Dust survival rates in clumps passing through the Cas A reverse shock I: results for a range of clump densities
Florian Kirchschlager, Franziska D. Schmidt, M. J. Barlow, Erica L., Fogerty, Antonia Bevan, Felix D. Priestley

TL;DR
This study uses hydrodynamic simulations to evaluate dust survival in supernova ejecta, revealing that a significant fraction of dust can survive the reverse shock depending on initial grain sizes and gas densities, with implications for dust contribution to the interstellar medium.
Contribution
First detailed modeling of dust destruction in supernova remnants considering grain size, density, and grain-grain collisions, using new post-processing code Paperboats.
Findings
Up to 30% of carbon dust survives in high-density regions.
Silicate grains of 10-30 nm survive up to 40% in certain conditions.
Surviving dust redistributes into a two-component size distribution.
Abstract
The reverse shock in the ejecta of core-collapse supernovae is potentially able to destroy newly formed dust material. In order to determine dust survival rates, we have performed a set of hydrodynamic simulations using the grid-based code AstroBEAR in order to model a shock wave interacting with clumpy supernova ejecta. Dust motions and destruction rates were computed using our newly developed external, post-processing code Paperboats, which includes gas drag, grain charging, sputtering and grain-grain collisions. We have determined dust destruction rates for the oxygen-rich supernova remnant Cassiopeia A as a function of initial grain sizes and clump gas density. We found that up to 30 % of the carbon dust mass is able to survive the passage of the reverse shock if the initial grain size distribution is narrow with radii around ~10 - 50 nm for high gas densities, or with radii around…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
