Issue |
A&A
Volume 647, March 2021
|
|
---|---|---|
Article Number | A180 | |
Number of page(s) | 10 | |
Section | Planets and planetary systems | |
DOI | https://doi.org/10.1051/0004-6361/202039586 | |
Published online | 31 March 2021 |
NGTS-13b: a hot 4.8 Jupiter-mass planet transiting a subgiant star★
1
Observatoire de Genève, Université de Genève,
Chemin Pegasi 51b,
1290
Sauverny,
Switzerland
e-mail: Nolan.Grieves@unige.ch
2
Departamento de Astronomía, Universidad de Chile,
Casilla 36-D,
Santiago,
Chile
3
Department of Physics, University of Warwick,
Gibbet Hill Road,
Coventry,
CV4 7AL,
UK
4
Centre for Exoplanets and Habitability, University of Warwick,
Gibbet Hill Road,
Coventry,
CV4 7AL,
UK
5
Institute of Planetary Research, German Aerospace Center,
Rutherfordstrasse 2,
12489
Berlin,
Germany
6
School of Physics and Astronomy, University of Leicester,
LE1 7RH,
UK
7
Center for Astronomy and Astrophysics, TU Berlin,
Hardenbergstr. 36,
10623
Berlin,
Germany
8
Astronomy Unit, Queen Mary University of London,
Mile End Road,
London
E1 4NS,
UK
9
Astrophysics Group, Cavendish Laboratory,
J.J. Thomson Avenue,
Cambridge
CB3 0HE,
UK
10
Department of Physics, Kavli Institute for Astrophysics and Space Research, MIT,
77 Mass. Ave,
Cambridge,
MA 02139,
USA
11
Centro de Astrofísica y Tecnologías Afines (CATA),
Casilla 36-D,
Santiago,
Chile
12
Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez,
Av. Diagonal las Torres 2640,
Peñalolén,
Santiago,
Chile
13
Millennium Institute for Astrophysics,
Santiago,
Chile
14
Instituto de Astronomía, Universidad Católica del Norte,
Angamos 0610,
1270709
Antofagasta,
Chile
Received:
2
October
2020
Accepted:
6
January
2021
We report the discovery of the massive hot Jupiter NGTS-13b by the Next Generation Transit Survey (NGTS). The V = 12.7 host star is likely in the subgiant evolutionary phase with logg* = 4.04 ± 0.05, Teff = 5819 ± 73 K, M* = 1.30−0.18+0.11 M⊙, and R* = 1.79 ± 0.06 R⊙. The NGTS detected a transiting planet with a period of P = 4.12 days around the star, which was later validated with the Transiting Exoplanet Survey Satellite (TESS; TIC 454069765). We confirm the planet using radial velocities from the CORALIE spectrograph. Using NGTS and TESS full-frame image photometry combined with CORALIE radial velocities, we determine NGTS-13b to have a radius of RP = 1.142 ± 0.046 RJup, a mass of MP = 4.84 ± 0.44 MJup, and an eccentricity of e = 0.086 ± 0.034. Previous studies have suggested that ~4 MJup may be the border separating two formation scenarios (e.g., core accretion and disk instability) and that massive giant planets share similar formation mechanisms as lower-mass brown dwarfs. NGTS-13b is just above 4 MJup, making it an important addition to the statistical sample needed to understand the differences between various classes of substellar companions. The high metallicity of NGTS-13, [Fe/H] = 0.25 ± 0.17, does not support previous suggestions that massive giants are found preferentially around lower metallicity host stars, but NGTS-13b does support findings that more massive and evolved hosts may have a higher occurrence of close-in massive planets than lower-mass unevolved stars.
Key words: planets and satellites: detection / planets and satellites: individual: NGTS-13b / techniques: photometric / techniques: radial velocities
NGTS and TESS reduced photometry files are only available at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/cat/J/A+A/647/A180
© ESO 2021
1 Introduction
The first extrasolar planets discovered were a new class of planets not found in our Solar System: hot Jupiters, Jupiter-mass planets with orbital periods ≤10 days (e.g., Mayor & Queloz 1995). Planet formation and evolution theories were abruptly altered to account for these distinct systems (e.g., Boss 1996). However, there is still no consensus on the predominant formation mechanism of these massive close-in planets. Three main hypotheses have been suggested as possible mechanisms for how hot Jupiters formed and now reside so close to their hoststar: in situ formation, disk migration, and high-eccentricity tidal migration. A review of these scenarios is available in Dawson & Johnson (2018).
There are two mechanisms proposed for giant planet formation in general: gravitational instability – through its own self-gravity, the protoplanetary disk fragments into giant gaseous protoplanets that contract and collapse to form giant planets (e.g., Boss 1997) – and core accretion – planetesimal collisions form rocky protoplanet cores that accrete gas from the protoplanetary disk (e.g., Pollack et al. 1996). In situ gravitational instability is not plausible at short orbital periods because the disk’s fast rotation and high temperature (thermal pressure) support the local gas against gravitational collapse (Dawson & Johnson 2018). Core accretion can only occur if there is enough mass in the feeding zone to grow a sufficiently large core (~10 M⊕; Rafikov 2006; Lee et al. 2014; Piso et al. 2015) within the disk’s lifetime; however, at short orbital periods, the feeding zone is smaller (smaller Hill radii) and thus it is implausible that it forms a massive core under normal disk conditions (Lee & Chiang 2016). However, in situ core accretion of hot Jupiters may be possible with highly efficient radial drift of solids to the inner disk (Dawson & Johnson 2018).
A more common line of thought is that hot Jupiters formed at larger orbital periods and migrated inward. One mechanism for this migration is via disk migration, where angular momentum is exchanged with the disk through corotation and Lindblad torques (e.g., Lin et al. 1996). Another proposed mechanism for migration is high-eccentricity tidal migration, where a giant planet has orbital angular momentum extracted from it by a perturber, pushing the planet to a highly elliptical orbit; the planet then reduces its orbital energy through tidal interactions with the central star, causing it to circularize into a close-in orbit (e.g., Rasio & Ford 1996).
These various formation theories should have differing effects on the characteristics of hot Jupiters. Slow and smooth migration inthe circumstellar disk from simple angular momentum loss should produce aligned and circularized orbits (D’Angelo et al. 2003), but more dynamic migration, such as high-eccentricity tidal migration, should produce planets with higher eccentricities in more misaligned orbits (e.g., Triaud et al. 2010). However, for hot Jupiters with orbital periods shorter than 5 days, the eccentricity will be erased in a few billion years due to tidal circularization (Adams & Laughlin 2006). Orbital misalignment, a difference between a star’s rotation axis and a planet’s orbital axis (obliquity), evolves more slowly than eccentricity (Hut 1981; Winn et al. 2005; Barker & Ogilvie 2009) and can therefore be detected in relatively older stars. There are various methods for measuring obliquity (e.g., the Rossiter-McLaughin effect, Rossiter 1924; McLaughlin 1924).
The parameter distributions of the entire sample of hot Jupiters, such as their semimajor axes and ages, also provide insight into which formation mechanisms dominate in general as well as in specific environments. It is thus important to continue to build a sample of planets (and host stars) with well-characterized parameters. When a planet is detected by both the transit and radial velocity (RV) methods, multiple characteristics of the planet can be constrained better, relative to when it is only detected through one method. Thus, large photometric surveys are valuable as they can view large samples of stars and identify those with transiting planets, which can then be followed up with RV instruments that can typically only observe one star at a time.
The Next Generation Transit Survey (NGTS; Wheatley et al. 2018) is a ground-based photometric survey located in Chile at the European Southern Observatory (ESO) Paranal site. NGTS consists of 12 fully robotic telescopes each with a 20 cm photometric aperture and a wide 8 deg2 field-of-view, making the survey capable of observing a large sample of stars. Here we present the discovery of a massive hot Jupiter orbiting a subgiant star. This planet was first detected by NGTS, was subsequently verified by the space-based all-sky Transiting Exoplanet Survey Satellite (TESS; Ricker et al. 2015), and was then confirmed through follow-up RV measurements with the CORALIE (Queloz et al. 2001) spectrograph. In Sect. 2, we present our observations, in Sect. 3 we describe our analysis and present our results, in Sect. 4 we discuss our results, and we present our conclusions in Sect. 5.
![]() |
Fig. 1 TESS FFI cutout (15 × 15 pixels) in the region of NGTS-13 (TIC 454069765; T = 12.11) from which the TESS lightcurve was generated. The red boxes indicate the target pixel aperture mask used to extract the photometry, and the white boxes without a border are the pixels used to determine the sky background. The red star indicates the position of NGTS-13. The yellow crosses indicate TIC 454069767 (ΔT = 1.68, 22.5″ west) and TIC 454069772 (ΔT = 0.6, 57″ southeast). Blue plus signs denote the positions of stars in Gaia DR2 within 1′ of NGTS-13. |
2 Observations
2.1 NGTS photometry
NGTS-13 was observed using a single NGTS telescope from UT 2017 December 10 to UT 2018 August 7. During this period, 255 709 observations were taken with exposure times of 10 s. Image reduction and photometry were performed with the NGTS pipeline (Wheatley et al. 2018) using CASUtools software1. The lightcurve was de-trended, and systematic effects were removed using the SysRem algorithm (Tamuz et al. 2005).
The NGTS lightcurves were searched for periodic transit-like signals using an implementation of the box-fitting least-squares (BLS) algorithm (Kovács et al. 2002). We detected a strong peak in the BLS periodogram at 4.12 days, and folding the NGTS photometry on this period revealed a clear transit signal.
We binned the NGTS data to 2-min bins for our analysis, as described in Sect. 3.2. NGTS-13 has some nearby companions (see Fig. 1), and the NGTS photometry is expected to suffer some dilution, which we discuss in Sect. 3.2.2. The NGTS photometry in 30-min bins, for display purposes, is shown in Fig. 2. A full table of reduced NGTS photometric observations is available in a machine-readable format at the CDS.
![]() |
Fig. 2 Photometry observations of NGTS-13. Top: NGTS observations (in 30-min bins for visual purposes), with the EXOFASTv2 model (see Sect. 3.2) in red. Middle: TESS FFI observations after the de-trending described in Sect. 2.2 was applied, with the EXOFASTv2 model in red. Bottom: NGTS in 30-min bins and TESS FFI photometry phased to the 4.12 day period of NGTS-13b. The NGTS data are in 30-min bins and then phased to the period. The red lines show the EXOFASTv2 model. |
2.2 TESS photometry
TESS observed NGTS-13 (TIC 454069765) in Sector 10 from UT 2019 March 28 to UT 2019 April 21 with 30-min cadence full frame images (FFIs). We created a lightcurve via aperture photometry of the TESS 30-min cadence FFIs following the method set out in Gill et al. (2020). We determined a threshold for target pixels and for background pixels based on an iterative sigma-clipping to determine the median and standard deviation of the background pixels. To exclude the neighboring T = 12.8 star (TIC 454069772, 57″ southeast, see Fig. 1), we only included pixels in our final aperture for which neighboring pixels closer to the center of our target show a higher illumination. For the final pixel mask of NGTS-13, seven pixels were selected (see Fig. 1). To remove the systematic trends from the TESS lightcurve, we masked out the transits and then fitted a linear spline interpolating across the positions of the transits. Before removing systematic trends, we estimated a signal-to-noise ratio (S/N) as the transit depth relative to the standard deviation of out-of-transit observations and found a single TESS transit S∕N ~ 5.4, which corresponds to S∕N ~ 13.3 when considering all six transits in the TESS data (S∕N ∝). The TESS photometry is displayed in Fig. 2. A full table of reduced TESS photometric observations is available in a machine-readable format at the CDS.
2.3 CORALIE spectroscopy
We obtained 13 observations of NGTS-13 with the high resolution echelle spectrograph CORALIE on the Swiss 1.2 m Euler telescope (Queloz et al. 2001) at La Silla Observatory, Chile, between UT 2019 December 12 and UT 2020 January 23. CORALIE has a resolution of R ~ 60 000 and is fed by two fibers: a 2 arcsec on-sky science fiber encompassing the star and another fiber that can either connect to a Fabry-Pérot etalon for simultaneous wavelength calibration or on-sky for background subtraction of sky flux. We observed NGTS-13 in the simultaneous Fabry-Pérot wavelength calibration mode using an exposure time of 2700 s The spectra were reduced with the CORALIE standard reduction pipeline, and RVs were computed for each epoch by cross-correlating with a binary G2 mask (Pepe et al. 2002). We co-added the 13 CORALIE spectroscopic observations by correcting each epoch to the stellar rest frame with its corresponding barycentric RV to create a single high signal-to-noise spectrum. We list the CORALIE RVs in Table 1. The RVs phased to the 4.12 day period are displayed in Fig. 3.
CORALIE RVs for NGTS-13.
![]() |
Fig. 3 CORALIE RVs for NGTS-13 with the relative RV offset γrel subtracted (Table 4) and phased to the 4.12 day period of NGTS-13b. The red line shows the Keplerian fit for the planet from EXOFASTv2 (see Sect. 3.2). |
3 Analysis
3.1 Spectral analysis
We used wavelet analysis to extract stellar atmospheric parameters from the co-added 13 CORALIE spectroscopic observations of NGTS-13 using the methodology set out in Gill et al. (2018, 2019). With the wavelet analysis, we find NGTS-13 to have surface rotational velocity vsini* = 6.2 ± 1.2 km s−1. We also derived stellar atmospheric parameters using SpecMatch-Emp (Yee et al. 2017). SpecMatch-Emp uses a largelibrary of stars with well-determined parameters to match the input spectra and derive spectral parameters. We used a spectral region that includes the Mg I b triplet (5100–5400 Å) to match our spectra. SpecMatch-Emp uses χ2 minimisation and a weighted linear combination of the five best matching spectra in the SpecMatch-Emp library to determine Teff, log g*, and [Fe/H]. We combined the wavelet analysis results and the SpecMatch-Emp results to create wide uncertainties that include the uncertainty range of both methods to use as priors for their final derivation, as described below. We present the spectroscopically derived parameters obtained from both methods and the adopted combined values in Table 2.
Stellar atmospheric parameters derived from CORALIE spectra and combining the analysis results of both SpecMatch-Emp (Yee et al. 2017) and the wavelet analysis described in Gill et al. (2018, 2019).
![]() |
Fig. 4 Spectral energy distribution model (black line) of NGTS-13 from the EXOFASTv2 fit with broadband averages (blue circles)and broadband measurements (red). The error bars in wavelength denote the bandwidth of the corresponding filter, and the error bars in flux denote the measurement uncertainty. |
3.2 Global analysis
We derived both planet and stellar parameters using EXOFASTv2 (Eastman et al. 2013, 2019; Eastman 2017), which can globally fit all photometry and RV data. A full description of EXOFASTv2 is given in Eastman et al. (2019). It can fit any number of transits and RV sources while exploring the vast parameter space through a differential evolution Markov chain coupled with a Metropolis-Hastings Monte Carlo sampler. A built-in Gelman-Rubin statistic (Gelman & Rubin 1992; Gelman et al. 2003; Ford 2006) is used to check the convergence of the chains.
We used Modules for Experiments in Stellar Astrophysics (MESA) Isochrones & Stellar Tracks (MIST; Choi et al. 2016; Dotter 2016) isochrones and the spectral energy distribution (SED) within the EXOFASTv2 fit to determine host star parameters. For the SED fit within EXOFASTv2 (Stassun & Torres 2016), we used photometry from APASS DR9 BV (Henden et al. 2016), 2MASS JHK (Skrutskie et al. 2006), SDSS DR12 gri (Alam et al. 2015), ALL-WISE W1, W2, and W3 (Wright et al. 2010), and GALEX NUV (Bianchi et al. 2011), which is presented in Table 3. The SED fit within EXOFASTv2 puts systematic floors on the broadband photometry errors (Stassun & Torres 2016). The EXOFASTv2 SED fit is presented in Fig. 4. We used our spectroscopically derived Teff, log g*, and [Fe/H] values as priors in the global model as well as a Gaussian prior for parallax from the value and uncertainty in Gaia DR2. We put an upper limit on the extinction based on the maximum in the line-of-sight value from the Schlafly & Finkbeiner (2011) galaxy dust map. All other fitted and derived parameters from our EXOFASTv2 model have conservative physical boundaries that are detailed in Table 3 of Eastman et al. (2019), which also gives a thorough explanation of each parameter.
Due to the long exposure time of the TESS full frame exposures, we had to account for smearing of the lightcurve. Therefore, within EXOFASTv2, we integrated the model over the exposures by using ten evaluated model points equally spaced around the center of each 30-min exposure (Eastman et al. 2019). We assigned theTESS FFI photometry to the built-in EXOFASTv2 “TESS” band and the NGTS photometry to the “R” band for the computation of the limb darkening coefficients. We found the EXOFASTv2 “R” band to have the most similar bandpass to NGTS in the EXOFASTv2 framework, which only allows filters defined in Claret & Bloemen (2011). We tested the use of this filter by running a fit with the NGTS data assigned to the “TESS” filter and excluding the TESS data; we obtained limb darkening parameters for the NGTS photometry within the uncertainties of the parameters presented in Table 4. We also ran a fit including the TESS data assigned to the “TESS” filter and the NGTS data assigned to the “Kepler” filter; we again found limb darkening parameters within the uncertainties of those presented. We accounted for the dilution of the photometry lightcurves, which we describe in Sect. 3.2.2. For our EXOFASTv2 global model and Markov chain Monte Carlo (MCMC) fit, we used 56 walkers (2 × nparameters), or chains, and allowed the fit to run until convergence (132 776 steps).
NGTS-13 (2MASS J11445767-38082292 / TIC 454069765) astrometric and photometric properties.
3.2.1 Bimodal stellar mass and age
We inspected the posterior distributions of each fitted and derived parameter and found bimodal distributions in both the stellar mass and age. This type of bimodality between two different stellar ages and masses occurs due to a degeneracy between two MIST isochrone tracks that EXOFASTv2 interpolates between and which has been seen in several recent studies (e.g., Ikwut-Ukwa et al. 2020; Pepper et al. 2020; Carmichael et al. 2020a,b). Similar to those studies, we split the host star mass posterior distribution at the valley between the two peaks, 1.25 M⊙, and extracted two separate solutions, both of which are presented in Table 3. We show the stellar mass and age posterior distributions in Fig. 5. The higher mass solution is moderately more probable, with a probability of 60% compared to 40% for the lower mass solution.
However, for our final adopted solution, instead of picking one peak, we defined wider uncertainties by computing confidence intervals for each posterior distribution based on the smallest area in the posterior distribution function that contains 68% of the MCMC steps. We computed these confidence intervals using a subroutine within the open source python package MC32 (Cubillos et al. 2017). In summary, the code orders the posterior distribution by value and creates a smoothed posterior density distribution (PDF), computes a cumulative distribution function (CDF) of the ordered PDF, and computes where the CDF reaches 68%of its maximum value. The minimum and maximum parameter values inside this interval are the boundaries of the 1σ confidence interval. We used the median value within the confidence interval as our final value for each parameter and the minimum and maximum values within the confidence interval as their uncertainties, which are in bold in Table 4. Figure 5 displays these values for the host star mass and age.
![]() |
Fig. 5 Stellar mass and age posterior distributions from EXOFASTv2. These distributions show the relative probabilitybetween the two peaks of the bimodal distributions, which is approximately 60 to 40% in favor of a more massive, younger system. We split the stellar mass distribution in the valley at 1.25 M⊙ to create the two solutions presented in Table 4. For our final solution, we adopt the median value (vertical black lines) and upper and lower boundaries (dashed vertical lines) of the 68% confidence intervals we computed, as describedin Sect. 3.2.1. |
3.2.2 Dilution
NGTS-13 has several close companions, including two stars that are only slightly dimmer: TIC 454069767 (TESS mag = 14.1, 22.5″ west) and TIC 454069772 (TESS mag = 13.4, 57″ southeast; see Fig. 1). We expect the TESS photometry of NGTS-13 to be completely diluted by the companion that is 22.5″ away and to be partially diluted by the companion 57.3′′ away. One other close companion has a TESS mag <18, while all other companions are dimmer and not expected to dilute at all.
To account for dilution, we calculated a dilution factor, given as a contrast ratio C, between the combined flux of all contaminating sources and the flux of the target star:
(1)
where Ftarget and Fcont are the flux contributions within the photometric aperture from the target and contaminating stars, respectively. We followed the method detailed in Bryant et al. (2020) to estimate dilution.
We determined the point-spread function (PSF) in the region of the camera surrounding NGTS-13 and extracted 2D Gaussian shape parameters (Pál 2009) using the Cluster Difference Imaging Photometric Survey (CDIPS; Bouma et al. 2019). For NGTS-13, we find shape parameters of S = 1.88 ± 0.047 (FHWM = 1.71 pixels), D = 0.033 ± 0.011, and K = 0.169 ± 0.007. We considered all the stars in TICv8 (Stassun et al. 2019) within 1′ of NGTS-13 and found a dilution factor of 19.10% in the TESS photometry. We applied the Bryant et al. (2020) method to our NGTS data assuming a circular Gaussian PSF (D = K = 0) and an FWHM = 2 ± 0.25 pixels, so S = 1.38 ± 0.3. We determined a dilution factor of 0.64
% for our NGTS photometry. Both of these dilution factors were used as inputs in the EXOFASTv2 global model. We note that EXOFASTv2 defines their dilution factor as the fractional contribution from neighboring stars, or C/(1+C).
4 Discussion
Our analysis finds NGTS-13b to have a mass of 4.84 ± 0.44 MJup, a radius of 1.142 ± 0.046 RJup, a short 4.12 day orbital period, and an equilibrium temperature of 1605 ± 30 K, making NGTS-13b a massive hot Jupiter. Although close-in very massive planets are the easiest to detect, they are far from the most common. As of 2020 September 16, the NASA Exoplanet Archive3 lists 4277 confirmed planets; however, only 1764 have measured masses and only 1691 of these are below 13 MJup (the approximate border between planets and brown dwarfs). Of the planets with measured masses, 971 are giant (MP = 0.5–13 MJup), and 223 of these giant planets havemasses greater than 4 MJup. Here we only focus on planets that have well-determined masses and radii (percentage errors <10%); this leaves us with 225 planets, of which 155 are giants and, of these, only 17 are massive giants (MP > 4 MJup). These very massive planets have a low occurrence rate, analogous to the more massive brown dwarfs, which are indeed known to be rare (the brown dwarf desert, e.g., Marcy & Butler 2000; Grether & Lineweaver 2006).
![]() |
Fig. 6 [Fe/H] and mass relationship for giant planets (0.5–13 MJup) with percentage errors <10% for both mass and radius and a defined [Fe/H] in the NASA Exoplanet Archive2 (i.e., a total of 137 previously known planets, including 17 with masses greater than 4 MJup). |
4.1 Massive substellar companion populations
Previous studies have suggested that 4 MJup could be an approximate border to distinguish a distinct massive giant planet population from lower-mass giant planets. Santos et al. (2017) find that massive giant planets tend to orbit stars that have [Fe/H] distributions statistically similar to those of field stars, unlike lower-mass giant planets, which occur more frequently around metal-rich stars. This could be evidence for two different formation mechanisms, where lower-mass giants form via core accretion (Pollack et al. 1996) and higher-mass giants form via disk instability (Boss 1997), as formation via disk instability is not as metallicity-dependent as core accretion (Boss 2002; Cai et al. 2006).
Additionally, Schlaufman (2018) find that substellar companions with masses ≲ 4 MJup preferentially orbit metal-rich stars (a property associated with core accretion), while companions with masses ≳ 10 MJup do not sharethis property. Maldonado et al. (2019) also find that massive giant planets tend to have host stars with lower metallicities relative to lower-mass giant planets and conclude that the core-accretion planet formation mechanism achieves its maximum efficiency for planets with masses in the range 0.2–2 MJup. Goda & Matsuo (2019) conclude that the mean metallicity of stars that host companions between 4 and 25 MJup is lower than that of the sample of companions with masses 0.3 to 4 MJup, and that the mean metallicity of stars with companions more massive than 25 MJup is much lower than those of the other two subsamples. Goda & Matsuo (2019) use 25 MJup as the lower mass limit of brown dwarfs (instead of the deuterium burning limit) because they found the mean metallicity of G-type host stars with objects lighter than 25 MJup to be much higher than those with objects more massive than 25 MJup; they suggest25 MJup as the upper mass limit for core-accreted planets. However, other studies have suggested that lower-mass brown dwarfs (13–42.5 MJup) form via disk instability, for example, Ma & Ge (2014).
Adibekyan (2019) analyzes the known giant planet population and his results do not support 4 MJup being a transition point between two separate formation channels; however, his results do suggest that high-mass planets can form through different mechanisms depending on their initial environment. With a metallicity of [Fe/H] = 0.25 ± 0.17, the host star of NGTS-13b is metal-rich and does not provide additional evidence that more massive giant planets prefer a formation mechanism that is not core accretion. We show NGTS-13b in this context in Fig. 6, which displays the metallicity of giant planets from 0.5 to 13 MJup.
4.2 NGTS-13b in the context of the mass-radius relationship
We depict NGTS-13b in terms of its density, ρP = 4.02 ± 0.55 g cm−3, in Fig. 7 by displaying it in the mass-radius diagram with other giant planets and brown dwarfs, as well as by displaying density as a function of mass. The colors of the symbols in the mass-radius diagram vary as a function of equilibrium temperature to show the effect that high equilibrium temperatures can have on radii. In this figure, we can see the larger maximum radii for planets with masses lower than ~4 MJup, but this is explained by the redder coloring of these symbols indicating higher equilibrium temperatures (resulting in more inflation). NGTS-13b has a typical radius compared to planets with similar masses, suggesting that, even with its high equilibrium temperature, it is not significantly affected by bloating (an inflated radius due to a planet’s atmosphere being heated from stellar irradiation and expanding), which is expected for massive planets (Sestovic et al. 2018). The downward trend in radii (and increasing trend in densities) with larger masses in Fig. 7 highlights the importance of the larger surface gravity in more massive objects.
4.3 A slightly evolved host star
NGTS-13 has an effective temperature of Teff = 5819 ± 73 K, suggesting it is a G2-type star (Pecaut & Mamajek 2013). However, the rather large stellar radius of R* = 1.79 ± 0.06 R⊙ indicates that NGTS-13 has slightly evolved as this radius is larger than expected for a main-sequence star of this temperature, and its surface gravity, logg* = 4.04 ± 0.05, suggests that the star is a subgiant (logg* < 4.1). Our MIST isochrone analysis (see Sect. 3.2) finds an equivalent evolutionary point (EEP) value of 413, which puts NGTS-13 still in the main sequence evolutionary phase, as the terminal age main sequence EEP does not start until EEP = 454 (see Choi et al. 2016; Dotter 2016, and the MIST documentation4). Additionally, it is less likely, in terms of evolutionary timescales, to observe a star near or past the turnoff point than to observe it in the middle of the main sequence; therefore, NGTS-13 is likely still fusing hydrogen in its core but has likely begun the transition from the main sequence to the red giant branch, placing it in the subgiant branch.
Lillo-Box et al. (2016) found a relative lack of hot Jupiters around giant and subgiant stars, suggesting that close-in massive planets around main-sequence stars are engulfed by the star as it evolves. However, other studies do not find a lack of hot Jupiters around evolved hosts; additionally, NGTS-13 is likely still burning hydrogen in its core and has not evolved long enough to engulf planets near the orbital distance of NGTS-13b. Grunblatt et al. (2019) found that low-luminosity evolved red giant branch stars potentially have a higher population of close-in giant (RP > 1 RJup) planets than main-sequence stars, suggesting that stellar evolution does not significantly affect the close-in giant planet occurrence until stars are substantially in the red giant branch with radii of 5–6 R⊙. Zhou et al. (2019) studied the occurrence rate of hot Jupiters as a function of stellar mass and did not find any statistically consistent trends with stellar mass; they found that hot Jupiters are just as abundant around main-sequence A stars as they are around F and G stars.
The M* = 1.30 M⊙ stellar mass of NGTS-13 is larger than a typical G-type main-sequence star, which suggests that it evolved to a cooler temperature and is a “retired” F star. A relatively larger host star mass is consistent with previous studies that found massive planets to be more common around massive stars (Santos et al. 2017; Maldonado et al. 2019). Santos et al. (2017) find that more massive giant planets are more common around more massive hosts, which are often more evolved; they suggest that the existence of two distinct populations of giant planets could be related to evolved stars not showing a clear metallicity-giant planet correlation.
We display host star masses by color in Fig. 7. Only one host star has a mass below 0.9 M⊙ in our sample of 17 well-defined massive giant planets. Grunblatt et al. (2018) found that close-in giant planets around evolved stars tend to have more eccentric orbits than those around main-sequence stars, but they focus on giant stars and orbital periods longer than 4.5 days. We explore the eccentricity of NGTS-13b further in Sect. 4.4.
![]() |
Fig. 7 Radius (top) and density (bottom) as a function of mass for transiting brown dwarfs and giant planets (0.5–13 MJup) with percentage errors <10% for both mass and radius in the NASA Exoplanet Archive3. Transiting brown dwarfs – of which there are a total of 16 with well-defined masses and radii – are from the literature summary found in Mireles et al. (2020). The stars denote NGTS-13b, and the vertical dashed lines mark masses of 4 MJup and 13 MJup (the latter being the approximate lower mass limit for brown dwarfs). The colors of the symbols correspond to equilibrium temperatures in the top plot and host star masses in the bottom plot. The downward trend in maximum radii with increasing mass exhibits the effect of stronger surface gravity on more massive objects, which is clearly displayed by the linear trend in density. In the top plot, we only display planets that also have an equilibrium temperature in the archive: a total of 133 previously known planets, including 14 with masses greater than 4 MJup. |
![]() |
Fig. 8 Posterior distribution of the eccentricity from our EXOFASTv2 MCMC analysis, including all chains and steps of the distribution. |
4.4 A slightly eccentric orbit
NGTS-13b has a slightly eccentric orbit, e = 0.086 ± 0.034, with a shortperiod of 4.12 days. Analyzing the posterior distribution of the eccentricity displayed in Fig. 8, we see that it is not centered around 0. We performed the test from Lucy & Sweeney (1971) and find the statistical significance of the eccentric fit to be P(e > 0) = 0.9591, which just passes the 5% significance level suggested by Lucy & Sweeney (1971).
To further test the eccentric model, we fitted a Keplerian to the 13 RVs with priors on the time of conjunction and period and compare fits with and without forcing and
to 0. We also allowed an RV jitter term to vary, which corresponds to k = 7 free parameters for the eccentric fit and k = 5 for the circular fit. We determined the Bayesian information criterion (BIC), where, given the free parameters k, the number of measurements n, and the maximized value of the likelihood function of the model
:
(2)
When choosing between several models, the one with the lowest BIC is preferred. We find a ΔBIC = 7.17, which shows that the eccentricity model is moderately favored over the circularized model. For this test, we found an RV jitter of 31 m s−1 for the circular fit but 0 m s−1 for the eccentric fit as no additional RV uncertainty is needed for the eccentric model.
We note that for the final EXOFASTv2 analysis we presented, the model has an RV jitter variance =
, which essentially reduces the error size of the RVs when evaluating the model. As noted in Eq. (13) of Hara et al. (2019), the uncertainty in the eccentricity is approximately proportional to the error of the RV measurement; therefore, this added RV jitter variance may cause an underestimation of our eccentricity uncertainties. Hara et al. (2019) show that the eccentricity estimate can be affected by an undetected correlated signal. Given the amplitude of the RV signal of NGTS-13b, we studied the possibility that an undetected outer planetary companion could cause a nonzero eccentricity. We tested this by adding a linear drift to our global EXOFASTv2 model. When allowing for an unconstrained linear RV drift, we find a slope of only 1.1
m s−1 day−1 and an eccentricity e = 0.084
, similar to our presented model with no RV drift. The similar eccentricity suggests that the NGTS-13b eccentricity measurement is not caused by the model attempting to account for an undetected planet.
The slight eccentricity of NGTS-13b is not uncommon for massive planets (see Fig. 9). Hansen (2010) found that hot giant planets more massive than 3 MJup have the upper envelope of their eccentricity distribution shifted to lower periods compared to less massive hot Jupiters. Additionally, Dawson & Johnson (2018) found that most hot Jupiters with periods < 3 days have circular orbits but that some hot Jupiters in the 3–10 day orbital period range occupy moderately eccentric orbits. Assuming a general tidal circularization effect for close-in planets from Eq. (3) of Adams & Laughlin (2006), NGTS-13b has a circularization timescale of 0.5 Gyr for a tidal quality factor Qp = 105, 5.0 Gyr for Qp = 106, and 50.3 Gyr for Qp = 107. Given the 4.23 Gyr age of the system, the current eccentricity may be an indication that NGTS-13b underwent dynamical interactions with other components in the system during its migration history, for example, high-eccentricity tidal migration (see Sect. 3.1 of Dawson & Johnson 2018). We place the eccentricity of NGTS-13b in context with other giant planets and transiting brown dwarfs in Fig. 9.
![]() |
Fig. 9 Eccentricity-period relationship for transiting brown dwarfs and giant planets (0.5–13 MJup) with percentage errors <10% for both mass and radius and a defined eccentricity in the NASA Exoplanet Archive1: a total of 129 previously known planets, including 17 with masses greater than 4 MJup. Transiting brown dwarfs are from the literature summary found in Mireles et al. (2020), for a total of 16 with well-defined masses and radii. |
5 Conclusions
We report the discovery of NGTS-13b, a massive hot Jupiter orbiting a subgiant star at a 4.12 day period. NGTS-13b has a mass of 4.84 ± 0.44 MJup, a radius of 1.142 ± 0.046 RJup, and an eccentricity of e = 0.086 ± 0.034 with an estimated equilibrium temperature of 1605 ± 30 K. Even with this high equilibrium temperature, NGTS-13b is a dense object with ρP = 4.02 ± 0.55 g cm−3, as is expected for massive planets. The effective temperature of NGTS-13, Teff = 5819 ± 73 K, suggests that it is a G-type star; however, its mass of M* = 1.30 M⊙ and radius R* = 1.788 ± 0.057 R⊙ suggest thatit is a retired F star in the subgiant branch.
NGTS-13b is a valuable addition to the relatively rare group (fewer than 20 currently known) of well-characterized (mass and radius percent errors <10%) massive (MP > 4 MJup) giant planets that are needed to understand the differences between giants, massive giants, and brown dwarfs. Previous studies have considered 4 MJup to be the boundary between core-accretion planet formation and other formation mechanisms, such as disk instability, based on metallicity distributions. However, NGTS-13 has a metallicity [Fe/H] = 0.25 ± 0.17 and does not provide further evidence that these massive giants form through mechanisms other than core accretion.
Acknowledgements
We thank the Swiss National Science Foundation (SNSF) and the Geneva University for their continuous support to our planet search programs. This work was carried out in the frame of the National Centre for Competence in Research PlanetS supported by the Swiss National Science Foundation (SNSF). This work uses data collected under the NGTS project at the ESO La Silla Paranal Observatory. The NGTS facility is operated by the consortium institutes with support from the UK Science and Technology Facilities Council (STFC) under projects ST/M001962/1 and ST/S002642/1. This publication makes use of The Data & Analysis Center for Exoplanets (DACE), which is a facility based at the University of Geneva (CH) dedicated to extrasolar planets data visualisation, exchange and analysis. DACE is a platform of the Swiss National Centre of Competence in Research (NCCR) PlanetS, federating the Swiss expertise in Exoplanet research. The DACE platform is available at https://dace.unige.ch. This paper includes data collected by the TESS mission. Funding for the TESS mission is provided by the NASA Explorer Program. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. J.S.J. acknowledges support by FONDECYT grant 1201371 and partial support from CONICYT project Basal AFB-170002. J.V.S. acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (project Four Aces; grant agreement No. 724427). M.N.G. acknowledges support from MIT’s Kavli Institute as a Juan Carlos Torres Fellow. Contributions by authors from the University of Warwick were supported by STFC consolidated grants ST/P000495/1 and ST/T000406/1. E.G. gratefully acknowledges support from the David and Claudia Harding Foundation in the form of a Winton Exoplanet Fellowship. The authors thank Aaron Dotter for his helpful advice.
References
- Adams, F. C., & Laughlin, G. 2006, ApJ, 649, 1004 [NASA ADS] [CrossRef] [Google Scholar]
- Adibekyan, V. 2019, Geosciences, 9, 105 [NASA ADS] [CrossRef] [Google Scholar]
- Alam, S., Albareti, F. D., Allende Prieto, C., et al. 2015, ApJS, 219, 12 [NASA ADS] [CrossRef] [Google Scholar]
- Barker, A. J., & Ogilvie, G. I. 2009, MNRAS, 395, 2268 [Google Scholar]
- Bianchi, L., Herald, J., Efremova, B., et al. 2011, Ap&SS, 335, 161 [Google Scholar]
- Boss, A. P. 1996, Lunar Planet. Sci. Conf., 27, 139 [Google Scholar]
- Boss, A. P. 1997, Science, 276, 1836 [NASA ADS] [CrossRef] [Google Scholar]
- Boss, A. P. 2002, ApJ, 567, L149 [Google Scholar]
- Bouma, L. G., Hartman, J. D., Bhatti, W., Winn, J. N., & Bakos, G. Á. 2019, ApJS, 245, 13 [CrossRef] [Google Scholar]
- Bryant, E. M., Bayliss, D., Nielsen, L. D., et al. 2020, MNRAS, 499, 3139 [Google Scholar]
- Cai, K., Durisen, R. H., Michael, S., et al. 2006, ApJ, 636, L149 [NASA ADS] [CrossRef] [Google Scholar]
- Carmichael, T. W., Quinn, S. N., Mustill, A. J., et al. 2020a, AJ, 160, 53 [Google Scholar]
- Carmichael, T. W., Quinn, S. N., Zhou, G., et al. 2020b, AJ, 161, 97 [Google Scholar]
- Choi, J., Dotter, A., Conroy, C., et al. 2016, ApJ, 823, 102 [Google Scholar]
- Claret, A., & Bloemen, S. 2011, A&A, 529, A75 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Cubillos, P., Harrington, J., Loredo, T. J., et al. 2017, AJ, 153, 3 [Google Scholar]
- D’Angelo, G., Kley, W., & Henning, T. 2003, ApJ, 586, 540 [NASA ADS] [CrossRef] [Google Scholar]
- Dawson, R. I., & Johnson, J. A. 2018, ARA&A, 56, 175 [NASA ADS] [CrossRef] [Google Scholar]
- Dotter, A. 2016, ApJS, 222, 8 [NASA ADS] [CrossRef] [Google Scholar]
- Eastman, J. 2017, EXOFASTv2: Generalized publication-quality exoplanet modeling code Astrophys. Source Code Libr. [record ascl:1710.003] [Google Scholar]
- Eastman, J., Gaudi, B. S., & Agol, E. 2013, PASP, 125, 83 [NASA ADS] [CrossRef] [Google Scholar]
- Eastman, J. D., Rodriguez, J. E., Agol, E., et al. 2019, PASP, submitted [arXiv:1907.09480] [Google Scholar]
- Ford, E. B. 2006, ApJ, 642, 505 [NASA ADS] [CrossRef] [Google Scholar]
- Gelman, A., & Rubin, D. B. 1992, Stat. Sci., 7, 457 [Google Scholar]
- Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. 2003, Bayesian Data Analysis, 2nd edn. (London: Chapman & Hall) [Google Scholar]
- Gill, S., Maxted, P. F. L., & Smalley, B. 2018, A&A, 612, A111 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Gill, S., Maxted, P. F. L., Evans, J. A., et al. 2019, A&A, 626, A119 [CrossRef] [EDP Sciences] [Google Scholar]
- Gill, S., Wheatley, P. J., Cooke, B. F., et al. 2020, ApJ, 898, L11 [Google Scholar]
- Goda, S., & Matsuo, T. 2019, ApJ, 876, 23 [Google Scholar]
- Grether, D., & Lineweaver, C. H. 2006, ApJ, 640, 1051 [NASA ADS] [CrossRef] [Google Scholar]
- Grunblatt, S. K., Huber, D., Gaidos, E., et al. 2018, ApJ, 861, L5 [NASA ADS] [CrossRef] [Google Scholar]
- Grunblatt, S. K., Huber, D., Gaidos, E., et al. 2019, AJ, 158, 227 [NASA ADS] [CrossRef] [Google Scholar]
- Hansen, B. M. S. 2010, ApJ, 723, 285 [NASA ADS] [CrossRef] [Google Scholar]
- Hara, N. C., Boué, G., Laskar, J., Delisle, J. B., & Unger, N. 2019, MNRAS, 489, 738 [NASA ADS] [CrossRef] [Google Scholar]
- Henden, A. A., Templeton, M., Terrell, D., et al. 2016, VizieR Online Data Catalog: II/336 [Google Scholar]
- Hut, P. 1981, A&A, 99, 126 [NASA ADS] [Google Scholar]
- Ikwut-Ukwa, M., Rodriguez, J. E., Bieryla, A., et al. 2020, AJ, 160, 209 [Google Scholar]
- Kovács, G., Zucker, S., & Mazeh, T. 2002, A&A, 391, 369 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Lee, E. J., & Chiang, E. 2016, ApJ, 817, 90 [Google Scholar]
- Lee, E. J., Chiang, E., & Ormel, C. W. 2014, ApJ, 797, 95 [Google Scholar]
- Lillo-Box, J., Barrado, D., & Correia, A. C. M. 2016, A&A, 589, A124 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Lin, D. N. C., Bodenheimer, P., & Richardson, D. C. 1996, Nature, 380, 606 [NASA ADS] [CrossRef] [Google Scholar]
- Lucy, L. B., & Sweeney, M. A. 1971, AJ, 76, 544 [NASA ADS] [CrossRef] [Google Scholar]
- Ma, B., & Ge, J. 2014, MNRAS, 439, 2781 [NASA ADS] [CrossRef] [Google Scholar]
- Maldonado, J., Villaver, E., Eiroa, C., & Micela, G. 2019, A&A, 624, A94 [CrossRef] [EDP Sciences] [Google Scholar]
- Marcy, G. W., & Butler, R. P. 2000, PASP, 112, 137 [NASA ADS] [CrossRef] [Google Scholar]
- Mayor, M., & Queloz, D. 1995, Nature, 378, 355 [Google Scholar]
- McLaughlin, D. B. 1924, ApJ, 60, 22 [Google Scholar]
- Mireles, I., Shporer, A., Grieves, N., et al. 2020, AJ, 160, 133 [Google Scholar]
- Pál, A. 2009, PhD thesis, Eötvös Loránd University [Google Scholar]
- Pecaut, M. J., & Mamajek, E. E. 2013, ApJS, 208, 9 [Google Scholar]
- Pepe, F., Mayor, M., Rupprecht, G., et al. 2002, The Messenger, 110, 9 [NASA ADS] [Google Scholar]
- Pepper, J., Kane, S. R., Rodriguez, J. E., et al. 2020, AJ, 159, 243 [Google Scholar]
- Piso, A.-M. A., Youdin, A. N., & Murray-Clay, R. A. 2015, ApJ, 800, 82 [NASA ADS] [CrossRef] [Google Scholar]
- Pollack, J. B., Hubickyj, O., Bodenheimer, P., et al. 1996, Icarus, 124, 62 [NASA ADS] [CrossRef] [Google Scholar]
- Queloz, D., Mayor, M., Udry, S., et al. 2001, The Messenger, 105, 1 [NASA ADS] [Google Scholar]
- Rafikov, R. R. 2006, ApJ, 648, 666 [Google Scholar]
- Rasio, F. A., & Ford, E. B. 1996, Science, 274, 954 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Ricker, G. R., Winn, J. N., Vanderspek, R., et al. 2015, J. Astron. Telesc. Instrum. Syst., 1, 014003 [Google Scholar]
- Rossiter, R. A. 1924, ApJ, 60, 15 [Google Scholar]
- Santos, N. C., Adibekyan, V., Figueira, P., et al. 2017, A&A, 603, A30 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Schlafly, E. F., & Finkbeiner, D. P. 2011, ApJ, 737, 103 [NASA ADS] [CrossRef] [Google Scholar]
- Schlaufman, K. C. 2018, ApJ, 853, 37 [NASA ADS] [CrossRef] [Google Scholar]
- Sestovic, M., Demory, B.-O., & Queloz, D. 2018, A&A, 616, A76 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163 [Google Scholar]
- Stassun, K. G., & Torres, G. 2016, AJ, 152, 180 [Google Scholar]
- Stassun, K. G., Oelkers, R. J., Paegert, M., et al. 2019, AJ, 158, 138 [Google Scholar]
- Tamuz, O., Mazeh, T., & Zucker, S. 2005, MNRAS, 356, 1466 [NASA ADS] [CrossRef] [Google Scholar]
- Triaud, A. H. M. J., Collier Cameron, A., Queloz, D., et al. 2010, A&A, 524, A25 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Wheatley, P. J., West, R. G., Goad, M. R., et al. 2018, MNRAS, 475, 4476 [NASA ADS] [CrossRef] [Google Scholar]
- Winn, J. N., Noyes, R. W., Holman, M. J., et al. 2005, ApJ, 631, 1215 [Google Scholar]
- Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868 [Google Scholar]
- Yee, S. W., Petigura, E. A., & von Braun, K. 2017, ApJ, 836, 77 [NASA ADS] [CrossRef] [Google Scholar]
- Zhou, G., Huang, C. X., Bakos, G. Á., et al. 2019, AJ, 158, 141 [CrossRef] [Google Scholar]
All Tables
Stellar atmospheric parameters derived from CORALIE spectra and combining the analysis results of both SpecMatch-Emp (Yee et al. 2017) and the wavelet analysis described in Gill et al. (2018, 2019).
NGTS-13 (2MASS J11445767-38082292 / TIC 454069765) astrometric and photometric properties.
All Figures
![]() |
Fig. 1 TESS FFI cutout (15 × 15 pixels) in the region of NGTS-13 (TIC 454069765; T = 12.11) from which the TESS lightcurve was generated. The red boxes indicate the target pixel aperture mask used to extract the photometry, and the white boxes without a border are the pixels used to determine the sky background. The red star indicates the position of NGTS-13. The yellow crosses indicate TIC 454069767 (ΔT = 1.68, 22.5″ west) and TIC 454069772 (ΔT = 0.6, 57″ southeast). Blue plus signs denote the positions of stars in Gaia DR2 within 1′ of NGTS-13. |
In the text |
![]() |
Fig. 2 Photometry observations of NGTS-13. Top: NGTS observations (in 30-min bins for visual purposes), with the EXOFASTv2 model (see Sect. 3.2) in red. Middle: TESS FFI observations after the de-trending described in Sect. 2.2 was applied, with the EXOFASTv2 model in red. Bottom: NGTS in 30-min bins and TESS FFI photometry phased to the 4.12 day period of NGTS-13b. The NGTS data are in 30-min bins and then phased to the period. The red lines show the EXOFASTv2 model. |
In the text |
![]() |
Fig. 3 CORALIE RVs for NGTS-13 with the relative RV offset γrel subtracted (Table 4) and phased to the 4.12 day period of NGTS-13b. The red line shows the Keplerian fit for the planet from EXOFASTv2 (see Sect. 3.2). |
In the text |
![]() |
Fig. 4 Spectral energy distribution model (black line) of NGTS-13 from the EXOFASTv2 fit with broadband averages (blue circles)and broadband measurements (red). The error bars in wavelength denote the bandwidth of the corresponding filter, and the error bars in flux denote the measurement uncertainty. |
In the text |
![]() |
Fig. 5 Stellar mass and age posterior distributions from EXOFASTv2. These distributions show the relative probabilitybetween the two peaks of the bimodal distributions, which is approximately 60 to 40% in favor of a more massive, younger system. We split the stellar mass distribution in the valley at 1.25 M⊙ to create the two solutions presented in Table 4. For our final solution, we adopt the median value (vertical black lines) and upper and lower boundaries (dashed vertical lines) of the 68% confidence intervals we computed, as describedin Sect. 3.2.1. |
In the text |
![]() |
Fig. 6 [Fe/H] and mass relationship for giant planets (0.5–13 MJup) with percentage errors <10% for both mass and radius and a defined [Fe/H] in the NASA Exoplanet Archive2 (i.e., a total of 137 previously known planets, including 17 with masses greater than 4 MJup). |
In the text |
![]() |
Fig. 7 Radius (top) and density (bottom) as a function of mass for transiting brown dwarfs and giant planets (0.5–13 MJup) with percentage errors <10% for both mass and radius in the NASA Exoplanet Archive3. Transiting brown dwarfs – of which there are a total of 16 with well-defined masses and radii – are from the literature summary found in Mireles et al. (2020). The stars denote NGTS-13b, and the vertical dashed lines mark masses of 4 MJup and 13 MJup (the latter being the approximate lower mass limit for brown dwarfs). The colors of the symbols correspond to equilibrium temperatures in the top plot and host star masses in the bottom plot. The downward trend in maximum radii with increasing mass exhibits the effect of stronger surface gravity on more massive objects, which is clearly displayed by the linear trend in density. In the top plot, we only display planets that also have an equilibrium temperature in the archive: a total of 133 previously known planets, including 14 with masses greater than 4 MJup. |
In the text |
![]() |
Fig. 8 Posterior distribution of the eccentricity from our EXOFASTv2 MCMC analysis, including all chains and steps of the distribution. |
In the text |
![]() |
Fig. 9 Eccentricity-period relationship for transiting brown dwarfs and giant planets (0.5–13 MJup) with percentage errors <10% for both mass and radius and a defined eccentricity in the NASA Exoplanet Archive1: a total of 129 previously known planets, including 17 with masses greater than 4 MJup. Transiting brown dwarfs are from the literature summary found in Mireles et al. (2020), for a total of 16 with well-defined masses and radii. |
In the text |
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.