Zodiacal Exoplanets in Time (ZEIT). VII. A Temperate Candidate Super-Earth in the Hyades Cluster

Vanderburg, Andrew; Mann, Andrew W.; Rizzuto, Aaron C.; Bieryla, Allyson; Kraus, Adam L.; Berlind, P.; Calkins, M.; Curtis, Jason L.; Douglas, Stephanie T.; Esquerdo, Gilbert A.; Everett, Mark E.; Horch, Elliott; Howell, Steve B.; Latham, David W.; Mayo, Andrew W.; Quinn, Samuel N.; Scott, Nicholas J.; Stefanik, R. P.

doi:10.3847/1538-3881/aac894

Cited by 45 publications

(28 citation statements)

References 109 publications

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“…By observing fields across the ecliptic, K2 can study stars both near the galactic plane, where many young stars can be found, and the older stars a few scale heights away. K2 also has observed (and discovered planets in) several nearby open clusters and associations (Obermeier et al 2016;Mann et al 2016;Rizzuto et al 2018;Livingston et al 2018c;Vanderburg et al 2018). K2's observational breadth could enable determining occurrence rates for planets around different stellar populations and in different galactic environments, a key to understanding how and why planets form.…”

Section: Introductionmentioning

confidence: 90%

Identifying Exoplanets with Deep Learning. II. Two New Super-Earths Uncovered by a Neural Network in K2 Data

Dattilo¹,

Vanderburg²,

Shallue³

et al. 2019

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For years, scientists have used data from NASA's Kepler Space Telescope to look for and discover thousands of transiting exoplanets. In its extended K2 mission, Kepler observed stars in various regions of sky all across the ecliptic plane, and therefore in different galactic environments. Astronomers want to learn how the population of exoplanets are different in these different environments. However, this requires an automatic and unbiased way to identify the exoplanets in these regions and rule out false positive signals that mimic transiting planet signals. We present a method for classifying these exoplanet signals using deep learning, a class of machine learning algorithms that have become popular in fields ranging from medical science to linguistics. We modified a neural network previously used to identify exoplanets in the Kepler field to be able to identify exoplanets in different K2 campaigns, which range in galactic environments. We train a convolutional neural network, called AstroNet-K2, to predict whether a given possible exoplanet signal is really caused by an exoplanet or a false positive. AstroNet-K2 is highly successful at classifying exoplanets and false positives, with accuracy of 98% on our test set. It is especially efficient at identifying and culling false positives, but for now, still needs human supervision to create a complete and reliable planet candidate sample. We use AstroNet-K2 to identify and validate two previously unknown exoplanets. Our method is a step towards automatically identifying new exoplanets in K2 data and learning how exoplanet populations depend on their galactic birthplace.

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Section: Introductionmentioning

confidence: 90%

Identifying Exoplanets with Deep Learning. II. Two New Super-Earths Uncovered by a Neural Network in K2 Data

Dattilo¹,

Vanderburg²,

Shallue³

et al. 2019

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“…Our period measurement, P = 25.0 +49.8 −0.1 days, is compatible with both. Vanderburg et al (2018) estimate the period of EPIC 248045685b by a very similar method to ours, also exploiting information from the host star's Gaia parallax and public broadband photometry. They conclude that P = 106 +74 −26 days, and we agree, with P = 118 +119 −41 days.…”

Section: Comparison To Previous Workmentioning

confidence: 99%

Estimation of singly transiting K2 planet periods with Gaia parallaxes

Sandford

Espinoza

Brahm

et al. 2019

Monthly Notices of the Royal Astronomical Society

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When a planet is only observed to transit once, direct measurement of its period is impossible. It is possible, however, to constrain the periods of single transiters, and this is desirable as they are likely to represent the cold and far extremes of the planet population observed by any particular survey. Improving the accuracy with which the period of single transiters can be constrained is therefore critical to enhance the longperiod planet yield of surveys. Here, we combine Gaia parallaxes with stellar models and broad-band photometry to estimate the stellar densities of K2 planet host stars, then use that stellar density information to model individual planet transits and infer the posterior period distribution. We show that the densities we infer are reliable by comparing with densities derived through asteroseismology, and apply our method to 27 validation planets of known (directly measured) period, treating each transit as if it were the only one, as well as to 12 true single transiters. When we treat eccentricity as a free parameter, we achieve a fractional period uncertainty over the true single transits of 94 +87 −58 %, and when we fix e = 0, we achieve fractional period uncertainty 15 +30 −6 %, a roughly threefold improvement over typical period uncertainties of previous studies.

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“…The prospects for these types of studies have been greatly enhanced by recent discoveries of transiting planets around young stars. Data from the NASA K2 mission have been used to find planets in the 10-Myr-old Upper-Sco moving group (Mann et al 2016b;David et al 2016), the 20-Myr-old Taurus-Auriga group (David et al 2019), and older clusters such as Praesepe and the Hyades (e.g Mann et al 2016aMann et al , 2017Vanderburg et al 2018;Rizzuto et al 2018). These efforts have also resulted in the first determinations of the occurrence rates of close-in small planets in young associations and clusters, and allowed meaningful comparisons with the planet population around mature mainsequence field stars (Rizzuto et al 2017).…”

Section: Introductionmentioning

confidence: 99%

A Well-aligned Orbit for the 45 Myr-old Transiting Neptune DS Tuc Ab

Zhou

Winn

Newton

et al. 2020

ApJL

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DS Tuc Ab is a Neptune-sized planet that orbits around a G star in the 45 Myr old Tucana-Horologium moving group. Here, we report the measurement of the sky-projected angle between the stellar spin axis and the planet's orbital axis, based on the observation of the Rossiter-McLaughlin effect during three separate planetary transits. The orbit appears to be well aligned with the equator of the host star, with a projected obliquity of λ = 2.5 +1.0 −0.9• . In addition to the distortions in the stellar absorption lines due to the transiting planet, we observed variations that we attribute to large starspots, with angular sizes of tens of degrees. The technique we have developed for simultaneous modeling of starspots and the planet-induced distortions may be useful in other observations of planets around active stars.

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Zodiacal Exoplanets in Time (ZEIT). VII. A Temperate Candidate Super-Earth in the Hyades Cluster

Cited by 45 publications

References 109 publications

Identifying Exoplanets with Deep Learning. II. Two New Super-Earths Uncovered by a Neural Network in K2 Data

Identifying Exoplanets with Deep Learning. II. Two New Super-Earths Uncovered by a Neural Network in K2 Data

Estimation of singly transiting K2 planet periods with Gaia parallaxes

A Well-aligned Orbit for the 45 Myr-old Transiting Neptune DS Tuc Ab

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