Surface properties of the Kalliope-Linus system from ALMA and VLA data

Authors: Katherine de Kleer, Saverio Cambioni, Bryan Butler, Michael Shepard

arXiv: 2409.12364v1 - DOI (astro-ph.EP)
Accepted to PSJ

Abstract: The abundance and distribution of metal in asteroid surfaces can be constrained from thermal emission measurements at radio wavelengths, informing our understanding of planetesimal differentiation processes. We observed the M-type asteroid (22) Kalliope and its moon Linus in thermal emission at 1.3, 9, and 20 mm with the Atacama Large Millimeter/submillimeter Array (ALMA) and the Karl G. Jansky Very Large Array (VLA) over most of Kalliope's rotation period. The 1.3 mm data provide ~30 km resolution on the surface of Kalliope, while both the 1.3 and 9 mm data resolve Linus from Kalliope. We find a thermal inertia for Kalliope of 116$^{+326}_{-91}$ J m$^{-2}$ s$^{-0.5}$ K$^{-1}$ and emissivities of 0.65$\pm$0.02 at 1.3 mm, 0.56$\pm$0.03 at 9 mm, and 0.77$\pm$0.02 at 20 mm. Kalliope's millimeter wavelength emission is suppressed compared to its centimeter wavelength emission, and is also depolarized. We measure emissivities for Linus of 0.73$\pm$0.04 and 0.85$\pm$0.17 at 1.3 and 9 mm respectively, indicating a less metal-rich surface composition for Linus. Spatial variability in Kalliope's emissivity reveals a region in the northern hemisphere with a high dielectric constant, suggestive of enhanced metal content. These results are together consistent with a scenario in which Linus formed from reaggregated ejecta from an impact onto a differentiated Kalliope, leaving Kalliope with a higher surface metal content than Linus, which is distributed heterogeneously across its surface. The low emissivity and lack of polarization suggest a reduced regolith composition where iron is in the form of metallic grains and constitutes ~25% of the surface composition.

Submitted to arXiv on 18 Sep. 2024

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.