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Detection of titanium oxide in the atmosphere of a hot Jupiter

Nature volume 549, pages 238–241 (2017)Cite this article

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Abstract

As an exoplanet transits its host star, some of the light from the star is absorbed by the atoms and molecules in the planet’s atmosphere, causing the planet to seem bigger; plotting the planet’s observed size as a function of the wavelength of the light produces a transmission spectrum1. Measuring the tiny variations in the transmission spectrum, together with atmospheric modelling, then gives clues to the properties of the exoplanet’s atmosphere. Chemical species composed of light elements—such as hydrogen, oxygen, carbon, sodium and potassium—have in this way been detected in the atmospheres of several hot giant exoplanets2,3,4,5, but molecules composed of heavier elements have thus far proved elusive. Nonetheless, it has been predicted that metal oxides such as titanium oxide (TiO) and vanadium oxide occur in the observable regions of the very hottest exoplanetary atmospheres, causing thermal inversions on the dayside6,7. Here we report the detection of TiO in the atmosphere of the hot-Jupiter planet WASP-19b. Our combined spectrum, with its wide spectral coverage, reveals the presence of TiO (to a confidence level of 7.7σ), a strongly scattering haze (7.4σ) and sodium (3.4σ), and confirms the presence of water (7.9σ) in the atmosphere5,8.

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Figure 1: Light curves and models.
Figure 2: WASP-19b transmission spectrum.
Figure 3: Constituents detected in WASP-19b’s atmosphere.

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Acknowledgements

This work is based on observations made with the FORS2 instrument on the European Southern Observatory (ESO)’s VLT. We thank staff astronomers J. Anderson and J. Smoker for performing some of the observations. E.S. acknowledges support from the ESO through the studentship programme. R.J.M. and S.G. acknowledge financial support from the UK Science and Technology Facilities Council (STFC) towards their doctoral programmes. M.O. acknowledges research funding from the Deutsche Forschungsgemeinschaft (DFG), grant OS 508/1-1, as well as support from the Fundação para a Ciência e a Tecnologia (FCT) through national funds and from FEDER through COMPETE2020 from the following grants: UID/FIS/04434/2013 and POCI-01-0145-FEDER-007672; and PTDC/FIS-AST/1526/2014 and POCI-01-0145-FEDER-016886. We thank the Spanish Ministry of Education and Science (MEC; grants AYA2015-71718-R and ESP2015-65712-C5-5-R) for support during the development of this work. We also thank the referees for their comments, which improved the manuscript.

Author information

Authors and Affiliations

  1. European Southern Observatory, Alonso de Córdova 3107, Santiago, Chile

    Elyar Sedaghati & Henri M. J. Boffin

  2. Deutsches Zentrum für Luft- und Raumfahrt, Rutherfordstrasse 2, Berlin, 12489, Germany

    Elyar Sedaghati & Heike Rauer

  3. Zentrum für Astronomie und Astrophysik, TU Berlin, Hardenbergstrasse 36, Berlin, 10623, Germany

    Elyar Sedaghati & Heike Rauer

  4. Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge, CB3 0HA, UK

    Ryan J. MacDonald, Siddharth Gandhi & Nikku Madhusudhan

  5. Astrophysics Research Centre, School of Mathematics and Physics, Queens University Belfast, Belfast, BT7 1NN, UK

    Neale P. Gibson

  6. Institut für Astrophysik, Georg-August Universität Göttingen, Friedrich-Hund-Platz 1, Göttingen, 37077, Germany

    Mahmoudreza Oshagh

  7. Instituto de Astrofísica e Ciências do Espaço, Universidade do Porto, CAUP, Rua das Estrelas, Porto, 4150-762, Portugal

    Mahmoudreza Oshagh

  8. Instituto de Astrofísica de Andalucía, CSIC, Apartado 3004, Granada, 18080, Spain

    Antonio Claret

Authors
  1. Elyar Sedaghati
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Contributions

E.S. and H.M.J.B. led the scientific proposal, observational campaigns, data reduction and analysis up to the production of transmission spectra. R.J.M. conducted the atmospheric retrieval and S.G. generated the absorption cross-sections, both under the supervision of N.M., who planned and oversaw the atmospheric analyses and theoretical interpretation. N.P.G. wrote the python modules for the Gaussian process and the Monte Carlo Markov Chain analysis. M.O. analysed the impact of unocculted stellar active regions. A.C. calculated the theoretical limb-darkening coefficients for the specific bandpasses. H.R. provided feedback on the manuscript and is involved in the supervision of E.S. All authors contributed to writing the manuscript.

Corresponding author

Correspondence to Elyar Sedaghati.

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Reviewer Information Nature thanks K. Heng and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 Observations and reduction.

a, An example of a mask design used for MXU observations. The field of view of the FORS2 spectrograph is shown in blue, with the green lines indicating the two-chip detector mosaic. The grey shaded regions show the areas of the detectors used for recording the stellar spectra. In the instance shown, WASP-19 is the star in the upper-most slit. b, Top, an example of a two-dimensional spectrum, from the red data set, extracted from a frame taken with the FORS2 instrument. The final size of the extraction box and the regions used for sky subtraction are indicated. Bottom, the process of choosing the width of the extraction box, where the final value is shown as a dashed line and the grey shading represents the 1σ confidence limits. The exemplar frame used to produce these plots is selected at large seeing. W19, WASP-19; comp., comparison star.

Extended Data Figure 2 Spectra and light curves.

Top row, an example set of spectra for the target (WASP-19) and the chosen comparison star, for each observing run (blue, green and red). Counts are given in analogue-to-digital units (ADUs), read directly from the sum of values of charge-coupled-device (CCD) pixels. Middle row, light curves for the target and comparison stars for each data set, obtained through broadband integration of the series of spectra. Colours match those in the top row; values are normalized to the mean of the out-of-transit fluxes and shifted for clarity. The transit imprint from WASP-19b is clearly evident even in these raw light curves. Bottom row, differential broadband light curves (black data point) obtained simply by dividing the two light curves in the middle row. We also show our fitted transit model (blue curve) and the Gaussian process systematic noise model (red curve) with its 1σ (dark grey shading) and 3σ (light grey shading) confidence levels. The points below are the residuals of the two models, where the colours correspond to the fit that they represent. The green line shows the flux variations resulting from changes in seeing conditions, used as an input for our Gaussian process model. JD, Julian day.

Extended Data Figure 3 Spectrophotometric light curves for the blue data set.

Left, raw light curves produced from each of the narrow-band channels in the blue data set. Right, those light curves that have been corrected for the common-mode systematics. Our best-fit Gaussian process systematic noise models are shown as solid black lines, with the centre of the integration bin for each light curve given to the right of it in micrometres. All light curves have been shifted vertically for clarity.

Source data

Extended Data Figure 4 Spectrophotometric light curves for the green data set.

As for Extended Data Fig. 3 but for the green data set.

Source data

Extended Data Figure 5 Spectrophotometric light curves for the red data set.

As for Extended Data Figs 3 and 4 but for the red data set.

Source data

Extended Data Figure 6 Correlations.

Random samples drawn from the four MCMC simulations, for all the fitted parameters (see Methods for definitions), in modelling a broadband light curve (lower-left triangle) and a spectroscopic light curve (upper-right triangle). Both examples are from the blue data set. Mutual convergence of all independent chains is evident, as are the well documented degeneracies between the impact parameter (b) and the scaled semi-major axis (a/R*), and between the two coefficients of the limb-darkening law (c1 and c2). ηfwhm is the Gaussian process inverse length scale for ‘seeing’.

Extended Data Figure 7 Stellar activity impact.

a, Broadband light curve from the blue data set, modelled using an analytical model that includes a spot-crossing event by the planet. The new inferred planetary radius and the limb-darkening coefficients (the ‘transit parameters’) are shown, for which the offset to our previous results is substantially lower than the derived precision. This is because our systematic model accounts well for this anomaly. The inferred spot parameters are also shown. HJD, heliocentric Julian day; res, residuals. b, Dependence of spot contrast ratio on the observation wavelength, from which the spot temperature is determined using Planck’s law. The 1σ error bars are derived from a joint analysis of posterior probability distributions of the relative planetary radius, from the MCMC simulations. For reference, a spectrum of WASP-19 is plotted in light grey. The prior stellar photospheric temperature (T*) and the fitted spot temperature (T•) are also given. c, Comparison of transmission spectra in the blue data set, from red noise analysis and spot modelling; 1σ error bars were derived as above. The spot-analysis results (blue points) have been shifted by +0.01 μm to better distinguish between the two sets of results. Wavelength-dependent radius variations induced by the presence of spots with 20% filling factor (f) and temperature differences of 200 K, 600 K or 1,000 K are also plotted. LC, light curve; CMC, common mode corrected.

Extended Data Table 1 Observational information on the three sets of observational campaigns
Full size table
Extended Data Table 2 Transit parameters from broadband analysis of all three data sets
Full size table
Extended Data Table 3 Bayesian model comparison detections of WASP-19b's terminator chemistry and cloud properties
Full size table

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Sedaghati, E., Boffin, H., MacDonald, R. et al. Detection of titanium oxide in the atmosphere of a hot Jupiter. Nature 549, 238–241 (2017). https://doi.org/10.1038/nature23651

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