A New View of Rocky Exoplanets

Rocky exoplanets show a diversity of characteristics from their mean density, irradiation, and atmospheric composition. Our Solar System provides several rocky objects and a reference spectra catalog, but the critical input for understanding a rocky planet’s evolution will come from observations of a sample of known rocky extrasolar planets filling in the pattern. The snapshots of evolutionary stages for rocky exoplanets at different positions throughout and beyond the Habitable Zone we have just started to observe will probe how their position and age influence their diversity and observable spectra. However, spectroscopy remains time-intensive, and therefore, initial characterization is critical to prioritize targets. Machine Learning algorithms hold a promise to initially identify water and biota on the surface of exoplanets using broad-band filter photometry to aid prioritization of targets for time-intense follow-up observations. I will highlight key results and challenges to interpreting spectra of rocky exoplanets through this wide parameter space. Telescopes like JWST now and PLATO, Ariel, and ELTs in the near future will probe the parameter space, and a selection of targets that can inform our understanding of rocky planets will be critical to maximizing their observation time and impact of these observations.

References:

Follow the water: finding water, snow, and clouds on terrestrial exoplanets with photometry and machine learning, Pham D. & Kaltenegger L., MNRAS, V513, L72,1, 2021.

Color classification of Earth-like planets with machine, Pham D. & Kaltenegger L., MNRAS, 504, 4, 2021.

A Catalog of Spectra, Albedos, and Colors of Solar System Bodies for Exoplanet Comparison, Madden J.H. & Kaltenegger L., Astrobiology, 18,12, 2018.

How to characterize a Habitable Planet, Kaltenegger L., Annual Review of Astronomy and Astrophysics 55, 433-485, 2017.