Observational Window Functions in Planet Transit Searches

TL;DR

This paper analyzes how observational strategies and noise types influence the probability of detecting planetary transits, providing insights into optimizing transit search methods considering various astrophysical and instrumental factors.

Contribution

It introduces a detailed simulation framework for window functions that accounts for both white and red noise, enhancing understanding of detection probabilities in transit surveys.

Findings

Detection probability depends on observing run length, cadence, and transit duration.

Red noise significantly reduces detection efficiency compared to white noise.

Optimal observing strategies vary with noise characteristics and astrophysical parameters.

Abstract

Window functions describe, as a function of orbital period, the probability that an existing planetary transit is detectable in one's data for a given observing strategy. We show the dependence of this probability upon several strategy and astrophysical parameters, such as length of observing run, observing cadence, length of night, and transit duration. The ability to detect a transit is directly related to the intrinsic noise of the observations. In our simulations of the window function, we explicitly address non-correlated (gaussian or white) noise and correlated (red) noise and discuss how these two different noise components affect window functions in different manners.