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Published December 1, 2022 | public
Journal Article

Using Anisotropies as a Forensic Tool for Decoding Supernova Remnants

Abstract

We present a method for analyzing supernova remnants (SNRs) by diagnosing the drivers responsible for structure at different angular scales. First, we perform a suite of hydrodynamic models of the Rayleigh–Taylor instability (RTI) as a supernova (SN) collides with its surrounding medium. Using these models we demonstrate how power spectral analysis can be used to attribute which scales in an SNR are driven by RTI and which must be caused by intrinsic asymmetries in the initial explosion. We predict the power spectrum of turbulence driven by RTI and identify a dominant angular mode that represents the largest scale that efficiently grows via RTI. We find that this dominant mode relates to the density scale height in the ejecta, and therefore reveals the density profile of the SN ejecta. If there is significant structure in an SNR on angular scales larger than this mode, then it is likely caused by anisotropies in the explosion. Structure on angular scales smaller than the dominant mode exhibits a steep scaling with wavenumber, possibly too steep to be consistent with a turbulent cascade, and therefore might be determined by the saturation of RTI at different length scales (although systematic 3D studies are needed to investigate this). We also demonstrate, consistent with previous studies, that this power spectrum is independent of the magnitude and length scales of perturbations in the surrounding medium and therefore this diagnostic is unaffected by "clumpiness" in the circumstellar medium.

Additional Information

We would like to thank the anonymous referee for the helpful review. Numerical calculations were performed on the Stampede2 supercomputer under allocations TG-PHY210027 & TG-PHY210035 provided by the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562 (Towns et al. 2014). P.D. acknowledges NSF support from grant AAG-2206299, and NASA support from grant 21-FERMI21-0034. D.M. acknowledges NSF support from grants PHY-1914448, PHY-2209451, AST-2037297, and AST-2206532. This research was supported in part by the National Science Foundation under grant No. NSF PHY-1748958.

Additional details

Created:
August 22, 2023
Modified:
October 24, 2023