Detecting Transits in Sparsely Sampled Surveys

TL;DR

This paper develops analytical tools and simulations to estimate the potential of large, sparsely sampled surveys like Pan-STARRS and LSST to detect transiting Earth-like planets around low-mass stars, highlighting challenges and opportunities.

Contribution

It introduces simple analytical equations and Monte Carlo simulations to assess the feasibility of detecting transiting planets in sparsely sampled survey data.

Findings

Upper limits on the number of detectable transiting planets in specific surveys.

Identification of challenges in detecting transits with sparse sampling.

Successful application of methods to SDSS-II data for eclipsing binaries.

Abstract

The small sizes of low mass stars in principle provide an opportunity to find Earth-like planets and "super-Earths" in habitable zones via transits. Large area synoptic surveys like Pan-STARRS and LSST will observe large numbers of low mass stars, albeit with widely spaced (sparse) time sampling relative to the planets' periods and transit durations. We present simple analytical equations that can be used to estimate the feasibility of a survey by setting upper limits to the number of transiting planets that will be detected. We use Monte Carlo simulations to find upper limits for the number of transiting planets that may be discovered in the Pan-STARRS Medium Deep and 3-pi surveys. Our search for transiting planets and M-dwarf eclipsing binaries in the SDSS-II supernova data is used to illustrate the problems (and successes) in using sparsely sampled surveys.