Predicting the Yields of Photometric Surveys for Transiting Planets

TL;DR

This paper compares forward and backward modeling approaches to predict the yields of photometric transit surveys like TrES, XO, and Kepler, highlighting their insights and uncertainties in exoplanet detection.

Contribution

It introduces and compares two modeling methods for predicting survey yields, providing insights into survey efficiency and astrophysical parameters affecting detections.

Findings

Both modeling approaches offer valuable insights into survey performance.

Predictions help evaluate survey efficiency and astrophysical influences.

Uncertainties in statistical cut-offs impact yield estimates.

Abstract

Observing extrasolar planetary transits is one of the only ways that we may infer the masses and radii of planets outside the Solar System. As such, the detections made by photometric transit surveys are one of the only foreseeable ways that the areas of planetary interiors, system dynamics, migration, and formation will acquire more data. Predicting the yields of these surveys therefore serves as a useful statistical tool. Predictions allows us to check the efficiency of transit surveys (``are we detecting all that we should?'') and to test our understanding of the relevant astrophysics (``what parameters affect predictions?''). Furthermore, just the raw numbers of how many planets will be detected by a survey can be interesting in its own right. Here, we look at two different approaches to modeling predictions (forward and backward), and examine three different transit surveys (TrES, XO, and Kepler). In all cases, making predictions provides valuable insight into both extrasolar planets and the surveys themselves, but this must be tempered by an appreciation of the uncertainties in the statistical cut-offs used by the transit surveys.