before satellite observations for objectively-selected events and by helping to trigger fruitful follow-up observations for subjectively-chosen events. This paper represents our public commitment to these procedures, which is a necessary component of enforcing objectivity on the experimental protocol.
Keywords
Cite
@article{arxiv.1505.00014,
title = {Criteria for Sample Selection to Maximize Planet Sensitivity and Yield from Space-Based Microlens Parallax Surveys},
author = {Jennifer C. Yee and Andrew Gould and Charles Beichman and Sebastiano Calchi Novati and Sean Carey and B. Scott Gaudi and Calen Henderson and David Nataf and Matthew Penny and Yossi Shvartzvald and Wei Zhu},
journal= {arXiv preprint arXiv:1505.00014},
year = {2015}
}
Comments
46 pages; 3 figures; submitted to ApJ. Criterion (A2) for objective selection has been made more restrictive