Multiwavelength view of circumstellar interaction in supernovae

TL;DR

This review addresses how circumstellar interaction encodes progenitor mass-loss histories to map SN subtypes onto stellar death pathways. It synthesizes self-similar shock theory with multiwavelength observations, highlighting how forward/reverse shocks and the CDS shape X-ray, radio, optical, and infrared signals. Key findings include the rich diagnostic power of flash-ionization in H-rich SNe, the prominent role of dense CSM in SNe IIn, and the diverse CS environments revealed by metamorphosis events like SN 2014C, all supported by case studies such as SN 2010jl. The work argues that coordinated, high-cadence, multiwavelength campaigns, along with next-generation facilities, can leverage CS interaction as a Time Machine to connect pre-explosion mass loss to ultimate explosion outcomes.

Abstract

The interaction of post-explosion supernova ejecta with the surrounding circumstellar medium creates emission across the electromagnetic spectrum. Since the circumstellar medium is created by the mass lost from the progenitor star, it carries tell-tale signatures of the progenitor. Consequently, observations and modeling of radiation produced by the interaction in various types of supernovae have provided valuable insights into their progenitors. Detailed studies have shown that the interaction in supernovae begins and sustains over various timescales and lengthscales, with differing mass-loss rates in distinct sub-classes. This reveals diverse progenitor histories for these stellar explosions. This review paper summarizes various supernova subtypes, linking them to stellar death pathways, and presents an updated supernova classification diagram. We then present a multi-wavelength study of circumstellar interaction in different supernova classes. We also present unpublished Chandra X-ray as well as radio observations of a type IIn supernova, SN 2010jl, which allow us to extend its circumstellar interaction studies to about 7 years post-explosion. The new data indicates that the extreme mass-loss rate 0.1 Msun/yr in SN 2010jl, reported by Chandra et al. (2015), commenced within the last 300 years before the explosion. We summarize the current status of the field and argue that via detailed studies of the circumstellar interaction, a.k.a. Time Machine technique, one of the big mysteries of stellar evolution, i.e., mapping supernovae progenitors to their explosive outcomes can be solved.

Figures (11)

• Figure S1: Supernova classification scheme that combined traditional classification with stellar death pathways. The two most significant luminosity power sources are mentioned for each subtype. The color scheme representing the power sources is explained in the box on the left.
• Figure S2: CS interaction picture in a typical SN. The left image shows ejecta-CSM interaction and the formation of various shocked and unshocked regions. The right side image shows the radiation produced as a result of ejecta-CS interaction.
• Figure S3: A cartoon diagram of variation of density, pressure, and velocity and temperature in the ejecta-CSM interaction region for $\rho_{\rm ej} \propto r^{-n}$, $n\ge5$ and $\rho_{\rm CSM} \propto r^{-2}$. The figure is inspired by the Chevalier model Chevalier1982a. The variables have been normalized to their values at the forward shock front. The quantities are not to be scaled.
• Figure S4: 1.4 GHz (left) and 8 GHz (right) radio spectral luminosity of published SNe IIb. SNe eIIb are plotted with squares, and SNe cIIb are plotted with circles. There is no clear differentiation between compact and extended progenitors. However, there may be a hint of faster evolution of SNe cIIb. SN 2008ax, with the highest ejecta velocity, clearly peaks at much earlier times than other SNe IIb. The data are taken from Nayana2022Gangopadhyay2023.
• Figure S5: Peak 8 GHz radio spectral luminosity and the peak time for SESNe for a small sample of well-observed cases. While SNe IIb/Ib/Ic do not show a major difference from SNe Ic-bl, SNe associated with GRBs seem to be intrinsically different from the rest of the sample. Most of the data are taken from Nayana2021Nayana2022Ho2020.