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What Are the Next Steps in Exoplanet Research?by@exoplanetology
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What Are the Next Steps in Exoplanet Research?

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Several new interesting questions and opportunities for more detailed studies arise from this work. First of all, we plan to work on a study that will take advantage of the model selection potential that Bayesian retrievals have to offer, for example by comparing retrievals including and excluding non-retrieved parameters (e.g. the CO abundance). We are also performing retrievals assuming various cloud models (Konrad et al., in prep.).
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This paper is available on arxiv under CC 4.0 license.

Authors:

(1) Eleonora Alei, ETH Zurich, Institute for Particle Physics & Astrophysics & National Center of Competence in Research PlanetS;

(2) Björn S. Konrad, ETH Zurich, Institute for Particle Physics & Astrophysics & National Center of Competence in Research PlanetS;

(3) Daniel Angerhausen, ETH Zurich, Institute for Particle Physics & Astrophysics, National Center of Competence in Research PlanetS & Blue Marble Space Institute of Science;

(4) John Lee Grenfell, Department of Extrasolar Planets and Atmospheres (EPA), Institute for Planetary Research (PF), German Aerospace Centre (DLR)

(5) Paul Mollière, Max-Planck-Institut für Astronomie;

(6) Sascha P. Quanz, ETH Zurich, Institute for Particle Physics & Astrophysics & National Center of Competence in Research PlanetS;

(7) Sarah Rugheimer, Department of Physics, University of Oxford;

(8) Fabian Wunderlich, Department of Extrasolar Planets and Atmospheres (EPA), Institute for Planetary Research (PF), German Aerospace Centre (DLR);

(9) LIFE collaboration, www.life-space-mission.com.

Abstract & Introduction

Methods

Results

Discussion

Conclusions

Next Steps & References

Appendix A: Scattering of terrestrial exoplanets

Appendix B: Corner Plots

Appendix C: Bayes’ factor analysis: other epochs

Appendix D: Cloudy scenarios: additional figures

6. Next Steps

Several new interesting questions and opportunities for more detailed studies arise from this work. First of all, we plan to work on a study that will take advantage of the model selection potential that Bayesian retrievals have to offer, for example by comparing retrievals including and excluding non-retrieved parameters (e.g. the CO abundance). We are also performing retrievals assuming various cloud models (Konrad et al., in prep.).


Retrievals of hazy planets (see e.g. Arney et al. 2016), as well as ocean worlds, might also help us further quantify the science potential of LIFE for a variety of different planet types.


Another interesting study would be to increase S/N and R to even higher values. This will not only evaluate the extreme limits of a concept like LIFE, but also help us better understand if retrievals are limited by R rather than S/N (e.g. due to unresolved narrow features at low R).


It would also be useful to compare different R-S/N combinations, this time fixing the observing time.


This would help us quantify the best R-S/N combination needed to optimize the characterization of a terrestrial atmosphere. Further work is needed to optimize the yield in the characterization phase of the LIFE mission concept.


The estimates of the observation time needed to establish knowledge about the habitability and the presence of biologically relevant molecules in the atmosphere that we derived here are a crucial piece of information for these follow-up studies.


In this work, we only used simulated data obtained with the LIFE mission. However, in the future there will likely be more information available to each system and planet. Therefore, it will be important to put this study in context with other observations.


For instance, joint retrievals of reflected light data obtained with LUVOIR/HabEx at optical/near-infrared wavelengths and thermal emission spectra as obtained by LIFE would provide useful insight on the synergies between the various missions.


One of the most important open questions regarding the ultimate goal of detecting extrasolar life will require to put our results in context with life detection frameworks (e.g. Green et al. 2021; Catling et al. 2018; Walker et al. 2018). Our ongoing retrieval efforts could be useful for the fine-tuning of such frameworks.


These, in turn, would provide insight on the meaning and the likelihood of a potential biosignature detection, which would allow us to infer and justify the presence of life forms on another planet.


Acknowledgements. This work has been carried out within the framework of the National Center of Competence in Research PlanetS supported by the Swiss National Science Foundation. S.P.Q. and E.A. acknowledge the financial support from the SNSF. P.M. acknowledges support from the European Research Council under the European Union’s Horizon 2020 research and innovation program under grant agreement No. 832428. J.L.G. thanks ISSI Team 464 for useful discussions.


Author contributions. E.A. carried out the analyses, created the figures, and wrote the bulk part of the manuscript. B.S.K. and D.A. wrote part of the manuscript. S.P.Q. initiated the project, guided the project and wrote part of the manuscript.


All authors discussed the results and commented on the manuscript.


Software. This research made use of: Astropy[7] , a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013, 2018); Matplotlib[8] (Hunter 2007); pandas (pandas development team 2020); seaborn [9] .

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[7] http://www.astropy.org


[8] https://matplotlib.org/3.1.1/index.html


[9] https://seaborn.pydata.org


This paper is available on arxiv under CC 4.0 license.