PLATON II: New Capabilities and a Comprehensive Retrieval on HD 189733b Transit and Eclipse Data
Abstract
Recently, we introduced PLanetary Atmospheric Tool for Observer Noobs (PLATON), a Python package that calculates model transmission spectra for exoplanets and retrieves atmospheric characteristics based on observed spectra. We now expand its capabilities to include the ability to compute secondary eclipse depths. We have also added the option to calculate models using the correlated-k method for radiative transfer, which improves accuracy without sacrificing speed. Additionally, we update the opacities in PLATON—many of which were generated using old or proprietary line lists—using the most recent and complete public line lists. These opacities are made available at R = 1000 and R = 10,000 over the 0.3–30 μm range, and at R = 375,000 in select near-IR bands, making it possible to utilize PLATON for ground-based high-resolution cross-correlation studies. To demonstrate PLATON's new capabilities, we perform a retrieval on published Hubble Space Telescope (HST) and Spitzer transmission and emission spectra of the archetypal hot Jupiter HD 189733b. This is the first joint transit and secondary eclipse retrieval for this planet in the literature, as well as the most comprehensive set of both transit and eclipse data assembled for a retrieval to date. We find that these high signal-to-noise data are well matched by atmosphere models with a C/O ratio of 0.66^(+0.05)_(−0.09) and a metallicity of 12⁺⁸₋₅ times solar where the terminator is dominated by extended nanometer-sized haze particles at optical wavelengths. These are among the smallest uncertainties reported to date for an exoplanet, demonstrating both the power and the limitations of HST and Spitzer exoplanet observations.
Additional Information
© 2020 The American Astronomical Society. Received 2020 January 30; revised 2020 June 29; accepted 2020 June 30; published 2020 August 10. M.Z. acknowledges Plato (Greek:ΠΛATΩN) for his clear and thought-provoking dialogs, which ought to be exemplars of good writing for academics everywhere. We also thank Michael R. Line for helpful advice. Support for this work was provided by the HST GO programs 13431, 13665, and 14260. Software: numpy (van der Walt et al. 2011), scipy (Virtanen et al. 2020), matplotlib (Hunter 2007), emcee (Foreman-Mackey et al. 2013), dynesty (Speagle 2020), corner (Foreman-Mackey 2016), nose, Travis-CI.Attached Files
Published - Zhang_2020_ApJ_899_27.pdf
Submitted - 2004.09513.pdf
Files
Name | Size | Download all |
---|---|---|
md5:b7af1f41e6eb638f6d207dc627f6d46d
|
6.2 MB | Preview Download |
md5:e7e93bf14bc917810c84bf33b76fd4f4
|
2.7 MB | Preview Download |
Additional details
- Eprint ID
- 104131
- Resolver ID
- CaltechAUTHORS:20200629-130053098
- NASA
- HST-GO-13431
- NASA
- HST-GO-13665
- NASA
- HST-GO-14260
- Created
-
2020-06-29Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field
- Caltech groups
- Astronomy Department, Division of Geological and Planetary Sciences (GPS)