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Published in

Oxford University Press, Monthly Notices of the Royal Astronomical Society, 3(516), p. 3167-3174, 2022

DOI: 10.1093/mnras/stac2536

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High-resolution spectral models of TRAPPIST-1e seen as a Pale Blue Dot for ELT and JWST observations

Journal article published in 2022 by Zifan Lin ORCID, Lisa Kaltenegger ORCID
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Postprint: archiving allowed
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Data provided by SHERPA/RoMEO

Abstract

ABSTRACT Rocky exoplanets orbiting in the habitable zone (HZ) of nearby M dwarfs provide unique opportunities for characterizing their atmospheres and searching for biosignature gases. TRAPPIST-1e, a temperate Earth-sized exoplanet in the HZ of a nearby M dwarf, is arguably the most favourable target for ground- and space-based atmospheric characterization by the extremely large telescopes (ELTs) and the James Webb Space Telescope. To inform future observations in reflected and emitted lights using these upcoming telescopes, we simulate the high-resolution reflection and emission spectra for TRAPPIST-1e for both modern and prebiotic Earth-like atmospheric compositions. To demonstrate the effects of wavelength-dependent albedo on climate and spectra, we further simulate five albedo scenarios for each atmospheric composition: cloudy modern Earth-like, cloud-free modern Earth-like, cloudy ocean planet, 100 per cent cloudy planet, and wavelength-independent albedo of 0.31. We use the recent Mega-MUSCLES spectral energy distribution of TRAPPIST-1 for our models. We show that the O2 + CH4 and O3 + CH4 biosignature pairs as well as climate indicators (CO2 and H2O) show features in both high-resolution reflection and emission spectra of TRAPPIST-1e that the ELTs can search for. Our high-resolution data base for modern and prebiotic Earth TRAPPIST-1e models with various surface compositions and cloud distributions provides a tool for observers to train retrieval algorithms and plan observation strategies to characterize this potentially habitable world.