Transient Sulfate Aerosols as a Signature of Exoplanet Volcanism

Authors: Amit Misra, Joshua Krissansen-Totton, Matthew C. Koehler, Steven Sholes

arXiv: 1504.04629v1 - DOI (astro-ph.EP)
69 pages, 4 figures, Accepted to Astrobiology 4/10/2015

Abstract: Geological activity is thought to be important for the origin of life and for maintaining planetary habitability. We show that transient sulfate aerosols could be a signature of exoplanet volcanism, and therefore a geologically active world. A detection of transient aerosols, if linked to volcanism, could thus aid in habitability evaluations of the exoplanet. On Earth, subduction-induced explosive eruptions inject SO2 directly into the stratosphere, leading to the formation of sulfate aerosols with lifetimes of months to years. We demonstrate that the rapid increase and gradual decrease in sulfate aerosol loading associated with these eruptions may be detectable in transit transmission spectra with future large-aperture telescopes, such as the James Webb Space Telescope (JWST) and European Extremely-Large Telescope (E-ELT) for a planetary system at a distance of 10 pc, assuming an Earth-like atmosphere, bulk composition, and size. Specifically, we find that a S/N of 12.1 and 7.1 could be achieved with E-ELT (assuming photon-limited noise) for an Earth-analog orbiting a Sun-like star and M5V star, respectively, even without multiple transits binned together. We propose that the detection of this transient signal would strongly suggest an exoplanet volcanic eruption, if potential false positives such as dust storms or bolide impacts can be ruled out. Furthermore, because scenarios exist in which O2 can form abiotically in the absence of volcanic activity, a detection of transient aerosols that can be linked to volcanism, along with a detection of O2, would be a more robust biosignature than O2 alone.

Submitted to arXiv on 17 Apr. 2015

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.