Aluminium oxide in the atmosphere of hot Jupiter WASP-43b
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We have conducted a re-analysis of publicly available Hubble Space Telescope Wide Field Camera 3 (HST WFC3) transmission data for the hot-Jupiter exoplanet WASP-43b, using the Bayesian retrieval package Tau-REx. We report evidence of AlO in transmission to a high level of statistical significance (>5σ in comparison to a flat model, and 3.4σ in comparison to a model with H2O only). We find no evidence of the presence of CO, CO2, or CH4 based on the available HST WFC3 data or on Spitzer IRAC data. We demonstrate that AlO is the molecule that fits the data to the highest level of confidence out of all molecules for which high-temperature opacity data currently exists in the infrared region covered by the HST WFC3 instrument, and that the subsequent inclusion of Spitzer IRAC data points in our retrieval further supports the presence of AlO. H2O is the only other molecule we find to be statistically significant in this region. AlO is not expected from the equilibrium chemistry at the temperatures and pressures of the atmospheric layer that is being probed by the observed data. Its presence therefore implies direct evidence of some disequilibrium processes with links to atmospheric dynamics. Implications for future study using instruments such as the James Webb Space Telescope are discussed, along with future opacity needs. Comparisons are made with previous studies into WASP-43b.
Chubb , K L , Min , M , Kawashima , Y , Helling , C & Waldmann , I 2020 , ' Aluminium oxide in the atmosphere of hot Jupiter WASP-43b ' , Astronomy and Astrophysics , vol. 639 , A3 . https://doi.org/10.1051/0004-6361/201937267
Astronomy and Astrophysics
Copyright © 2020 ESO. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1051/0004-6361/201937267.
DescriptionFunding: European Union’s Horizon 2020 Research and Innovation Programme, under Grant Agreement 776403, and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 758892, ExoAI.
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