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Project 4.2: Observational signatures of habitability

To assess the potential habitability of terrestrial exoplanets we will primarily rely on investigating the objects’ atmosphere and surface via photometric or spectroscopic observations (see, e.g., Kaltenegger 2017 for a review). While prime targets in the immediate solar vicinity have already been detected (e.g., Proxima b, Ross 128 b), in-depth characterisation of these objects is not feasible with current technologies. However, the next generation of ground- and space-based observatories – or upgraded versions of existing facilities – will at least offer a first step in this direction (e.g., Quanz et al. 2015, Snellen et al. 2015, Lovis et al. 2017, Kasper et al. 2013, Kammerer & Quanz 2018, Morley et al. 2017) and the direct involvement of PlanetS members in future ELT instruments (METIS and HiRES) is a key asset. In addition to classical high-contrast thermal infrared imaging and spectroscopy, one of the most promising future techniques for atmospheric characterisation of habitable exoplanets is high-contrast, high-resolution (HCHR) spectroscopy, which combines an (extreme) AO system with a high-resolution (R ~ 100 000) spectrograph. Thanks to AO, the planet is spatially separated from the star, the contrast is enhanced, and the relative Doppler shift between stellar and planetary light is used to measure the planet spectrum (Snellen et al. 2014, 2015). Exoplanets of all kinds (Jupiters to Earths, cold to warm) orbiting very nearby stars will be amenable to characterisation with the HCHR technique in reflected light.

To cross the observational frontier related to habitability and make a first step towards characterising nearby terrestrial planets with upcoming instruments, it is essential that technical developments and modelling efforts go hand in hand. Only then one can quantify what different observational approaches can deliver and which robust science requirements for future – and potentially even more ambitious – projects and missions can be formulated. Hence, we propose work packages linking models of terrestrial exoplanets (atmospheres / interiors) or observations of the only known habitable planet – Earth – with (future) instruments aiming at the direct detection of habitable exoplanets.

Work-package 1: High-resolution spectra of habitable exoplanets in reflected light

In this work package, we will simulate realistic high-resolution, reflected-light spectra of various kinds of temperate planets and study the detectability of their spectral features from an observational point of view. The parameter space to explore is vast and includes various molecular absorbers in the gas phase, different kinds of clouds and their impact on the planet albedo, and several surface compositions such as liquid water, rocks, and ice caps. Additional phenomena such as ocean glint and Raman scattering may be detectable in the reflected-light spectrum and will be studied as well.

Work-package 2: The information content of terrestrial planet spectra

We will implement a spectral retrieval code for terrestrial exoplanets. While being the standard approach for the characterisation of Hot Jupiters (e.g., Line et al. 2016, Oreshenko et al. 2017), this technique has yet to be applied systematically to terrestrial exoplanets. First steps in this direction were recently presented in Feng et al. (2018) analysing the power of reflected light observations with missions like HabEX or LUVOIR. Spectral retrieval frameworks allow us to constrain – in a statistical sense – the individual parameter of an underlying model by ‘letting the data speak’. Such an approach needs to be the basis for combining our knowledge (and ignorance) of individual parameters to arrive at a statistical quantitative conclusion regarding an exoplanet’s habitability. Using the Earth’s atmosphere as a starting point, but then varying (1) the spectral coverage, (2) the spectral resolution, (3) the signal-to-noise, (4) the cloud coverage, (5) the host star type, and (6) the composition, we will assess the impact of these parameters on the final conclusions from our retrieval code. As such this work will deliver a key piece for a statistical framework assessing habitability (see Catling et al. 2017 for a recent proposal) and will also inform us about the knowledge to be gained from future observations with upcoming facilities. This work is led by NCCR post-doc Dr. Eleonora Alei.

Work-package 3: From stellar abundances to planetary interiors

NCCR postdoc Dr. Haiyang Wang (ETHZ; 50% in-kind / 50% NCCR) joined the team on 15 October 2019. He has a multi-​disciplinary background in planetary astrophysics, cosmochemistry and geophysics and uses stellar spectra to derive stellar abundances which serve as input to model the composition and interior structure of potential terrestrial planets orbiting these stars. A key ingredient in his model is a devolatilisation prescription of material as a function of stellar distance, which is benchmarked with Solar System objects, in order to correct for the abundances of volatile elements (in particular Oxygen). Work aiming at refining the devolatilisation model and the interior structure mode is ongoing; first results are expected in the first half of 2020.

Work-package 4: Earth as an exoplanet

Jean-Noel Mettler (shared ETHZ+UniZ; 75% in-kind / 25% NCCR) started his PhD on 1 September 2019. He is using Earth observation data to understand how the only known habitable (and inhabited) planet – Earth – appears from ‘the outside’. A first paper based in mid-infrared data obtained with the MODIS instrument onboard the Aqua satellite over a timeline of 15 years has just been submitted to A&A. Key findings are: (1) that the Earth spectrum shows – as one might expect – significant variations in its thermal flux and in the strength of absorption lines depending on the dominant surface type (e.g., ocean vs. land vs. poles); there is no such a thing as the Earth spectrum;  (2) that depending on the observing band and time sampling one might be able to deduce the existence of seasons on an exoplanet based on thermal emission data.

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