Transiting Planets with LSST II. Period Detection of Planets Orbiting 1 Solar Mass Hosts

TL;DR

This study evaluates LSST's capability to detect transiting exoplanets around solar-mass stars using simulated data, demonstrating effective period recovery especially with deep drilling cadence for planets up to 20 days.

Contribution

It provides an assessment of the BLS algorithm's effectiveness in recovering exoplanet periods from LSST simulated light curves, highlighting the advantages of different observing cadences.

Findings

LSST can reliably detect Hot Jupiters with periods under 3 days.

Deep drilling cadence improves detection of planets up to 20 days.

A BLS power criterion can distinguish 98% of true positives from false positives.

Abstract

The Large Synoptic Survey Telescope (LSST) will photometrically monitor approximately 1 billion stars for ten years. The resulting light curves can be used to detect transiting exoplanets. In particular, as demonstrated by Lund et al. (2015), LSST will probe stellar populations currently undersampled in most exoplanet transit surveys, including out to extragalactic distances. In this paper we test the efficiency of the box-fitting least-squares (BLS) algorithm for accurately recovering the periods of transiting exoplanets using simulated LSST data. We model planets with a range of radii orbiting a solar-mass star at a distance of 7 kpc, with orbital periods ranging from 0.5 to 20 days. We find that standard-cadence LSST observations will be able to reliably recover the periods of Hot Jupiters with periods shorter than approximately 3 days, however it will remain a challenge to confidently distinguish these transiting planets from false positives. At the same time, we find that the LSST deep drilling cadence is extremely powerful: the BLS algorithm successfully recovers at least 30% of sub-Saturn-size exoplanets with orbital periods as long as 20 days, and a simple BLS power criterion robustly distinguishes approximately 98% of these from photometric (i.e. statistical) false positives.