Detecting planetary geochemical cycles on exoplanets: Atmospheric signatures and the case of SO2

Authors: L. Kaltenegger, D. Sasselov

Astrophys.J.708:1162-1167,2010
arXiv: 0906.2193v2 - DOI (astro-ph.EP)
9 pages, 6 figures, ApJ accepted - detailed discussion added

Abstract: We study the spectrum of a planetary atmosphere to derive detectable features in low resolution of different global geochemical cycles on exoplanets - using the sulphur cycle as our example. We derive low resolution detectable features for first generation space- and ground- based telescopes as a first step in comparative planetology. We assume that the surfaces and atmospheres of terrestrial exoplanets (Earth-like and super-Earths) will most often be dominated by a specific geochemical cycle. Here we concentrate on the sulphur cycle driven by outgassing of SO2 and H2S followed by their transformation to other sulphur-bearing species which is clearly distinguishable from the carbon cycle which is driven by outgassing of CO2. Due to increased volcanism, the sulphur cycle is potentially the dominant global geochemical cycle on dry super-Earths with active tectonics. We calculate planetary emission, reflection and transmission spectrum from 0.4 to 40 micrometer with high and low resolution to assess detectable features using current and Archean Earth models with varying SO2 and H2S concentrations to explore reducing and oxidizing habitable environments on rocky planets. We find specific spectral signatures that are observable with low resolution in a planetary atmosphere with high SO2 and H2S concentration. Therefore first generation space and ground based telescopes can test our understanding of geochemical cycles on rocky planets and potentially distinguish planetary environments dominated by the carbon and sulphur cycle.

Submitted to arXiv on 11 Jun. 2009

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.