Discovering the Surface Composition of TNOs (DiSCo-TNOs) with the James Webb Space Telescope

Mario De Pra,Brittany Harvison,Michele Bannister,Dale Cruikshank,Nuno Peixinho,Josh Emery,Charles Schambeau,Aurélie Guilbert-Lepoutre,Noemi Pinilla-Alonso,Yvonne Pendleton,Thomas Muller,Bryan Holler,Rosario Brunetto,Vania Lorenzi,John Stansberry,Ana Carolina Souza Feliciano

doi:10.5194/epsc2022-1196

Abstract

&lt;p&gt;The discovery of trans-Neptunian objects (TNOs) marked an important milestone in the understanding of the outer Solar System. Due to their environmental conditions, these objects could preserve the most pristine materials that were present on the protoplanetary disk. Studies focused on understanding TNOs physical and dynamical properties can be used to probe planetary formation processes and the subsequent solar system dynamical evolution that followed the formation era.&lt;br&gt;&lt;br&gt;Nowadays, above 3,000 TNOs have been detected, including four large ones that receive the official designation of dwarf-planets. Analysis of TNOs revealed a compositionally and dynamically diverse population. However, despite all the progress in the last decades, much is still unknown about the composition of the TNOs.&lt;br&gt;&lt;br&gt;The recently launched James Webb Space Telescope (launched on December 25, 2021) will provide a powerful tool to investigate the TNOs surface composition, where all prior instrumentation has fallen short. The NIRSpec instrument onboard JWST will provide high-quality data that will surpass the quality of the data available by orders of magnitude. DiSCo-TNOs, lead by the Florida Space Institute, is the only large program approved by JWST for the study of the Solar System. With it, we aim to assess the relative ratio of water ice, complex organics, silicates, and volatiles on the surface of a large sample of TNOs. This information is vital to improving models of the formation of our Solar System and other planetary systems. In this talk we present the scope of the DiSCo program, and the tools that are being developed to extract the maximal information from the data. We pay special attention to the compositional modeling technique that uses an implementation of a nested sampling algorithm for Bayesian inference of the abundances and grain sizes distribution of the materials present on TNOs surfaces.&lt;/p&gt;&lt;div&gt;&amp;#160;&lt;/div&gt;&lt;div&gt;&amp;#160;&lt;/div&gt;

Full Text