Exoplanet Aeronomy: A Case Study of WASP-69 b's Variable Thermosphere

Copyright and License

© 2024. 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.

Acknowledgement

We appreciate the detailed peer-reviewing efforts from Gloria Guilluy and a second anonymous referee—their insightful reports helped us to improve the scientific content of this manuscript. We thank the Palomar Observatory telescope operators, support astronomers, hospitality and administrative staff, and directorate for their support. We are especially grateful to Tom Barlow, Carolyn Heffner, Diana Roderick, Kathleen Koviak, Isaac Wilson, Jennifer Milburn, and Andy Boden.

We are grateful to the Swift team for an expedient approval of our ToO request and for executing the observations. We acknowledge the use of public data from the Swift data archive. Parts of the results shown are based on archival observations obtained by XMM-Newton, an ESA science mission with instruments and contributions directly funded by ESA Member States and NASA. This work 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 (NASA) under the Exoplanet Exploration Program.

We appreciate that Romain Allart, Lisa Nortmann, Dakotah Tyler, and Gloria Guilluy shared their reduced spectra for WASP-69 b from SPIRou, CARMENES, and Keck, respectively—having these data allowed us to compare WIRC light curves to spectrally resolved results.

We benefited from useful conversations with Greg Laughlin, Sarbani Basu, Emma Louden, Christopher Lindsay, Earl Bellinger, Sam Cabot, Darryl Seligman, Quang Tran, Leonardo dos Santos, Fei Dai, Raissa Estrela, Jessie Christiansen, Elena Gallo, Parke Lloyd, Ethan Schreyer, Morgan Saidel, Kim Paragas, and Jonathan Gomez-Barrientos. Parts of this manuscript were checked for typographical errors and edited for clarity with assistance from OpenAI's GPT-4.

W.G.L. gratefully acknowledges support from the Department of Defense's National Defense Science & Engineering Graduate (NDSEG) Fellowship and Yale University's John F. Enders Fellowship for dissertation research travel. W.G.L. also thanks the LSST-DA Data Science Fellowship Program, which is funded by LSST-DA, the Brinson Foundation, and the Moore Foundation; his participation in the program has benefited this work. A.D.F. acknowledges funding from NASA through the NASA Hubble Fellowship grant No. HST-HF2-51530.001-A awarded by STScI.

Software References

numpy (Harris et al. 2020), pandas (McKinney et al. 2011), scipy (Virtanen et al. 2020), matplotlib (Hunter 2007), astropy (Astropy Collaboration et al. 2013, 2018, 2022), photutils (Bradley et al. 2023), pymc3 (Salvatier et al. 2016), theano (Theano Development Team 2016), arviz (Kumar et al. 2019), exoplanet (Foreman-Mackey et al. 2021a, 2021b), ldtk (Husser et al. 2013; Parviainen & Aigrain 2015), starry (Luger et al. 2019), celerite2 (Foreman-Mackey et al. 2017; Foreman-Mackey 2018), pymc3 (Salvatier et al. 2016), corner (Foreman-Mackey 2016), p-winds (dos Santos et al. 2022), HEASoft (Nasa High Energy Astrophysics Science Archive Research Center (Heasarc), 2014), Xspec (Arnaud 1996)