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Published May 1, 2022 | Published + Accepted Version
Journal Article Open

Cloudy and Cloud-free Thermal Phase Curves with PICASO: Applications to WASP-43b

Abstract

We present new functionality within PICASO, a state-of-the-art radiative transfer model for exoplanet and brown dwarf atmospheres, by developing a new pipeline that computes phase-resolved thermal emission (thermal phase curves) from three-dimensional (3D) models. Because PICASO is coupled to Virga, an open-source cloud code, we are able to produce cloudy phase curves with different sedimentation efficiencies (f_(sed)) and cloud condensate species. We present the first application of this new algorithm to hot Jupiter WASP-43b. Previous studies of the thermal emission of WASP-43b from Kataria et al. found good agreement between cloud-free models and dayside thermal emission, but an overestimation of the nightside flux, for which clouds have been suggested as a possible explanation. We use the temperature and vertical wind structure from the cloud-free 3D general circulation models of Kataria et al. and post-process it using PICASO, assuming that clouds form and affect the spectra. We compare our models to results from Kataria et al., including Hubble Space Telescope Wide-Field Camera 3 (WFC3) observations of WASP-43b from Stevenson et al. In addition, we compute phase curves for Spitzer at 3.6 and 4.5 μm and compare them to observations from Stevenson et al. We are able to closely recover the cloud-free results, even though PICASO utilizes a coarse spatial grid. We find that cloudy phase curves provide much better agreement with the WFC3 and Spitzer nightside data, while still closely matching the dayside emission. This work provides the community with a convenient, user-friendly tool to interpret phase-resolved observations of exoplanet atmospheres using 3D models.

Additional Information

© 2022. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 2021 July 31; revised 2022 March 11; accepted 2022 April 6; published 2022 May 6. Upon acceptance of the manuscript, we plan to post the phase curve code to GitHub as a publicly available addition to PICASO. We thank the anonymous reviewer for their helpful suggestions. This work was supported by the NASA Jet Propulsion Laboratory Education Office, Caltech Student-Faculty Programs, and ANRE Technologies Inc. through the JPL Summer Internship Program and the JPL Year-Round Internship Program. We thank Valerie Arriero for computing the 20 × 20 and 40 × 40 resolution phase curves. N.R.B thanks Xi Zhang for the advice and the useful discussions. N.B. acknowledges support from the NASA Astrophysics Division. Software: picaso (Batalha et al. 2022), virga (Batalha et al. 2020a), numba (Lam et al. 2015), Matplotlib (Hunter 2007), pandas (McKinney 2010), pickle (Van Rossum 2020), bokeh (Bokeh Development Team 2014), NumPy (van der Walt 2011), SciPy (Virtanen et al. 2020), IPython (Perez and Granger 2007), Jupyter (Kluyver et al. 2016), PySynphot (STScI Development Team 2013), sqlite3 (sqlite3 Development Team 2019).

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Published - Robbins-Blanch_2022_ApJ_930_93.pdf

Accepted Version - 2204.03545.pdf

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Additional details

Created:
August 22, 2023
Modified:
October 24, 2023