Rave: A non-parametric method for recovering the surface brightness and height profiles of edge-on debris disks

Authors: Yinuo Han, Mark C. Wyatt, Luca Matra

arXiv: 2202.04475v1 - DOI (astro-ph.EP)
16 pages, 11 figures, accepted for publication in MNRAS
License: CC BY-NC-SA 4.0

Abstract: Extrasolar analogues of the Solar System's Kuiper belt offer unique constraints on outer planetary system architecture. Radial features such as the sharpness of disk edges and substructures such as gaps may be indicative of embedded planets within a disk. Vertically, the height of a disk can constrain the mass of embedded bodies. Edge-on debris disks offer a unique opportunity to simultaneously access the radial and vertical distribution of material, however recovering either distribution in an unbiased way is challenging. In this study, we present a non-parametric method to recover the surface brightness profile (face-on surface brightness as a function of radius) and height profile (scale height as a function of radius) of azimuthally symmetric, edge-on debris disks. The method is primarily designed for observations at thermal emission wavelengths, but is also applicable to scattered light observations under the assumption of isotropic scattering. By removing assumptions on underlying functional forms, this algorithm provides more realistic constraints on disk structures. We also apply this technique to ALMA observations of the AU Mic debris disk and derive a surface brightness profile consistent with estimates from parametric approaches, but with a more realistic range of possible models that is independent of parametrisation assumptions. Our results are consistent with a uniform scale height of 0.8 au, but a scale height that increases linearly with radius is also possible.

Submitted to arXiv on 09 Feb. 2022

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.