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Register a new userAn a priori investigation of astrophysical false positives in ground-based transiting planet surveys
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Astrophysical false positives due to stellar eclipsing binaries pose one of the greatest challenges to ground-based surveys for transiting Hot Jupiters. We have used known properties of multiple star systems and Hot Jupiter systems to predict, a priori, the number of such false detections and the number of genuine planet detections recovered in two hypothetical but realistic ground-based transit surveys targeting fields close to the galactic plane (b~10 degrees): a shallow survey covering a magnitude range 10<V<13, and a deep survey covering a magnitude range 15<V<19. Our results are consistent with the commonly-reported experience of false detections outnumbering planet detections by a factor of ~10 in shallow surveys, while in our synthetic deep survey we find ~1-2 false detections for every planet detection. We characterize the eclipsing binary configurations that are most likely to cause false detections and find that they can be divided into three main types: (i) two dwarfs undergoing grazing transits, (ii) two dwarfs undergoing low-latitude transits in which one component has a substantially smaller radius than the other, and (iii) two eclipsing dwarfs blended with one or more physically unassociated foreground stars. We also predict that a significant fraction of Hot Jupiter detections are blended with the light from other stars, showing that care must be taken to identify the presence of any unresolved neighbors in order to obtain accurate estimates of planetary radii. This issue is likely to extend to terrestrial planet candidates in the CoRoT and Kepler transit surveys, for which neighbors of much fainter relative brightness will be important.
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We report our investigation of the first transiting planet candidate from the YETI project in the young (~4 Myr old) open cluster Trumpler 37. The transit-like signal detected in the lightcurve of the F8V star 2M21385603+5711345 repeats every 1.364894+/-0.000015 days, and has a depth of 54.5+/-0.8 mmag in R. Membership to the cluster is supported by its mean radial velocity and location in the color-magnitude diagram, while the Li diagnostic and proper motion are inconclusive in this regard. Follow-up photometric monitoring and adaptive optics imaging allow us to rule out many possible blend scenarios, but our radial-velocity measurements show it to be an eclipsing single-lined spectroscopic binary with a late-type (mid-M) stellar companion, rather than one of planetary nature. The estimated mass of the companion is 0.15-0.44 solar masses. The search for planets around very young stars such as those targeted by the YETI survey remains of critical importance to understand the early stages of planet formation and evolution.
It has been hypothesized that the presence of closely orbiting giant planets is associated with enhanced chromospheric emission of their host stars. The main cause for such a relation would likely be enhanced dynamo action induced by the planet. We present measurements of chromospheric emission in 234 planet candidate systems from the Kepler mission. This ensemble includes 37 systems with giant planet candidates, which show a clear emission enhancement. The enhancement, however, disappears when systems which are also identified as eclipsing binary candidates are removed from the ensemble. This suggests that a large fraction of the giant planet candidate systems with chromospheric emission stronger than the Sun are not giant planet system, but false positives. Such false-positive systems could be tidally interacting binaries with strong chromospheric emission. This hypotesis is supported by an analysis of 188 eclipsing binary candidates that show increasing chromospheric emission as function of decreasing orbital period.
State of the art exoplanet transit surveys are producing ever increasing quantities of data. To make the best use of this resource, in detecting interesting planetary systems or in determining accurate planetary population statistics, requires new automated methods. Here we describe a machine learning algorithm that forms an integral part of the pipeline for the NGTS transit survey, demonstrating the efficacy of machine learning in selecting planetary candidates from multi-night ground based survey data. Our method uses a combination of random forests and self-organising-maps to rank planetary candidates, achieving an AUC score of 97.6% in ranking 12368 injected planets against 27496 false positives in the NGTS data. We build on past examples by using injected transit signals to form a training set, a necessary development for applying similar methods to upcoming surveys. We also make the texttt{autovet} code used to implement the algorithm publicly accessible. texttt{autovet} is designed to perform machine learned vetting of planetary candidates, and can utilise a variety of methods. The apparent robustness of machine learning techniques, whether on space-based or the qualitatively different ground-based data, highlights their importance to future surveys such as TESS and PLATO and the need to better understand their advantages and pitfalls in an exoplanetary context.
The radial velocity technique is currently used to classify transiting objects. While capable of identifying grazing binary eclipses, this technique cannot reliably identify blends, a chance overlap of a faint background eclipsing binary with an ordinary foreground star. Blends generally have no observable radial velocity shifts, as the foreground star is brighter by several magnitudes and therefore dominates the spectrum, but their combined light can produce events that closely resemble those produced by transiting exoplanets. The radial velocity technique takes advantage of the mass difference between planets and stars to classify exoplanet candidates. However, the existence of blends renders this difference an unreliable discriminator. Another difference must therefore be utilized for this classification -- the physical size of the transiting body. Due to the dependence of limb darkening on color, planets and stars produce subtly different transit shapes. These differences can be relatively weak, little more than 1/10th the transit depth. However, the presence of even small color differences between the individual components of the blend increases this difference. This paper will show that this color difference is capable of discriminating between exoplanets and blends reliably, theoretically capable of classifying even terrestrial-class transits, unlike the radial velocity technique.
We report the discovery, from WASP and CORALIE, of a transiting exoplanet in a 1.54-d orbit. The host star, WASP-36, is a magnitude V = 12.7, metal-poor G2 dwarf (Teff = 5959 pm 134 K), with [Fe/H] = -0.26 pm 0.10. We determine the planet to have mass and radius respectively 2.30 pm 0.07 and 1.28 pm 0.03 times that of Jupiter. We have eight partial or complete transit light curves, from four different observatories, which allows us to investigate the potential effects on the fitted system parameters of using only a single light curve. We find that the solutions obtained by analysing each of these light curves independently are consistent with our global fit to all the data, despite the apparent presence of correlated noise in at least two of the light curves.
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