Abstract
Wide-field surveys for transiting planets are well suited to searching diverse stellar populations, enabling a better understanding of the link between the properties of planets and their parent stars. We report the discovery of HAT-P-69 b (TOI 625.01) and HAT-P-70 b (TOI 624.01), two new hot Jupiters around A stars from the Hungarian-made Automated Telescope Network (HATNet) survey that have also been observed by the Transiting Exoplanet Survey Satellite. HAT-P-69 b has a mass of MJup and a radius of RJup and resides in a prograde 4.79 day orbit. HAT-P-70 b has a radius of RJup and a mass constraint of MJup and resides in a retrograde 2.74 day orbit. We use the confirmation of these planets around relatively massive stars as an opportunity to explore the occurrence rate of hot Jupiters as a function of stellar mass. We define a sample of 47,126 main-sequence stars brighter than Tmag = 10 that yields 31 giant planet candidates, including 18 confirmed planets, 3 candidates, and 10 false positives. We find a net hot Jupiter occurrence rate of 0.41 ± 0.10% within this sample, consistent with the rate measured by Kepler for FGK stars. When divided into stellar mass bins, we find the occurrence rate to be 0.71 ± 0.31% for G stars, 0.43 ± 0.15% for F stars, and 0.26 ± 0.11% for A stars. Thus, at this point, we cannot discern any statistically significant trend in the occurrence of hot Jupiters with stellar mass.
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1. Introduction
Radial velocity and transit surveys have been responsible for the discovery of about 400 close-in giant planets with periods less than 10 days.49 These "hot Jupiters" are the best-characterized exoplanets and are test beds for nearly all of the techniques to measure the densities, composition, atmospheres, orbital, and dynamical properties of exoplanetary systems. Hot Jupiters are also extreme examples of planetary migration, thought to have formed beyond the ice line, and migrated to their present-day locations via interactions with the protoplanetary gas disk, or via dynamical interactions with nearby planets or stars followed by tidal migration (as recently reviewed by Dawson & Johnson 2018).
About three-quarters of the known hot Jupiters have emerged from ground-based, wide-field transit surveys. These surveys have been successful not only in detecting a large number of planets, but also in searching a wide range of stellar types, thanks to their wide-field sky coverage. Transiting Jovian planets have been confirmed around stars ranging from M dwarfs (HATS-6, Hartman et al. 2015; NGTS-1, Bayliss et al. 2018; HATS-71, Bakos et al. 2018) to A stars (e.g., WASP-33, Collier Cameron et al. 2010; KELT-9, Gaudi et al. 2017).
The properties of planets are thought to be dependent on the properties of the host stars. In particular, more massive stars may host more massive protoplanetary disks (e.g., Natta et al. 2006). Radial velocity surveys of intermediate-mass subgiants ("retired A stars") reported that giant planets are more abundant around more massive stars, but tend to have wider and more circular orbits than their lower-mass main-sequence counterparts (Johnson et al. 2010; Jones et al. 2014; Reffert et al. 2015; Ghezzi et al. 2018). Data from the Kepler primary mission allowed for the determination of occurrence rates for planets as small as 1 R⊕ around FGK stars (e.g., Howard et al. 2012; Dong & Zhu 2013; Fressin et al. 2013; Petigura et al. 2013; Burke et al. 2015; Petigura et al. 2018). In particular, occurrence rates from Kepler indicate that small planets with orbital periods less than a year are more common around less massive stars (Dressing & Charbonneau 2013; Mulders et al. 2015).
Despite this progress, many questions remain unanswered. Planets around main-sequence A stars are still poorly explored. A stars have radii as large as 4 R⊙ on the main sequence, causing the transit depth of a Jovian planet to be 16 times smaller than it would be for a solar-type star. As such, ground-based transit surveys have poor completeness in this regime. The Kepler mission could have performed a sensitive search for giant planets around A stars, but in fact very little data from main-sequence A stars were obtained, because the mission was geared toward the detection of smaller planets for which FGK stars are more favorable. For these reasons, there has been no robust determination of the frequency of giant planets around main-sequence A stars.
There has also been tension between the occurrence rates of hot Jupiters measured by Kepler (0.43 ± 0.05% from Fressin et al. 2013, from Petigura et al. 2018, from Masuda & Winn 2017) and those from radial velocity surveys (1.5 ± 0.6% from Cumming et al. 2008, 1.2 ± 0.4% from Wright et al. 2012). These differences have been attributed to metallicity (e.g., Wright et al. 2012), stellar age, or multiplicity (Wang et al. 2015, although see also Bouma et al. 2018). Surveying different populations with a diverse set of host stars may help resolve these tensions.
The launch of the Transiting Exoplanet Survey Satellite (TESS; Ricker et al. 2016) heralds a new era of exoplanet characterization. In particular, the 30 minute cadence Full Frame Images (FFI) are providing us with an opportunity to search a wide range of stellar types. Unlike Kepler, with TESS there is no need to preselect the target stars to be within a certain range of masses or sizes. Based on observations of seven sky sectors between late 2018 July and 2019 February, TESS has delivered space-based photometry for 126,950 stars brighter than Tmag = 10. The promise of near-complete sensitivity from space-based photometry to hot Jupiters across the main sequence and the availability of follow-up results from the tremendous efforts of the TESS follow-up program motivate another look into the occurrence rates of hot Jupiters.
In this paper, we describe the confirmation of two planets discovered by the Hungarian-made Automated Telescope Network (HATNet) survey around A stars, members of a relatively unexplored planet demographic. TESS data for these objects became available during our confirmation process and were independently identified as planet candidates based on FFI photometry. The follow-up observations, modeling of the systems, and derived system parameters are described in Sections 2 and 3. In Section 4, we describe our estimates of the occurrence rates of hot Jupiters around main-sequence A, F, and G stars. The estimate makes use of a magnitude-limited sample of main-sequence stars surveyed by TESS during its first seven sectors, planets cataloged in the TESS Objects of Interest (TOI) list, existing planets from the literature recovered by TESS, and false-positive rates estimated via vetting observations of the TESS follow-up program.
2. Observations
HAT-P-69 and HAT-P-70 were identified as transiting planet candidates by the HATNet survey (Bakos et al. 2004). HAT-P-69 was observed by HATNet between 2010 November and 2011 June, resulting in approximately 24,000 photometric data points. Subsequently, it received photometric and spectroscopic follow-up observations over 2011–2019 that confirmed its planetary nature. It was then observed during Sector 7 of the TESS mission, flagged as a transiting planet candidate by the MIT quick-look pipeline (C. X. Huang et al. 2019, in preparation), and assigned TOI number 625. These highly precise space-based photometric observations are subsequently incorporated in the analyses below. HAT-P-69 was also independently identified as a planet candidate (1SWASPJ084201.35+034238.0) by the Wide Angle Search for Planets (WASP) survey (Pollacco et al. 2006) and was the subject of extensive photometric follow-up via the WASP survey team. These observations are described in Section 2.1 and included in the global analyses.
HAT-P-70 was identified as a planet candidate based on nearly 10,000 HATNet observations spanning the interval from 2009 September to 2010 March. Subsequent ground-based photometric follow-up observations were attempted during the 2016–2017 time frame, but these observations failed to recover the transit event due to the accumulation of uncertainty in the transit ephemerides. HAT-P-70 was also independently identified as a hot Jupiter candidate by the Massachusetts Institute of Technology (MIT) quick-look pipeline and given the designation TOI 624. The revised ephemeris from TESS allowed us to successfully perform photometric and spectroscopic follow-up observations that confirmed the planetary nature of the system. HAT-P-70 was also identified by the WASP survey independently as a planet candidate (1SWASPJ045812.56+095952.7), receiving substantial ground-based photometric follow-up prior to the TESS observations.
2.1. Photometry
2.1.1. Candidate Identification by HATNet
The HATNet survey (Bakos et al. 2004) is one of the longest-running wide-field photometric surveys for transiting planets. It employs a network of small robotic telescopes at the Fred Lawrence Whipple Observatory (FLWO) in Arizona and at Maunakea Observatory in Hawaii. Each survey field is 8° × 8°, and observations are obtained with the Sloan r' filter. Observations are reduced following the process laid out by Bakos et al. (2010). Light curves were extracted via aperture photometry. Systematic effects were mitigated using external parameter decorrelation (EPD; Bakos et al. 2007), and the trend filtering algorithm (TFA; Kovács et al. 2005). Periodic transit signals were identified via the box-fitting least squares analysis (BLS; Kovács et al. 2002). The HATNet observations are summarized in Table 1, and the discovery light curves are shown in Figure 1.
Table 1. Summary of Photometric Observations
Target | Facility | Date(s) | Number of Imagesa | Cadence (s)b | Filter |
---|---|---|---|---|---|
HAT-P-69 | WASP-South/North | 2009 Jan 14–2012 Apr 23 | 25282 | 432 | WASP Broadband |
HAT-P-69 | HAT-6 | 2010 Nov 2–2011 Apr 21 | 10384 | 229 | r |
HAT-P-69 | HAT-7 | 2010 Nov 2–2011 May 25 | 8707 | 233 | r |
HAT-P-69 | HAT-7 | 2011 Feb 14–2011 Jun 3 | 4539 | 215 | r |
HAT-P-69 | KeplerCam 1.2 m | 2011 Dec 15 | 93 | 170 | z |
HAT-P-69 | KeplerCam 1.2 m | 2012 Jan 3 | 417 | 44 | z |
HAT-P-69 | LCO BOS 1.0 m | 2012 Feb 20 | 170 | 48 | i |
HAT-P-69 | LCO BOS 1.0 m | 2012 Apr 8 | 223 | 68 | i |
HAT-P-69 | KeplerCam 1.2 m | 2013 Mar 14 | 617 | 24 | i |
HAT-P-69 | KeplerCam 1.2 m | 2018 Feb 6 | 759 | 22 | z |
HAT-P-69 | KeplerCam 1.2 m | 2018 Mar 2 | 886 | 22 | z |
HAT-P-69 | TRAPPIST 0.6 m | 2018 Nov 11 | 234 | 60 | RCc |
HAT-P-69 | TRAPPIST 0.6 m | 2018 Dec 5 | 251 | 60 | GCd |
HAT-P-69 | KeplerCam 1.2 m | 2019 Jan 12 | 381 | 18 | i |
HAT-P-69 | TRAPPIST 0.6 m | 2019 Feb 9 | 223 | 52 | RC |
HAT-P-69 | TESS | 2019 Jan 8–2019 Feb 1 | 1087 | 1800 | TESS |
HAT-P-70 | WASP-North | 2008 Oct 13–2011 Feb 4 | 19266 | 351 | WASP Broadband |
HAT-P-70 | HAT-9 | 2009 Sep 19–2010 Mar 30 | 9987 | 224 | r |
HAT-P-70 | TRAPPIST 0.6 m | 2018 Sep 23 | 238 | 40 | RC |
HAT-P-70 | TRAPPIST 0.6 m | 2018 Nov 5 | 376 | 40 | RC |
HAT-P-70 | TRAPPIST 0.6 m | 2018 Nov 27 | 231 | 35 | RC |
HAT-P-70 | TRAPPIST 0.6 m | 2018 Dec 9 | 209 | 42 | GC |
HAT-P-70 | TESS | 2018 Nov 15–2018 Dec 10 | 1024 | 1800 | TESS |
HAT-P-70 | KeplerCam 1.2 m | 2019 Feb 21 | 563 | 18 | i |
Notes.
aOutlying exposures have been discarded. bMedian time difference between points in the light curve. Uniform sampling was not possible due to visibility, weather, and pauses. cRC: Red continuum filter centered at 7128 Å with width of 58 Å. dGC: Green continuum filter centered at 5260 Å with width of 65 Å.Download table as: ASCIITypeset image
2.1.2. TESS Observations
HAT-P-69 and HAT-P-70 were observed by TESS during Year 1 of its primary mission. HAT-P-69 is present in the Camera 1 FFIs obtained during the Sector 7 campaign, between 2019 January 7 and February 2. HAT-P-70 is present on the Camera 1 FFIs in Sector 5, between 2018 November 15 and December 11. TESS FFIs provide approximately 27 days of nearly continuous monitoring for all stars within its field of view.
We extracted the FFI light curves of the two systems with the lightkurve package (Barentsen et al. 2019) using the public FFI images from the Mikulski Archive for Space Telescopes (MAST) archive produced from the Science Processing Operations Center pipeline (Jenkins et al. 2016). The raw aperture photometry light curves are diluted by the presence of nearby bright stars. In particular, HAT-P-70 is located within (1.6 pixels) of a fainter star with a magnitude difference of . We extracted 10 × 10 pixel subrasters surrounding each star and defined photometric apertures to include all pixels with fluxes higher than 68% of the fluxes of nearby pixels. For HAT-P-70, this aperture includes both the target star and the nearby neighbor. For HAT-P-69, the photometric aperture does not contain any other stars within 6 mag of the target star. Nearby pixels of apparently blank sky were used to estimate the background flux surrounding the target star. Figure 2 shows each star as observed by TESS, along with the photometric aperture. An R-band image of the star field from the Digitized Sky Survey 2 (McLean et al. 2000) is also shown for reference. The extracted light curve of HAT-P-70 was then deblended, based on the magnitudes of nearby stars from version 6 of the TESS Input Catalog (Stassun et al. 2018).
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Standard image High-resolution imageFigures 3 and 4 present the TESS light curves of the target stars. The TESS light curves of HAT-P-69 and HAT-P-70 show no large systematic variation, nor signs of pulsations or additional eclipsing companions. The TESS transit signals agree in depth with the depths that are measured from ground-based observations.
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Standard image High-resolution imagePhase Modulation and Secondary Eclipses—Hot Jupiters on circular orbits are expected to be tidally locked (e.g., Mazeh 2008), with a fixed dayside atmosphere facing the star at all times. As a result, there can be large temperature differences between the dayside and nonilluminated nightside. During a secondary eclipse, when the planet passes behind the star, the total flux from the dayside is occulted. In addition, as the planet orbits the host star, the flux from the planet's sky-projected hemisphere changes periodically, producing an atmospheric brightness modulation.
To search for these signals in the TESS data, we fit a simple phase curve model to the full light curve (transits, secondary eclipses, and out-of-eclipse flux modulation), following the methods described in detail in Shporer et al. (2019). Given the geometry of the system, the extrema of the atmospheric brightness modulation occur during conjunction, that is, a cosine of the orbital phase. The out-of-eclipse flux is therefore given by , where is the orbital phase, and B1 is the semiamplitude of the phase curve signal. We include secondary eclipse signals halfway between transits, with a depth parameterized by fp, the relative brightness of the planet's dayside hemisphere.
Since we are interested in temporal signals in the out-of-eclipse light curve, we do not use the detrended time series and instead multiply the phase curve model by generalized polynomials in time to capture all nonastrophysical time-dependent signals in the raw light curve, which are likely attributable to instrumental systematics. The raw light curves shown in Figures 3 and 4 display clear long-term temporal trends, as well as discontinuities in flux that occur during momentum dumps.
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Standard image High-resolution imageGiven these discontinuities, we split each light curve into small segments separated by momentum dumps and fit a separate polynomial systematics model to each segment. The orders of the polynomials used in the final fit are determined by first fitting each segment individually and minimizing the Bayesian information criterion (BIC), defined as , where k is the number of fitted parameters, and n is the number of data points. After optimizing the polynomial orders, we carry out a joint fit of the full light curve.
For HAT-P-70, we find that the nonastrophysical systematics in the segments are well described by polynomials of second to third order. In the joint fit, we report a marginal 2.4σ secondary eclipse detection of 159 ± 65 ppm, while the atmospheric brightness modulation amplitude is consistent with zero. Figure 4 shows the systematics-corrected and phase-folded light curve in the vicinity of the secondary eclipse, along with the best-fit model.
To evaluate the statistical significance of this HAT-P-70 b secondary eclipse detection, we compare the BIC of a joint fit that includes only transits and secondary eclipses (fixing B1 to zero) with the BIC of a fit that assumes a flat out-of-transit light curve (fixing B1 and fp to zero). The difference in BIC is less than 0.1, indicating that the secondary eclipse detection is not formally statistically robust. From an analogous analysis of the HAT-P-69 phase curve, we do not detect any significant secondary eclipse depth or phase curve signal.
2.1.3. Independent Identification by WASP
HAT-P-69 and HAT-P-70 were both independently identified as planet candidates by the WASP survey (Schanche et al. 2019). The northern facility (SuperWASP-North) and the southern facility (WASP-South) both consist of arrays of eight 200 mm f/1.8 Canon telephoto lenses on a common mount. Each camera is coupled with 2K × 2K detectors, yielding a field of view of 7.8 × 78 per camera (Pollacco et al. 2006). HAT-P-69 was observed by both WASP-South and SuperWASP-North, producing 25,200 photometric points spanning from 2009 January 14 to 2012 April 23. HAT-P-70 was observed by SuperWASP-North, producing 19,200 observations spanning 2008 October 13 to 2011 February 4. These long baseline observations are plotted in Figure 1 and were included in the global modeling (Section 3.2) to help refine the transit ephemeris.
2.1.4. Ground-based Follow-up Observations
A series of facilities provided follow-up photometry of HAT-P-69 and HAT-P-70 to confirm the transit signal, improve the determination of the planet radius, and increase the precision of the transit ephemeris. A number of transit observations were obtained with the FLWO 1.2 m telescope and KeplerCam, a 4K × 4K CCD camera operated with 2 × 2 binning, giving a plate scale of . Photometry was extracted as per Bakos et al. (2010). Follow-up photometry was also obtained using the Las Cumbres Observatory (LCO; Brown et al. 2013) network. These observations included transits obtained via the 0.8 m LCO telescope located at the Byrne Observatory at Sedgwick, California, using the SBIG STX-16803 camera with a field of view of . Observations were also obtained using the 1 m LCO telescope at Siding Spring Observatory, Australia, using the Sinistro Fairchild CCD, with a field of view of over the detector. Additional photometric follow-up was obtained using the TRAPPIST (TRAnsiting Planets and PlanetesImals Small Telescope) North facility (Jehin et al. 2011; Gillon et al. 2013; Barkaoui et al. 2019) at Oukaimeden Observatory in Morocco. TRAPPIST-North is a 0.6 m robotic photometer employing a CCD with a field of view of 198 × 198 at a plate scale of per pixel.
The dates, cadences, and filters used in these observations are summarized in Table 1. The light curves are made available in Tables 2 and 3 and shown in Figures 5 and 6.
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Standard image High-resolution imageTable 2. Differential Photometry of HAT-P-69
BJD | Mag (Raw)a | Mag (EPD) | Mag (TFA) | σ Mag | Instrument | Filter |
---|---|---|---|---|---|---|
2455502.9688207 | 9.12413 | 10.01248 | 10.00835 | 0.00161 | HATNet | r' |
2455502.9733846 | 9.11519 | 10.0089 | 10.00331 | 0.0016 | HATNet | r' |
2455502.9776452 | 9.11541 | 10.01343 | 10.00835 | 0.0016 | HATNet | r' |
2455502.9819047 | 9.12139 | 10.01393 | 10.01161 | 0.0016 | HATNet | r' |
2455502.9862569 | 9.10128 | 10.00651 | 9.99933 | 0.00159 | HATNet | r' |
Note.
aRaw, EPD, and TFA magnitudes are presented for HATNet light curves. The detrending and potential blending may cause the HATNet transit to be shallower than the true transit in the EPD and TFA light curves. This is accounted for in the global modeling by the inclusion of a dilution factor. Follow-up light curves have been treated with EPD simultaneous to the transit fitting. Pre-EPD magnitudes are presented for the follow-up light curves.Only a portion of this table is shown here to demonstrate its form and content. A machine-readable version of the full table is available.
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Table 3. Differential Photometry of HAT-P-70
BJD | Mag (Raw)a | Mag (EPD) | Mag (TFA) | σ Mag | Instrument | Filter |
---|---|---|---|---|---|---|
2455093.9914136 | 8.8238 | 9.69444 | 9.69370 | 0.00177 | HATNet | r' |
2455093.9939800 | 8.83789 | 9.69611 | 9.70024 | 0.00179 | HATNet | r' |
2455093.9966693 | 8.84903 | 9.67121 | 9.67967 | 0.0018 | HATNet | r' |
2455093.9993076 | 8.80637 | 9.70271 | 9.69896 | 0.00177 | HATNet | r' |
2455094.0019585 | 8.84992 | 9.69871 | 9.69148 | 0.00181 | HATNet | r' |
Note.
aRaw, EPD, and TFA magnitudes are presented for HATNet light curves. The detrending and potential blending may cause the HATNet transit to be shallower than the true transit in the EPD and TFA light curves. This is accounted for in the global modeling by the inclusion of a dilution factor. Follow-up light curves have been treated with EPD simultaneous to the transit fitting. Pre-EPD magnitudes are presented for the follow-up light curves.Only a portion of this table is shown here to demonstrate its form and content. A machine-readable version of the full table is available.
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2.2. Spectroscopy
We carried out a series of spectroscopic follow-up observations to confirm the nature of the transiting candidates, constrain the masses, and measure the orbital obliquities of the companions. The observations are listed in Table 4 and summarized below.
Table 4. Summary of Spectroscopic Observations
Target | Telescope/Instrument | Date Range | Number of Observations | Resolution | Observing Mode |
---|---|---|---|---|---|
HAT-P-69 | FLWO 1.5 m TRES | 2011 Oct 10–2017 Mar 14 | 45 | 44000 | RV |
HAT-P-69 | SALT HRS | 2015 Mar 6 | 11 | 40000 | Transit |
HAT-P-69 | FLWO 1.5 m TRES | 2017 Mar 8 | 18 | 44000 | Transit |
HAT-P-69 | FLWO 1.5 m TRES | 2017 Mar 13 | 17 | 44000 | Transit |
HAT-P-69 | FLWO 1.5 m TRES | 2019 Jan 12 | 22 | 44000 | Transit |
HAT-P-70 | FLWO 1.5 m TRES | 2013 Feb 1–2019 Feb 20 | 43 | 44000 | RV |
HAT-P-70 | FLWO 1.5 m TRES | 2019 Feb 21 | 19 | 44000 | Transit |
HAT-P-70 | FLWO 1.5 m TRES | 2019 Mar 4 | 19 | 44000 | Transit |
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The Tillinghast Reflector Echelle Spectrograph (TRES; Fűrész 2008) on the 1.5 m telescope at FLWO, Arizona, was used to obtain dozens of spectra for each system. TRES is a fiber-fed echelle spectrograph with a spectral resolution of R = 44,000 over the wavelength region of 3850–9100 Å. The observing strategy and data reduction process are described by Buchhave et al. (2012). Each spectrum is measured from the combination of three consecutive observations for optimal cosmic-ray rejection, and the wavelength solution is provided by bracketing ThAr hollow cathode lamp exposures. A series of TRES spectra were obtained at phase quadratures to most efficiently constrain the mass of the planets. For HAT-P-69, relative radial velocities were obtained using a multiorder analysis (Quinn et al. 2012) of the TRES spectra. For HAT-P-70, we modeled the stellar line profiles derived from a least-squares deconvolution (LSD; Donati et al. 1997) to derive the absolute radial velocities of each spectrum. In our experience with rapidly rotating stars, the best radial velocities are obtained by modeling of the LSD-derived line profiles. The TRES velocities for HAT-P-69 and HAT-P-70 are listed in Tables 5 and 6 and plotted in Figures 7 and 8, respectively.
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Standard image High-resolution imageTable 5. Relative Radial Velocities of HAT-P-69
BJD | Relative RVa | σ RV | Instrument |
---|---|---|---|
(UTC) | (km s−1) | (km s−1) | |
2455844.990516 | 1.437 | 0.433 | TRES |
2455889.044893 | 0.782 | 0.159 | TRES |
2455904.899944 | 0.763 | 0.200 | TRES |
2456399.650041 | 0.328 | 0.178 | TRES |
2456400.656614 | 0.462 | 0.151 | TRES |
2456403.681071 | 0.389 | 0.121 | TRES |
2456404.671615 | 0.138 | 0.134 | TRES |
2456409.670360 | 0.171 | 0.142 | TRES |
2456410.671948 | 0.293 | 0.140 | TRES |
2457819.604459 | 0.619 | 0.175 | TRES |
2457819.616148 | 0.473 | 0.155 | TRES |
2457819.627797 | 0.851 | 0.150 | TRES |
2457819.639439 | 0.482 | 0.121 | TRES |
2457819.651291 | 0.487 | 0.172 | TRES |
2457819.663107 | 0.587 | 0.135 | TRES |
2457819.675045 | 0.781 | 0.118 | TRES |
2457819.686914 | 0.753 | 0.125 | TRES |
2457819.698568 | 0.684 | 0.119 | TRES |
2457819.710512 | 0.728 | 0.107 | TRES |
2457819.723092 | 0.690 | 0.140 | TRES |
2457819.734747 | 0.487 | 0.129 | TRES |
2457819.746511 | 0.667 | 0.098 | TRES |
2457819.758189 | 0.733 | 0.134 | TRES |
2457819.770017 | 0.577 | 0.161 | TRES |
2457819.781718 | 0.593 | 0.122 | TRES |
2457819.793343 | 0.640 | 0.120 | TRES |
2457819.804946 | 0.364 | 0.152 | TRES |
2457819.816560 | 0.805 | 0.178 | TRES |
2457819.828173 | 0.847 | 0.156 | TRES |
2457820.675448 | 0.460 | 0.110 | TRES |
2457820.687137 | 0.732 | 0.099 | TRES |
2457820.698797 | 0.384 | 0.148 | TRES |
2457820.710475 | 0.553 | 0.148 | TRES |
2457820.722152 | 0.711 | 0.123 | TRES |
2457825.760728 | 0.264 | 0.123 | TRES |
2457825.772683 | 0.466 | 0.162 | TRES |
2457825.784297 | 0.350 | 0.146 | TRES |
2457825.795980 | 0.518 | 0.147 | TRES |
2457826.645765 | 0.480 | 0.157 | TRES |
2457826.657442 | 0.242 | 0.165 | TRES |
2457826.669091 | 0.062 | 0.161 | TRES |
2457826.682487 | 0.199 | 0.176 | TRES |
2457826.694234 | 0.194 | 0.110 | TRES |
2457826.705923 | 0.373 | 0.136 | TRES |
2457826.717589 | 0.292 | 0.225 | TRES |
2457826.729296 | 0.255 | 0.151 | TRES |
2457826.741008 | 0.199 | 0.103 | TRES |
2458495.942762 | 0.618 | 0.228 | TRES |
2458495.954736 | 0.642 | 0.330 | TRES |
2458495.966877 | 0.938 | 0.285 | TRES |
2458495.979216 | 0.286 | 0.303 | TRES |
2458495.991201 | 0.566 | 0.356 | TRES |
2458496.003036 | 0.810 | 0.192 | TRES |
Note.
aRelative radial velocities from a multiorder cross correlation. Internal errors excluding the component of astrophysical/instrumental jitter considered in Section 3. Velocities exclude those taken in transit.Download table as: ASCIITypeset image
Table 6. Relative Radial Velocities of HAT-P-70
BJD | RVa | σ RV | Instrument |
---|---|---|---|
(UTC) | (km s−1) | (km s−1) | |
2456324.697671 | 24.350 | 0.642 | TRES |
2456342.685872 | 24.867 | 0.616 | TRES |
2457671.822407 | 25.153 | 0.708 | TRES |
2457671.830590 | 25.270 | 0.620 | TRES |
2457671.838751 | 26.999 | 0.532 | TRES |
2457671.846928 | 25.516 | 0.639 | TRES |
2457671.855187 | 25.525 | 0.727 | TRES |
2457671.863348 | 25.804 | 1.043 | TRES |
2457671.871531 | 25.827 | 0.599 | TRES |
2457671.880432 | 26.936 | 0.676 | TRES |
2457671.892204 | 24.786 | 1.114 | TRES |
2457671.900399 | 24.601 | 0.688 | TRES |
2457671.908554 | 24.334 | 0.934 | TRES |
2457671.917565 | 24.760 | 0.634 | TRES |
2457671.925743 | 25.362 | 0.773 | TRES |
2457671.933920 | 24.981 | 0.663 | TRES |
2457671.942121 | 25.324 | 0.659 | TRES |
2457671.950363 | 26.011 | 0.538 | TRES |
2457671.958645 | 25.100 | 0.825 | TRES |
2457671.966979 | 25.709 | 0.726 | TRES |
2457671.975226 | 24.904 | 0.592 | TRES |
2457671.983427 | 25.817 | 0.819 | TRES |
2457671.991639 | 24.845 | 0.677 | TRES |
2457672.000309 | 25.261 | 0.536 | TRES |
2457672.008573 | 24.851 | 0.584 | TRES |
2457672.016844 | 25.661 | 0.839 | TRES |
2457672.025149 | 25.769 | 0.434 | TRES |
2458527.601110 | 25.803 | 1.160 | TRES |
2458531.776169 | 25.321 | 1.741 | TRES |
2458532.755301 | 24.806 | 0.962 | TRES |
2458534.591655 | 24.475 | 0.630 | TRES |
2458534.599808 | 25.870 | 0.654 | TRES |
2458534.607915 | 25.156 | 1.032 | TRES |
2458534.616045 | 24.988 | 0.569 | TRES |
2458534.624158 | 25.590 | 0.992 | TRES |
2458534.632322 | 24.981 | 0.916 | TRES |
2458534.640470 | 26.307 | 1.199 | TRES |
2458534.648681 | 24.680 | 0.628 | TRES |
2458534.656811 | 25.248 | 1.155 | TRES |
2458534.664964 | 24.315 | 0.761 | TRES |
2458534.673094 | 25.971 | 0.920 | TRES |
2458534.681230 | 24.672 | 0.470 | TRES |
2458534.689354 | 24.547 | 0.848 | TRES |
2458535.714170 | 24.300 | 0.508 | TRES |
2458535.722375 | 23.870 | 0.994 | TRES |
2458535.730499 | 24.905 | 2.141 | TRES |
2458535.738629 | 23.029 | 3.309 | TRES |
2458546.689854 | 25.441 | 0.382 | TRES |
2458546.698076 | 26.287 | 0.618 | TRES |
2458546.706287 | 24.819 | 0.922 | TRES |
2458546.714516 | 26.252 | 0.741 | TRES |
2458546.722686 | 23.979 | 0.821 | TRES |
2458546.730914 | 25.292 | 1.133 | TRES |
2458546.739183 | 25.804 | 1.377 | TRES |
Note.
aAbsolute velocities derived from the least-squares deconvolution profiles. Internal errors excluding the component of astrophysical/instrumental jitter considered in Section 3. Velocities exclude those taken in transit.Download table as: ASCIITypeset image
Spectroscopic observations were also obtained with TRES throughout the transits of each planet. These observations allow us to measure variations in the stellar line profile due to the partial obscuration of the photosphere of the rapidly rotating star (Collier Cameron et al. 2010). By measuring the planetary "shadow" on the line profile of the star, we confirm that the photometric transit signal is indeed caused by a small body that is transiting the bright, rapidly rotating target star, as opposed to being the diluted signal of a much fainter eclipsing binary that is spatially blended with the target star in the photometric aperture. The observing strategy and analysis largely follow the procedure laid out by Zhou et al. (2016). We observed three partial transits of HAT-P-69 on 2017 March 8 and 13 and 2019 January 12, with the Doppler shadow of the planet clearly detected in each individual transit (Figure 9). Two partial transits of HAT-P-70 were obtained on 2019 February 21 and March 4. Observations on 2019 February 21 were hampered by poor weather, but the subsequent transit on 2019 March 4 clearly revealed the planet shadow (Figure 10). These observations are used in the global analysis (Section 3.2) to derive the projected spin–orbit angle of the systems.
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Standard image High-resolution imageOne additional partial transit of HAT-P-69 b was obtained via the High Resolution Spectrograph (HRS; Crause et al. 2014) on the Southern African Large Telescope (SALT). HRS is a fiber-fed echelle spectrograph used in the medium resolution mode, yielding a spectral resolution of R = 40,000 over the wavelength region of 3700–5500 Å over the blue arm of the spectrograph. Observations from the red arm of the spectrograph were not used due to the fewer line counts over its spectral coverage. The observations were obtained covering the ingress of HAT-P-69 b on 2015 March 6, covering 11 spectra with integration times of 700 s each. The target star remained at an altitude of 47°–53° throughout the transit observations. The spectra were extracted and calibrated using the MIDAS pipeline (Kniazev et al. 2016, 2017). The spectral line profiles were extracted via a process similar to that described above. The average line profile is subtracted, leaving a significant detection of the planetary transit over ingress (Figure 9).
In addition, a number of spectroscopic resources contributed to the initial spectroscopic vetting of the targets. Observations of HAT-P-69 were obtained using the High Resolution Echelle Spectrometer on the 10 m Keck I at Maunakea Observatory. Observations were also obtained using the High Dispersion Spectrograph on the 8.2 m Subaru telescope on Maunakea Observatory. In both cases, observations were made using the iodine cell, but did not yield high-precision velocities due to the rapid rotation of the star. They were not included in the analysis. We also made use of the CHIRON instrument on the SMARTS 1.5 m telescope at Cerro Tololo Inter-American Observatory (CTIO), Chile (Tokovinin et al. 2013), obtaining four observations of HAT-P-70. Similarly, reconnaissance observations were obtained with the SOPHIE echelle facility on the 1.93 m Haute-Provence Observatory, France, as well as the CORALIE spectrograph on the 1.2 m Euler telescope at the ESO La Silla Observatory, Chile. Given that the TRES observations vastly outnumber these reconnaissance observations, we incorporate only the TRES data in our global modeling.
3. Analysis
3.1. Properties of the Host Star
Both HAT-P-69 and HAT-P-70 are classified as rapidly rotating A stars based on their Two Micron All Sky Survey (2MASS; Skrutskie et al. 2006) J − K colors and the reconnaissance spectra from TRES. Rapidly rotating stars have spectral lines that are blended and unresolved, making standard spectral classifications more difficult. In addition, the gravity-darkening effect causes the derived atmospheric parameters, such as effective temperature, to be dependent on our viewing angle. The same star would appear hotter when viewed pole-on and cooler when viewed along the equator. We adopt the approach described in Zhou et al. (2019) and match the spectral energy distribution (SED) of the star against a grid of synthetic magnitudes computed from the Geneva 2D rotational isochrones (Ekström et al. 2012) for a range of inclination angles. This is performed as part of the global modeling described in Section 3.2, as the transit light curve also contributes to constraining the inclination angle of the system.
The SEDs for both stars are shown in Figures 11 and 12. We find that both stars are late A dwarfs. HAT-P-69 has a mass of M⊙, radius of R⊙, and effective temperature of K. HAT-P-70 has a mass of M⊙, radius of R⊙, and effective temperature of K.
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Standard image High-resolution imageWe check this rotational SED analysis with an independent fit of the SEDs to Kurucz atmosphere models of nonrotating stars (Kurucz 1992). We find HAT-P-69 to have Teff = 7650 ± 400 K, R⋆ = 1.88 ± 0.19 R⊙, and reddening of A(v) = 0.01 ± 0.01. HAT-P-70 has Teff = 8400 ± 400 K, R⋆ = 2.08 ± 0.20 R⊙, with reddening of . For both stars, the nonrotational SED analysis agrees well with that from the global modeling detailed above.
As a check on the determination of the stellar parameters, we independently derived the effective temperature and metallicity of each star using the TRES spectra and the Stellar Parameter Classification pipeline (Buchhave et al. 2010). We find HAT-P-69 to have Teff = 7557 ± 52 K and [m/H] = +0.05 ± 0.08 dex, while HAT-P-70 has atmospheric parameters of Teff = 8246 ± 93 K and [m/H] = −0.06 ± 0.09 dex. The spectroscopic stellar parameters agree to within 1σ with those measured from the SED, though the uncertainties are likely underestimated. The rapid rotation of the star causes difficulties in continuum normalization of the spectra, making accurate spectroscopic determination of the stellar parameters and associated uncertainties more difficult. We incorporate the metallicity measurements from spectra as Gaussian priors in the global modeling. For a more accurate understanding of stellar properties, we simultaneously fit the SED with the transit and rotational stellar isochrones in our global modeling, instead of relying on the spectra-derived values.
An accurate measurement of the projected stellar rotation rate is crucial for interpreting the Doppler transit data, constraining the stellar gravity-darkening effect, and constraining the stellar oblateness. To measure the projected rotation velocity, we model the LSD spectral line profiles using a kernel that incorporates the effects of stellar rotation and radial-tangential macroturbulence via a numerical disk integration, and we model the instrument line broadening as a Gaussian convolution. We find HAT-P-69 to have and a macroturbulent velocity of . For HAT-P-70, the results are and .
3.2. Global Modeling of System Parameters
We perform a global analysis of the systems to model the large suite of observations available for HAT-P-69 and HAT-P-70. This global model simultaneously incorporates the photometric transit, radial velocities, stellar parameter constraints, Doppler transits, and the effect of photometric gravity darkening on the transit light curve and observed stellar properties.
Our modeling process largely follows that described by Zhou et al. (2019). Rapid rotation distorts the shapes of stars; they become oblate along the equator, causing the poles to be hotter and brighter, while the equator becomes cooler and darker (von Zeipel 1924). This gravity-darkening effect causes both the transit light curve (Barnes 2009) and the observed SED of the star (Brandt & Huang 2015) to depend on the viewing direction. The photometric transit is modeled using the simuTrans package from Herman et al. (2018), which accounts for both the gravity-darkened nonuniform brightness distribution of the stellar disk and the ellipsoidal nature of the rapidly rotating star. The stellar properties are inferred from the Geneva 2D rotational isochrones (Ekström et al. 2012), which incorporate the effects of rotation on stellar evolution and include prescriptions for the oblateness of the stars based on their rotation rates. In the case of an oblique transiting geometry about gravity-darkened stars, the resulting light curve often exhibits asymmetry because of the latitude dependence of the surface brightness distribution. This effect is detected for HAT-P-70 b and explored in greater depth in Section 3.4.
The limb-darkening coefficients are interpolated from the values of Claret & Bloemen (2011) and Claret (2017) for the Sloan and TESS bands. They are constrained by a Gaussian prior of width 0.02 during the global modeling, representing the difference in the limb-darkening coefficients should the stellar parameters be different by 1σ. To model the transit light curves, we adopt a gravity-darkening coefficient β from interferometric observations of Vega (β = 0.231 ± 0.028; Monnier et al. 2012). Similar interferometric gravity-darkening coefficients have been measured for other rapidly rotating A stars (e.g., α Cep β = 0.216 ± 0.021; Zhao et al. 2009). To account for the uncertainty in the gravity-darkening coefficient, it is modeled in the global fit as a free parameter constrained about the value and uncertainty of Vega reported in Monnier et al. (2012). The model fitting procedure also includes detrending of the ground-based follow-up light curves, via a linear combination of effects, including the pixel position of the target star, air mass, and background count values. We account for the 30 minute cadence of the TESS by supersampling and integrating the model over the exposure time.
The stellar parameters are constrained by the SED of the stars over the Tycho-2 (Høg et al. 2000), APASS (Henden et al. 2016), and 2MASS (Skrutskie et al. 2006) photometric bands, as well as the parallax from Gaia data release 2 (Gaia Collaboration et al. 2018). Local reddening is constrained by the maximum reddening value from the dust maps of Schlafly & Finkbeiner (2011), assuming Av = 3.1E(B − V). To account for the uncertainties in our deblending of the TESS light curves, we also include a TESS light curve dilution parameter, closely constrained by a Gaussian prior, with width derived from the reported uncertainties in the TESS band magnitudes of the target and nearby stars from TIC v6.
The Doppler transit signal is simultaneously modeled with the light curve and provides the best constraint on the projected spin–orbit angle λ for the orbital plane of the planets. We model variations of the stellar line profiles via a 2D integration of the rotating stellar surface being occulted by the transiting planet, incorporating the effects of differential limb darkening, radial-tangential macroturbulence, and instrument broadening.
To derive the best-fit system parameters and their associated uncertainties, we perform a Markov chain Monte Carlo analysis using the emcee package (Foreman-Mackey et al. 2013). The resulting stellar and planetary parameters are shown in Tables 7 and 8, respectively.
Table 7. Stellar Parameters
Parameter | HAT-P-69 | HAT-P-70 |
---|---|---|
Catalog Information | ||
TIC | 379929661 | 399870368 |
Tycho-2 | 0215-01594-1 | 0688-01684-1 |
Gaia DR2 | 3080104185367102592 | 3291455819447952768 |
Gaia R.A. (2015.5) | 08:42:01.353 | 04:58:12.560 |
Gaia Decl. (2015.5) | +03:42:38.038 | +09:59:52.726 |
Gaia μα (mas yr−1) | −2.856 ± 0.074 | −2.657 ± 0.096 |
Gaia μδ (mas yr−1) | 0.984 ± 0.051 | −4.996 ± 0.065 |
Gaia DR2 Parallax (mas) | 2.902 ± 0.043 | 2.996 ± 0.061 |
Stellar atmospheric propertiesa | ||
(K) | ||
() | ||
vmacro () | ||
Photometric properties | ||
TESS T (mag) | 9.612 ± 0.018 | 9.298 ± 0.019 |
Gaia G (mag) | 9.77216 ± 0.00035 | 9.45112 ± 0.00035 |
TYCHO B (mag) | 10.052 ± 0.061 | 9.621 ± 0.045 |
TYCHO V (mag) | 9.7740 ± 0.0050 | 9.4700 ± 0.0040 |
APASS g' (mag) | 9.796 ± 0.030 | 9.842 ± 0.351 |
APASS r' (mag) | 9.855 ± 0.041 | 9.506 ± 0.028 |
APASS i' (mag) | 9.976 ± 0.020 | 9.962 ± 0.061 |
2MASS J (mag) | 9.373 ± 0.024 | 9.068 ± 0.022 |
2MASS H (mag) | 9.293 ± 0.022 | 9.023 ± 0.029 |
2MASS Ks (mag) | 9.280 ± 0.023 | 8.963 ± 0.024 |
Stellar properties | ||
() | ||
() | ||
(cgs) | ||
() | ||
Stellar oblateness | ||
Line of sight inclination I* | ||
(mag)b | ||
Age (Gyr) | ||
Distance (pc) | 329.0 ± 6.5 |
Notes.
aDerived from the global modeling described in Section 3, coconstrained by spectroscopic stellar parameters and the Gaia DR2 parallax. bUniform prior for reddening up to the local maximum set by Schlafly & Finkbeiner (2011).Download table as: ASCIITypeset image
Table 8. Orbital and Planetary Parameters
Parameter | HAT-P-69 b | HAT-P-70 b |
---|---|---|
Light curve parameters | ||
P (days) | ||
Tc (BJD-TDB)a | ||
T14 (days)a | ||
/ | ||
i (deg) | ||
(deg) | ||
Limb-darkening and gravity-darkening coefficientsb | ||
(HAT) (linear term) | 0.1194 (fixed) | 0.1550 (fixed) |
(HAT) (quadratic term) | 0.3974 (fixed) | 0.3306 (fixed) |
aGC | ||
bGC | ||
aRc | ||
bRc | ||
aTESS | ||
bTESS | ||
β Gravity-darkening coefficient | ||
RV parameters | ||
K () | ||
e | 0 (fixed) | 0 (fixed) |
RV jitter () | ||
Systemic RV ()c | 784 ± 24 | 25260 ± 110 |
Planetary parameters | ||
() | ||
() | ||
() | ||
(cgs) | ||
a (au) | ||
(K)d |
Notes.
aTc: Reference epoch of midtransit that minimizes the correlation with the orbital period. T14: total transit duration, time between first and last contact. bValues for a quadratic law given separately for each of the filters with which photometric observations were obtained. These values were adopted from the tabulations by Claret & Bloemen (2011) according to the spectroscopic initial estimate of the stellar parameters. The limb-darkening coefficients are constrained by strong Gaussian priors of width 0.02 about their initial values. The gravity-darkening coefficient β is also constrained by a Gaussian prior of width 0.028 in the fit. cThe systemic RV for the system as measured relative to the telluric lines. dTeq calculated assuming zero albedo and full heat redistribution.Download table as: ASCIITypeset image
3.3. Blending and Astrophysical False-positive Scenarios
Many astrophysical scenarios can mimic the transit signal of a planetary system. False-positive scenarios such as M dwarf companions with radii similar to substellar counterparts are ruled out by the mass constraints imposed by our radial velocity measurements. The possibility that the transit signals are due to fainter eclipsing binaries whose eclipses are diluted by the brighter target stars is more difficult to eliminate. We adopt a number of observations, including diffraction-limited imaging and analysis of the spectroscopic transit, to eliminate this possibility.
To rule out spatially nearby companions, we obtained observations with the NN-explore Exoplanet Stellar Speckle Imager (NESSI; Scott et al. 2018) on the 3.5 m WIYN telescope at Kitt Peak National Observatory, Arizona. Speckle imaging gives a resolution of ≳004 in both the r-narrow and z-narrow bands for both HAT-P-69 and HAT-P-70, corresponding to spatial scales as close to the stars as 14–22 au (at 562 nm and 832 nm, respectively). The corresponding constraints from NESSI are plotted in Figure 13. In addition, we obtained J- and Ks-band infrared seeing limited imaging HAT-P-69 with the WIYN High-Resolution Infrared Camera (WHIRC; Smee et al. 2011), also finding no visual companions to the target star.
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Standard image High-resolution imageFinally, the Doppler detection of the planetary transit confirms that the transits indeed occur around the rapidly rotating, bright A star hosts, not background stars (e.g., Collier Cameron et al. 2010). The depth of the spectroscopic shadow agrees with the depth observed in the photometric light curves, suggesting that the dilution due to background sources is negligible.
3.4. Detection of an Asymmetric Gravity-darkened Transit for HAT-P-70
A transiting planet crossing a gravity-darkened stellar disk may exhibit an asymmetric transit when the projected spin–orbit angle is misaligned with the stellar rotation axis. The effects specific to gravity darkening are only visible at the parts-per-thousand level, and as such they are difficult to detect with ground-based data. The only previous confirmed instance of asymmetric gravity darkening being observed for a planetary system is for Kepler-13. The asymmetric transit light curves of Kepler-13 were identified and modeled by Szabó et al. (2011), Barnes et al. (2011), and Herman et al. (2018). Subsequent ground-based Doppler transit confirmation of the spin–orbit misalignment was performed by Johnson et al. (2014) and an eventual joint light curve and spectroscopic transit model developed by Masuda (2015).
The TESS light curves of HAT-P-70 exhibit asymmetric transits similar to those seen for Kepler-13. The transit is shallower at ingress and deeper near egress, indicating that the planet traverses a stellar surface that is darker near ingress and brighter near egress. Our global model reproduces such a transit, with the projected spin–orbit misaligned at ◦ and the stellar pole inclined to the line of sight by ◦ degrees. Figure 14 shows the TESS transit light curve, with the best-fit standard and gravity-darkened transit models overplotted. An asymmetry at the 500 ppm level can be seen in the residuals to the standard transit model, akin to that seen for Kepler-13.
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Standard image High-resolution imageWe note that we make use of the bolometric gravity-darkening coefficient β in our light curve modeling. Improvements can be made via a more careful treatment for the band dependence of the gravity-darkening effect (e.g., Espinosa Lara & Rieutord 2011). We note, though, that running the global modeling while allowing β to be free reproduces the same projected obliquity λ value to within uncertainties, and as such the actual adopted gravity-darkening coefficient is not critical to the modeling.
4. Occurrence Rate of Hot Jupiters from TESS
Although hot Jupiters were some of the earliest exoplanets to be discovered, they are not intrinsically common. Radial velocity searches from the Keck, Lick, and Anglo Australia Telescope programs of 1330 FGK stars revealed a hot Jupiter occurrence rate of 1.2 ± 0.2% (<15 MJup, <0.1 au; Marcy et al. 2005), revised to 1.20 ± 0.38% (>0.1 MJup, P < 10 days) by Wright et al. (2012) using the California Planet Search sample. Cumming et al. (2008) found an occurrence rate of 1.5 ± 0.6% (>0.3 MJup, <0.1 au) using the Keck planet search sample. Using the HARPS and CORALIE samples, Mayor et al. (2011) found a hot Jupiter occurrence rate of 0.89 ± 0.36% (>0.15 MJup, <11 days).
These radial velocity occurrence rates are generally thought to be higher than those offered by the Kepler survey. Studies by Howard et al. (2012) and Fressin et al. (2013) of the early Kepler data found rates of 0.4 ± 0.1% and 0.43 ± 0.05% for hot Jupiters, respectively. Recent analyses with improved stellar properties from Petigura et al. (2018) found that of main-sequence FGK stars (, K) host hot Jupiters. The measured giant planet occurrence rate from the CoRoT mission is higher than that from Kepler, finding 21 giant planets within 10 day period orbits, corresponding to an occurrence rate of 0.98 ± 0.26% (Deleuil et al. 2018).
The stars that host hot Jupiters are more metal-rich than random stars of the same spectral class (Santos et al. 2003; Valenti & Fischer 2005; Buchhave et al. 2012; Petigura et al. 2018). Differences between the metallicity distribution of the Kepler stellar sample and those of the radial velocity surveys have been raised as an explanation for the differences in the hot Jupiter occurrence rates (Wright et al. 2012), although Guo et al. (2017) showed that there is minimal difference between the Kepler field star metallicity distribution and that of the California Planet Search sample. Wang et al. (2015) offered a correction for the Kepler sample based on an improved classification of the subgiant population. They suggested that multiplicity or a lower occurrence rate of hot Jupiters around subgiants may be the cause of the disagreement. Later, Bouma et al. (2018) showed that binarity is unlikely to be responsible for any disagreements between the Doppler and Kepler samples.
A radial velocity survey of intermediate-mass subgiants has shown that higher mass stars tend to host more gas giant planets within a few astronomical units (e.g., Johnson et al. 2010; Jones et al. 2014; Reffert et al. 2015; Ghezzi et al. 2018), though caveats regarding the accuracy of the mass measurements of these evolved stars should be noted (e.g., Lloyd 2013; Schlaufman & Winn 2013; Stello et al. 2017). The giant planets around subgiants tend to be found in orbits beyond 0.1 au; there appears to be a paucity of hot Jupiters around evolved stars. These studies suggest that hot Jupiters undergo tidal orbital decay when a star begins evolving into a subgiant (Schlaufman & Winn 2013). The planets around these "retired A stars" tend to be in longer period and more circular orbits than those found around main-sequence stars (Jones et al. 2014), although recent discoveries have unveiled numerous hot Jupiters in close-in orbits about evolved stars (Grunblatt et al. 2018). These issues inspired us to look into the hot Jupiter occurrence rate around main-sequence A stars.
In this section, we aim to examine the hot Jupiter occurrence rate via the TESS stellar population, with two key differences from the previous works from Kepler:
- 1.The TESS stellar population encompasses bright stars covering one-quarter of the sky. This sample is a significantly closer (150 pc for a solar-type main-sequence star) population than that from Kepler. The TESS sample is a closer match to the radial velocity sample of bright nearby stars and should provide another test for any tension in the occurrence rates derived by the two techniques.
- 2.The TESS sample spans A, F, and G main-sequence stars. By comparing the planet distribution around A and FG samples, we can determine if the paucity of close-in planets around "retired A stars" is due to post-main-sequence stellar evolution. More broadly, we can test whether the occurrence rates of hot Jupiters change with stellar mass.
4.1. Main-sequence Sample
We restricted our study to main-sequence stars. We did not wish to consider evolved stars because of the problems with selection biases, shallower transit depths, and lack of substantial follow-up observations. We do note, though, that more than half of the TESS stars brighter than 10th magnitude are evolved. Eventually, this will be a rich hunting ground (e.g., Huber et al. 2019; Rodriguez et al. 2019).
Figure 15 shows the color–magnitude diagram (CMD) of the 120,000 stars brighter than Tmag = 10 that were observed by TESS. The BP − RP and G values are taken from a cross match against the Gaia DR2 catalog (Gaia Collaboration et al. 2018). To define the main sequence, we make use of the colors and magnitudes from the MESA Isochrones and Stellar Tracks (MIST; Dotter 2016). We draw an upper and a lower boundary in the versus G diagram based on the zero-age main sequence (ZAMS) and the terminal-age main sequence (TAMS) points in the solar-metallicity MIST evolution tracks. As per Dotter (2016), the ZAMS is defined by the criterion that the core hydrogen luminosity of the star is 99.9% of the total core luminosity, while the TAMS is defined by the criterion that the core hydrogen fraction has fallen below 10−12. The ZAMS and TAMS boundaries are plotted in Figure 15. Between these boundaries, we are left with 47,126 main-sequence stars for this study.
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Standard image High-resolution imageThe restriction to stars with Tmag < 10 allows us to make use of the TOI catalog available to the TESS follow-up community, which is essentially complete for hot Jupiters. The planet candidates around fainter stars in the FFIs are not fully vetted. We also restrict attention to the data from Sectors 1–7 because the candidates derived from later sectors have not yet received sufficient follow-up observations at the time of writing.
To check our CMD-derived stellar parameters and to estimate the metallicity of the population, we cross-match our field stellar population against the TESS-HERMES DR1 spectroscopic parameters for stars in the TESS southern continuous viewing zone (Sharma et al. 2018). Because the initial data release is restricted to stars within 10 < V < 13.1, we expect a very limited number of matches. We find 491 stars to have stellar parameters from TESS-HERMES within our sample, of which 301 have rotational broadening velocities . Figure 16 shows a comparison between our stellar effective temperature, surface gravity, and stellar mass against the spectroscopically measured values from TESS-HERMES.
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Standard image High-resolution imageThe median absolute deviations (MADs) between CMD and spectroscopic parameters are 60 K in Teff, 0.09 dex in log g, and 0.09 M⊙ in mass. However, we notice a systematic offset in our effective temperature and mass estimates for cool stars (dotted line in Figure 16). We correct for this bias by fitting for a polynomial correction to our parameters as follows for temperature:
for stars with 4000 < Teff,CMD < 6120 K. We also apply a correction in mass:
for . Postcorrection, we find that MADs between CMD and spectroscopic parameters are 40 K in Teff and 0.08 M⊙ in mass. Figure 17 shows the properties of the stellar population included in our sample. The sample is grouped into mass bins roughly corresponding to the A (1.4–2.3 M⊙), F (1.05–1.4 M⊙), and G (0.8–1.05 M⊙) spectral types. We elaborate on the occurrence rates of planets within each mass bin in Sections 4.3 and 4.4.
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Standard image High-resolution imageIn particular, the metallicity distributions of the 301 stars with TESS-HERMES measurements are plotted. We note that the population has near-solar metallicity of [Fe/H] = −0.06 ± 0.21. When subdivided into the mass bins, we find the G star bin to have [Fe/H] = −0.03 ± 0.20, F stars to have [Fe/H] = −0.13 ± 0.19, and A stars to have [Fe/H] = −0.26 ± 0.15. We note that when subdivided into their mass bins, the number of stars per bin becomes very small and may not be representative of the population. We look forward to further fields of the TESS-HERMES being completed, as well as similar surveys of brighter stars, to allow a better examination of the dependence between metallicity and the TESS planet properties.
4.2. Candidate Identification
Our planet sample makes use of the candidates (TOIs) released by the TESS Science office from the first seven sectors of TESS data around stars brighter than Tmag = 10. The TOIs are selected from a list of threshold crossing events (TCEs) by human vetters. A TCE requires the signal-to-noise ratio of the planet to be above 7.3 and that at least two transits are detected in the light curve. The human vetters reject some false positives based on standard diagnostics. For example, these may include large secondary eclipse/phase variation detections that indicate the eclipsing object is of stellar nature, an obvious centroid offset detection that indicates the eclipsing events happened on a background object, or significant depth variation with the choice of photometric aperture. We also cross-reference the TCEs with known false positive/eclipsing binary catalogs (Triaud et al. 2017; Collins et al. 2018). Although the initial TOIs were generated from two different sources (the 2 minute and the 30 minute data), for uniformity we ensured that all of the TOIs we used in this work are detected as TCEs through the quick-look pipeline, and that all of the TCEs detected by the quick-look pipeline around stars brighter than Tmag = 10 mag went through the TOI process.
We define our hot Jupiter candidates as TOIs with an orbital period between 0.9 and 10 days, a radius between 0.8 and 2.5 RJup, and a transit impact parameter smaller than 0.9. The period lower bound of 0.9 days was adopted to incorporate WASP-18b (Hellier et al. 2009), the shortest-period known hot Jupiter within TESS sectors 1–7 (Shporer et al. 2019), into our sample. A similar minimum period cut-off was also employed by Howard et al. (2012; 0.7 days) and Fressin et al. (2013; 0.8 days). We also note that no hot Jupiter candidates were found with periods <0.9 days within our sample. To ensure a clean sample, we also require candidates to have a signal-to-noise ratio (S/N) larger than 10, although, in practice, none of the giant planet candidates have an S/N between 10 and the traditional value of 7.3. We use the stellar radii interpolated from the Gaia CMD (Section 4.1) to recompute the radius of the planet during the selection.
4.3. Completeness and Signal-to-noise Ratio Estimates
Since the expected noise floor for a typical TESS star at Tmag = 10 per 1 hr is 200 ppm (Huang et al. 2018), any giant planet transiting a main-sequence star in our sample should be detected with a high S/N. However, some stars may exhibit large amplitude and short timescale stellar variability, such as stars on the instability strip of the CMD. Strong stellar variability can reduce the sensitivity to transit signals. To estimate our completeness rate more accurately, we measured the per-point MAD σmad of detrended/deblended light curves for all of the 47,126 stars used in this paper, derived from the FFIs using the quick-look pipeline. A factor of 1.48 is applied to σmad such that it approximates the standard deviation scatter of the light curves. The S/N of the candidates is then estimated with
where δ is the approximate transit depth, Tdur is the full transit duration in hours, and Ntr is the number of transits that appeared in the data from TESS Sectors 1–7.50 We assume any planet with a calculated S/N exceeding 10 was selected as a candidate, and otherwise was not selected. We also assume that the hot Jupiters exhibit a uniform distribution in transit impact parameter between 0 and 0.9. Figure 18 shows the survey completeness for a Jupiter-sized planet with an impact parameter of 0.45, for both 3 and 10 day orbits. The transit duration is calculated under the assumption of a circular orbit. While this assumption may not be valid for planets with periods approaching 10 days, it has been shown that modestly eccentric orbits have a negligible effect on survey completeness (Burke 2008).
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Standard image High-resolution image4.4. Results
A total of 47,126 stars and 31 TOIs are included in the occurrence rate calculation. The TOIs are composed of 18 confirmed planets, 3 planet candidates, and 10 false positives. The lists of planets, candidates, and false positives are given in the Appendix. To summarize the previous sections, the stellar and planet population is defined within the criteria below:
- 1.Brighter than Tmag = 10.
- 2.Lying within the solar-metallicity ZAMS and TAMS boundaries on the Gaia versus G CMD, and thereby classified as main sequence.
- 3.Planets are detected with BLS S/N > 10 and passed the vetting process.
- 4.Planets with periods 0.9 ≤ P ≤ 10 days.
- 5.Planets with radii 0.8 ≤ Rp ≤ 2.5 RJup.
- 6.Transits with impact parameter b < 0.9 to avoid grazing transits.
Within this stellar sample, the population is binned by stellar mass into A (1.4–2.3 M⊙), F (1.05–1.4 M⊙), and G (0.8–1.05 M⊙) spectral types. We estimate the occurrence rate f within each stellar mass bin as the conjugate distribution of the binomial distribution (i.e., the beta distribution):
in which nobs is the number of transiting planets observed in the mass bin, and ntrial is the effective number of times we try to conduct the detection of those transiting planets after accounting for transit probability and completeness. Specifically,
where wi is a weight indicating the probability that a planet/candidate falls within a particular mass bin. The probability distribution for the mass of each planet/candidate host star is modeled as a Gaussian distribution centered on the estimated mass, with a dispersion equal to 10% of the value of the estimated mass. The false-positive rate FP is estimated in each stellar mass bin using current follow-up results and is only applied to the active candidates. For the confirmed planets, the false-positive rate is set equal to zero. The false-positive rate is applied only to the active planet candidates, while FP = 0 for confirmed planets. The false-positive rate is calculated per stellar mass bin as
Based on the photometric and spectroscopic observations that have been performed so far by the TESS follow-up program, we find a false-positive rate of 15% for G stars, 41% for F stars, and 47% for A stars. Globally, the false-positive rate for hot Jupiters from TESS within our sample is 35%. Similar false-positive rates for short-period giant planets (29.3%) were reported by Fressin et al. (2013) for the initial Kepler candidates. The uncertainty assumes Poisson errors based on the number of planet candidates and false positives surveyed so far.
We define ntrial as
in which n* is the total number of observed stars falling in a particular mass bin, and and are the probability of a planet with period P and radius R transiting and being detected around star i, respectively. The transit probability for a planet with period P around a star with radius ri and mass mi is
The coefficient of 0.9 is present because we only consider planets and candidates with impact parameters smaller than 0.9. The probability of detection for each star is estimated following Section 4.3, assuming any planet with S/N ≤ 10 has been detected. The final integration is computed using a Monte Carlo method assuming that the intrinsic period distribution of a planet is uniform within the range from 0.9 to 10 days and the radius distribution of a planet is uniform within the range from 0.8 to 1.5 RJup.
Figure 19 summarizes the planet sample, search completeness, and field star population within each spectral-class mass bin.
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Standard image High-resolution imageThis planet and host star sample yields a total hot Jupiter occurrence rate from TESS of 0.41 ± 0.10%. Within each mass bin, we find an occurrence rate of 0.71 ± 0.31% for main-sequence G stars, 0.43 ± 0.15% for F stars, and 0.26 ± 0.11% for A stars. These occurrence rates are presented in Figure 20.
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Standard image High-resolution imageIn this analysis, we defined the main sequence as being bound within the solar-metallicity ZAMS and TAMS lines. The actual population should exhibit a dispersion in metallicity, with the effect of stars being brighter at higher metallicity for the same evolutionary state, and vice versa for lower metallicity stars. To test the effect of a more blurred main-sequence boundary, we reperformed the analysis while assuming a [Fe/H] = −0.27 ZAMS boundary and a [Fe/H] = +0.15 TAMS boundary, encompassing the 1σ dispersion in metallicity seen in our cross-matched TESS-HERMES stars. The resulting main-sequence sample increased to 52,788 stars and included two additional confirmed planets around F stars, two new candidates about G stars, one new candidate around an F star, and one new candidate around an A star. The net result is no significant change in the occurrence rates within each mass bin, nor any significant change for the whole sample.
Some caution may be necessary when directly comparing our occurrence rate against that derived from Kepler data. Our stellar sample is restricted to the main-sequence stars, while the Kepler sample may contain more evolved stars (Wang et al. 2015). Our definition of the main sequence is also different from more traditional definitions, which are based on surface gravity. We do not impose a surface gravity criterion because stars on the main sequence have different surface gravities at different masses: an intermediate-age main-sequence K star has log g ≈ 4.5, while A stars have log g ≈ 3.8 at the same evolutionary stage. Some previous works required log g < 3.9 or 4.0 to define the main sequence, which may remove 10%–30% of the main-sequence population in the range 6000 < Teff < 6500 K (e.g., Howard et al. 2012; Petigura et al. 2018). We find that if we apply a limit of log g < 4.0 to our sample, we increase the occurrence rates of hot Jupiters around F and A stars by nearly a factor of 2.
Although TESS is largely complete for hot Jupiters around F and G stars, the sensitivity is poorer for more evolved early A stars, for which the stellar radius can be as large as 4 R⊙. To check the dependence of our results on the completeness calculations, we tried drawing a boundary around smaller-radius A stars (defined by the boundary between −0.1 < BP − RP < 0.5 and G > GZAMS − 1.0). For stars within this boundary, the completeness is 80% for hot Jupiters with a period of 10 days. All of the confirmed cases of hot Jupiters around A stars that were used in our preceding calculations also reside within this more restricted sample. We find no significant difference (<1σ) in the occurrence rates presented above and those obtained within this "near-complete" box.
Unrecognized binaries in the main-sequence population can cause systematic errors in occurrence rate estimates. Bouma et al. (2018) found that systematic biases due to binarity may be important for small planets, but for Kepler hot Jupiters the bias is only at the level of ∼5%, smaller than our current uncertainties. Our occurrence rates were also obtained for a main sequence defined between the ZAMS and TAMS boundaries, which has the effect of removing some binaries because they appear overluminous. In testing for the effect of metallicity on our occurrence rates, we shifted the ZAMS and TAMS boundaries, but found minimal effect on the resulting occurrence rates.
A number of caveats still exist. The number of hot Jupiters around bright stars to be identified or recovered by TESS over the course of its mission will be at least four times that presented in this paper. We expect these occurrence rates and false-positive rates to be revised over the course of the mission. In particular, the majority of new hot Jupiters from TESS should be around intermediate-mass stars; the ground-based transit surveys are the least complete, and the hot Jupiter follow-up effort is most expensive within this regime. The uncertainties in our occurrence rates are currently dominated by Poisson statistics.
5. Conclusions
5.1. Agreement of TESS and Kepler Hot Jupiter Occurrence Rates
We find good agreement between the occurrence rates of hot Jupiters derived from the TESS and Kepler surveys. The occurrence rate from TESS is 0.41 ± 0.10%. From Kepler, various studies have found occurrence rates of 0.4 ± 0.1% (Howard et al. 2012), 0.43 ± 0.05% (Fressin et al. 2013), (Petigura et al. 2018), and (Masuda & Winn 2017).
The number of stars and planets within the TESS sample is already comparable to that from the Kepler sample and will soon grow. We make use of 47,126 stars and 18 planets and 3 active candidates. Previously determined occurrence rates of hot Jupiters were computed from 24 planet candidates around 58,000 stars by Howard et al. (2012), and out of 14 planets around 37,000 stars by Petigura et al. (2018). The light curve precision that TESS provides for these bright stars is also comparable to that for the relatively fainter stars from the Kepler sample.
Our initial estimates of the sample metallicity, derived from a cross match of the bright TESS stars against the TESS-HERMES (Sharma et al. 2018) catalog, suggest that our sample ([Fe/H] = −0.06 ± 0.21) is similar to that of Kepler (−0.045 ± 0.009; Guo et al. 2017). Future southern spectroscopic surveys of bright stars will continue to improve our understanding of the properties of field stars surveyed by TESS.
The average solar-type star from this TESS sample is located at 150 pc, while that observed by Kepler would be located at 400 pc (Mathur et al. 2017). Past surveys of more distant fields around the galactic bulge and disk (Gould et al. 2006; Bayliss & Sackett 2011) also found occurrence rates of hot Jupiters to be compatible with the rates derived from Kepler and TESS data, suggesting that there is not too much variety in the occurrence of hot Jupiters across the Galaxy.
We also remark on the near-completeness of the ground-based surveys. Of the 18 confirmed hot Jupiters within our sample, 13 were already discovered by the WASP (Pollacco et al. 2006), HATNet (Bakos et al. 2004), and KELT (Pepper et al. 2012) consortiums. Future studies of hot Jupiter properties from TESS will continue to capitalize on the follow-up efforts already made by these surveys.
5.2. No Evident Dependence on Stellar Mass
The occurrence rates of hot Jupiters within our A, F, and G mass bins agree with each other to within 1σ. Hot Jupiters are just as abundant around main-sequence A stars as they are around F and G stars. Radial velocity surveys have reported a paucity of giant planets in close-in orbits about "retired A stars." Together this seems to support the conclusion that enhanced tidal dissipation within evolved stars accelerates the process of tidal orbital decay of hot Jupiters (Schlaufman & Winn 2013). Post-main-sequence tidal evolution may be strongly dependent on the mass of the planets (e.g., Villaver & Livio 2009; Villaver et al. 2014), and more stringent constraints on the distribution of these main-sequence close-in giant planets may help yield additional clues into the tidal model for hot Jupiters. We note, though, that sample sizes of the Doppler surveys ranged from 166 stars (Jones et al. 2014) to 373 stars (Reffert et al. 2015), small enough that one should only expect ∼1 hot Jupiter to be found even if stellar evolution has no effect on the hot Jupiter occurrence rate. The Doppler surveys also noted an enhanced planet fraction for longer-period gas giants about more massive stars. Ghezzi et al. (2018) notes a 2× increase in planet fraction about 2 M⊙ stars compared to solar-mass stars, while Johnson et al. (2010) noted a nearly 3× increase in the planet fraction within the 1–2 M⊙ host mass range. Curiously, the hot Jupiter occurrence rate does not reflect this trend. Hot Jupiters are no more abundant about A stars than they are about F and G stars. Since the planets around early-type stars exhibit a wide distribution of obliquity angles (Albrecht et al. 2012), this may point to a lack of stellar mass preference for the dynamical migration of hot Jupiters.
We thank the referee for their careful reading of the manuscript; the comments significantly improved the quality of the paper. Work by G.Z. is provided by NASA through Hubble Fellowship grant HST-HF2-51402.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA under contract NAS 5-26555. HATNet operations have been funded by NASA grants NNG04GN74G and NNX13AJ15G. Follow-up of HATNet targets has been partially supported through NSF grant AST-1108686. G.Á.B., Z.C., and K.P. acknowledge partial support from NASA grant NNX09AB29G. J.H. acknowledges support from NASA grant NNX14AE87G. K.P. acknowledges support from NASA grants 80NSSC18K1009 and NNX17AB94G. We acknowledge partial support also from the Kepler Mission under NASA Cooperative Agreement NNX13AB58A (D.W.L., PI). Data presented in this paper are based on observations obtained at the HAT station at the Submillimeter Array of SAO, and the HAT station at the Fred Lawrence Whipple Observatory of SAO. This research has made use of the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program. Observations on the LCO 1 m network were made through NOAO program 2017B-0039, and time on the WIYN 3.5 m was awarded through NOAO programs 2016B-0078 and 2017A-0125. Observations on the SMARTS 1.5 m CHIRON facility were made through the NOAO program 2019A-0004. M.G. and E.J. are FNRS Senior Research Associates. The research leading to these results has received funding from the grant for Concerted Research Actions, financed by the Wallonia-Brussels Federation. Funding for the TESS mission is provided by NASA's Science Mission directorate. We acknowledge the use of public TESS Alert data from pipelines at the TESS Science Office and at the TESS Science Processing Operations Center. This research has made use of the Exoplanet Follow-up Observation Program website, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program. This paper includes data collected by the TESS mission, which are publicly available from the Mikulski Archive for Space Telescopes (MAST). G.K. is grateful for the support from the National Research, Development and Innovation Office (grant K 129249). G.K. is grateful for the support from the National Research, Development and Innovation Office (grant K 129249). The observations by the CHIRON spectrograph have been supported by the grant of the Hungarian Scientific Research Fund (OTKA, K-81373) to G.K. WASP-South is hosted by the South African Astronomical Observatory, and we are grateful for their ongoing support and assistance. Funding for WASP comes from consortium universities and from the UK's Science and Technology Facilities Council. Work by J.N.W. was supported by the Heising-Simons Foundation. This work is partly supported by JSPS KAKENHI grant Nos. JP18H01265 and JP18H05439, and JST PRESTO grant No. JPMJPR1775. Observations in the paper made use of the NN-EXPLORE Exoplanet and Stellar Speckle Imager (NESSI). NESSI was funded by the NASA Exoplanet Exploration Program and the NASA Ames Research Center. NESSI was built at the Ames Research Center by Steve B. Howell, Nic Scott, Elliott P. Horch, and Emmett Quigley. I.W. is supported by a Heising-Simons 51 Pegasi b postdoctoral fellowship. A. K. acknowledges support from the National Research Foundation (NRF) of South Africa. Some of the observations reported in this paper were obtained with the Southern African Large Telescope (SALT).
Facilities: HATNet - , FLWO 1.5 m - , CTIO:1.5m, TESS - , SALT. -
Software: lightkurve (Barentsen et al. 2019), emcee (Foreman-Mackey et al. 2013), simultrans (Herman et al. 2018), Astropy (Astropy Collaboration et al. 2013, 2018), fitsh (Pál 2012).
Appendix: Planets and Planet Candidate
We tabulate here all TOIs that made up the numerator of our occurrence rate calculation. Table 9 presents the confirmed planets, Table 10 shows the planet candidates and their follow-up stats, Table 11 shows the false positives, and Table 12 shows the confirmed giant planets orbiting stars Tmag < 10 that were not included in the sample due to the evolved states of their host stars. The planet and candidate lists are up-to-date as of 2019 June and can be accessed via tev.mit.edu.
Table 9. Confirmed Planets with
TIC | TOI | Name | Statusa | Period | Depth | Gaia G | Gaia BP | Gaia RP | Distance | Teffb | M⋆ | R⋆ | References |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
(days) | (ppm) | (mag) | (mag) | (mag) | (pc) | (K) | (M⊙) | (R⊙) | |||||
1129033 | 398.01 | WASP-77A | P | 1.4 | 16380 | 10.1 | 10.48 | 9.59 | 105 | 5433 | 0.91 | 0.98 | Maxted et al. (2013) |
25375553 | 143.01 | WASP-111 | P | 2.3 | 6939 | 10.11 | 10.36 | 9.73 | 300 | 6305 | 1.3 | 2.03 | Anderson et al. (2014) |
47911178 | 471.01 | WASP-101 | P | 3.6 | 12321 | 10.14 | 10.41 | 9.75 | 202 | 6209 | 1.16 | 1.39 | Hellier et al. (2014) |
65412605 | 626.01 | KELT-25 | P | 4.4 | 5812 | 9.6 | 9.68 | 9.47 | 442 | 7983 | 1.92 | 2.39 | R. Rodriguez et al. (2019, in preparation) |
92352620 | 107.01 | WASP-94A | P | 4.0 | 12999 | 10.03 | 10.33 | 9.6 | 212 | 5949 | 1.16 | 1.67 | Neveu-VanMalle et al. (2014) |
100100827 | 185.01 | WASP-18 | P | 0.9 | 10692 | 9.17 | 9.43 | 8.79 | 123 | 6291 | 1.16 | 1.3 | Hellier et al. (2009) |
144065872 | 105.01 | WASP-95 | P | 2.2 | 11836 | 9.94 | 10.29 | 9.46 | 138 | 5627 | 0.99 | 1.28 | Hellier et al. (2014) |
149603524 | 102.01 | WASP-62 | P | 4.4 | 14034 | 10.07 | 10.36 | 9.67 | 176 | 6123 | 1.13 | 1.29 | Hellier et al. (2012) |
166836920 | 267.01 | WASP-99 | P | 5.8 | 5386 | 9.33 | 9.64 | 8.89 | 159 | 5894 | 1.16 | 1.77 | Hellier et al. (2014) |
170634116 | 413.01 | WASP-79 | P | 3.7 | 12455 | 9.97 | 10.2 | 9.63 | 248 | 6571 | 1.38 | 1.65 | Smalley et al. (2012) |
183532609 | 191.01 | WASP-8 | P | 8.2 | 15535 | 9.61 | 10.0 | 9.11 | 90 | 5455 | 0.89 | 1.04 | Queloz et al. (2010) |
201248411 | 129.01 | ⋯ | P | 1.0 | 7028 | 10.59 | 11.23 | 9.85 | 61 | 4216 | 0.5 | 0.81 | L. Nielsen et al. (2019, in preparation) |
230982885 | 195.01 | WASP-97 | P | 2.1 | 13510 | 10.42 | 10.79 | 9.92 | 151 | 5526 | 0.9 | 1.17 | Hellier et al. (2014) |
267263253 | 135.01 | ⋯ | P | 4.1 | 10068 | 9.52 | 9.75 | 9.18 | 197 | 6538 | 1.37 | 1.63 | Jones et al. (2019) |
379929661 | 625.01 | HAT-P-69 | P | 4.8 | 7627 | 9.77 | 9.9 | 9.58 | 344 | 7532 | 1.68 | 1.92 | This work |
399870368 | 624.01 | HAT-P-70 | P | 2.7 | 8443 | 9.45 | 9.55 | 9.29 | 333 | 7818 | 1.77 | 2.03 | This work |
425206121 | 508.01 | KELT-19A | P | 4.6 | 10364 | 9.86 | 10.0 | 9.6 | 302 | 7188 | 1.52 | 1.77 | Siverd et al. (2018) |
455135327 | 490.01 | HAT-P-30 | P | 2.8 | 10758 | 10.3 | 10.58 | 9.89 | 215 | 6116 | 1.12 | 1.42 | Johnson et al. (2011) |
Notes.
aP: Confirmed planet. bParameters Teff, M⋆, and R⋆ from isochrone interpolation of the Gaia color–magnitude values. These can deviate from literature values but are consistent with the remainder of the analysis of the field star population.Download table as: ASCIITypeset image
Table 10. Planet Candidates
TIC | TOI | Statusa | Period | Depth | Gaia G | Gaia BP | Gaia RP | Distance | Teff | M⋆ | R⋆ | Follow-up |
---|---|---|---|---|---|---|---|---|---|---|---|---|
(days) | (ppm) | (mag) | (mag) | (mag) | (pc) | (K) | (M⊙) | (R⊙) | Status | |||
129637892 | 155.01 | PC | 5.4 | 8232 | 9.38 | 9.66 | 8.98 | 188 | 6144 | 1.26 | 1.87 | Passed spectroscopic vetting |
281408474 | 628.01 | PC | 3.4 | 6353 | 10.06 | 10.38 | 9.61 | 179 | 5850 | 1.05 | 1.45 | Undergoing spectroscopic vetting |
293853437 | 629.01 | PC | 8.7 | 2075 | 8.73 | 8.79 | 8.66 | 336 | 8400 | 2.06 | 2.49 | Undergoing spectroscopic vetting |
Note.
aPC: Active planet candidate.Download table as: ASCIITypeset image
Table 11. Candidates Determined to Be False Positives
TIC | TOI | Statusa | Period | Depth | Gaia G | Gaia BP | Gaia RP | Distance | Teff | M⋆ | R⋆ |
---|---|---|---|---|---|---|---|---|---|---|---|
(days) | (ppm) | (mag) | (mag) | (mag) | (pc) | (K) | (M⊙) | (R⊙) | |||
7624182 | 412.01 | NEB | 1.1 | 859 | 8.83 | 8.87 | 8.77 | 466 | 8099 | 2.2 | 3.28 |
14091633 | 447.01 | SB1 | 5.5 | 20670 | 9.2 | 9.46 | 8.83 | 126 | 6316 | 1.17 | 1.3 |
49899799 | 416.01 | SB1 | 7.0 | 7442 | 8.65 | 8.94 | 8.24 | 132 | 6065 | 1.23 | 1.89 |
55452495 | 336.01 | NEB | 8.9 | 4962 | 10.1 | 10.22 | 9.92 | 610 | 7287 | 1.88 | 2.95 |
92359850 | 387.01 | NPC | 4.2 | 3880 | 10.01 | 10.28 | 9.61 | 219 | 6165 | 1.26 | 1.61 |
123898871 | 630.01 | BEB | 4.9 | 11111 | 10.08 | 10.26 | 9.81 | 311 | 6980 | 1.45 | 1.74 |
156987351 | 476.01 | BEB | 3.1 | 6560 | 9.09 | 9.26 | 8.84 | 246 | 7104 | 1.62 | 2.11 |
175482273 | 369.01 | BEB | 5.5 | 3229 | 9.52 | 9.58 | 8.92 | 172 | 6192 | 1.26 | 1.58 |
350743714 | 165.01 | SB1 | 7.8 | 4399 | 10.01 | 10.25 | 9.61 | 211 | 6260 | 1.27 | 1.51 |
365781372 | 627.01 | BEB | 1.1 | 5368 | 10.17 | 10.3 | 9.95 | 640 | 7127 | 1.87 | 3.11 |
Note.
aNEB: Nearby eclipsing binary; BEB: blended eclipsing binary; SB1: single-lined spectroscopic binary; NPC: transit caused by nearby source that may still be planetary in origin.Download table as: ASCIITypeset image
Table 12. Confirmed Planets around Evolved Stars with Tmag < 10 Not Included in Occurrence Rate Calculation
TIC | TOI | Name | Status | Period | Depth | Gaia G | Gaia BP | Gaia RP | Distance | Teff | M⋆ | R⋆ | References |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
(days) | (ppm) | (mag) | (mag) | (mag) | (pc) | (K) | (M⊙) | (R⊙) | |||||
290131778 | 123.01 | ⋯ | P | 3.3 | 3177 | 8.23 | 8.43 | 7.77 | 162 | 6234 | 1.46 | 2.72 | Wang et al. (2019) |
231670397 | 104.01 | WASP-73 | P | 4.1 | 3586 | 10.26 | 10.57 | 9.82 | 319 | 5950 | 1.21 | 2.33 | Delrez et al. (2014) |
339672028 | 481.01 | ⋯ | P | 10.3 | 4590 | 9.85 | 10.22 | 9.35 | 180 | 5661 | 0.98 | 1.8 | |
410214986 | 200.01 | DS Tuc | P | 8.1 | 3576 | 8.32 | 8.7 | 7.81 | 44 | 5466 | 0.93 | 0.92 | Newton et al. (2019) |
452808876 | 453.01 | WASP-82 | P | 2.7 | 6400 | 9.9 | 10.18 | 9.49 | 277 | 6126 | 1.27 | 2.21 | West et al. (2016) |
Download table as: ASCIITypeset image
Footnotes
- ∗
Based on observations obtained with the Hungarian-made Automated Telescope Network. Based in part on observations obtained with the Tillinghast Reflector 1.5 m telescope and the 1.2 m telescope, both operated by the Smithsonian Astrophysical Observatory at the Fred Lawrence Whipple Observatory in Arizona. This work makes use of the Smithsonian Institution High Performance Cluster (SI/HPC). Based in part on observations made with the Southern African Large Telescope (SALT).
- 49
NASA Exoplanet Archive, 2019 April.
- 50
We have taken into account the actual duty cycles in each TESS sector by only using the light curve available to the Box Least Search in the quick-look pipeline. This is the light curve length after accounting for bad point masking due to scattered light, pointing jitter, and data downlink gap. The number of days used in each of these seven sectors are 21.5, 21.4, 16.5, 15.3, 21.5, 17.3, and 21.5.