Predictable patterns in planetary transit timing variations and transit duration variations due to exomoons

Abstract

We present new ways to identify single and multiple moons around extrasolar planets using planetary transit timing variations (TTVs) and transit duration variations (TDVs). For planets with one moon, measurements from successive transits exhibit a hitherto undescribed pattern in the TTV-TDV diagram, originating from the stroboscopic sampling of the planet's orbit around the planet-moon barycenter. This pattern is fully determined and analytically predictable after three consecutive transits. The more measurements become available, the more the TTV-TDV diagram approaches an ellipse. For planets with multiple moons in orbital mean motion resonance (MMR), like the Galilean moon system, the pattern is much more complex and addressed numerically in this report. Exomoons in MMR can also form closed, predictable TTV-TDV figures, as long as the drift of the moons' pericenters is sufficiently slow. We find that MMR exomoons produce loops in the TTV-TDV diagram and that the number of these loops is equal to the order of the MMR, or the largest integer in the MMR ratio. We use a Bayesian model and Monte Carlo simulations to test the discoverability of exomoons using TTV-TDV diagrams with current and near-future technology. In a blind test, two of us (BP, DA) successfully retrieved a large moon from simulated TTV-TDV by co-authors MH and RH, which resembled data from a known Kepler planet candidate. Single exomoons with a 10% moon-to-planet mass ratio, like to Pluto-Charon binary, can be detectable in the archival data of the Kepler primary mission. Multi-exomoon systems, however, require either larger telescopes or brighter target stars. Complementary detection methods invoking a moon's own photometric transit or its orbital sampling effect can be used for validation or falsification. A combination of TESS, CHEOPS, and PLATO data would offer a compelling opportunity for an exomoon discovery around a bright star.

Simultaneous characterization of the atmospheres, surfaces, and exomoons of nearby rocky exoplanets

“…Before introducing the results, it is important to point out the difference between our method and those in previous exomoon detection efforts, most of which propose to analyze transit photometry or transit timing/duration variations (Heller et al, 2016; Heller, 2017, and references therein). Several works have been published on exomoon detection through analyzing phase‐dependent thermal emission variability of exoplanet‐exomoon system during a full exomoon orbit.…”

“…The ability to detect exomoons could also be important for our understanding of exoplanetary atmospheres (Rein et al, 2014; Li T et al, 2016). Despite many efforts (Kipping, 2009, Agol et al, 2015, Heller et al, 2016; Heller, 2017, and references therein), no exomoon has been detected up to now.…”

“…The ability to detect exomoons could also be important for our understanding of exoplanetary atmospheres (Rein et al, 2014;Li T et al, 2016). Despite many efforts (Kipping, 2009, Agol et al, 2015, Heller et al, 2016Heller, 2017, and references therein), no exomoon has been detected up to now. Inspired by hyperspectral data retrieval methods for Martian mineralogy analysis (the Full Constraint Least Squares algorithm (Heinz and Chang, 2001;Schmidt et al, 2014), referred to as the FCLS algorithm in the following), we developed a method to achieve the goals of obtaining atmospheric composition, retrieving disk-averaged fractions of surface types, and detecting exomoons simultaneously for nearby rocky exoplanet-exomoon systems.…”

Detecting and Characterizing Exomoons and Exorings

“…This phase difference is key to breaking the degeneracy of simultaneous M s and a s measurements (a s being the moon's semimajor axis around the planet). When plotted in a TTV-TDV diagram (Montalto et al 2012;Awiphan and Kerins 2013), the resulting ellipse contains predictable dynamical patterns, which can help to discriminate an exomoon interpretation of the data from a planetary perturber, and it may even allow the detection of multiple moons (Heller et al 2016b) …”

How eclipse time variations, eclipse duration variations, and radial velocities can reveal S-type planets in close eclipsing binaries

“…Additionally, the tangential orbital velocity component of the secondary star in the star-planet system causes EDVs, which are analogous to the transit duration variations of a transiting planet with a moon (Kipping 2009a). Both ETVs and EDVs (or TTVs and TDVs) are phase-shifted by π/2, that is, they form an ellipse in the ETV-EDV (or TTV-TDV) diagram (Heller et al 2016). By using high-precision photometric time series, e.g.…”