JWST spectra of SN 2024abup show CO, C, O, and Mg features plus possible dust emission, with no clear r-process signatures identified via SUMO modeling.
Light-travel-time diagnostics in early Supernova spectra: substantial mass loss of the IIb progenitor of SN 2013cu through a superwind
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abstract
The progenitors of type-IIb supernovae (SNe) are believed to have lost their H-rich envelopes almost completely in the direct pre-SN phase. Recently the first 'flash spectrum' of a SN IIb (SN2013cu) has been presented, taken early enough to study its immediate circumstellar medium (CSM). Similar to a previous study by Groh (2014) we analyse the structure and chemical composition of the optically-thick CSM using non-LTE model atmospheres. For the first time we take light-travel time (LTT) effects on the spectrum formation into account, which affect the shapes and strengths of the observable emission lines, as well as the inferred SN luminosity. Based on the new CSM parameters we estimate a lower limit of ~0.3Msun for the CSM mass, which is a factor 10-100 higher than previous estimates. The spectral fit implies a CSM in the form of a homogeneous and spherically symmetric superwind whose mass-loss rate exceeds common expectations by up to two orders of magnitude. The derived chemical composition is in agreement with a progenitor that has just left, or is just about to leave the Red-Supergiant (RSG) stage, confirming the standard picture for the origin of SNe IIb. Due to its extreme mass loss the SN progenitor will likely appear as extreme RSG, Luminous Blue Variable (LBV), or Yellow Hypergiant (YHG). The direct detection of a superwind, and the high inferred CSM mass suggest that stellar wind mass loss may play an important role in the formation of SNe IIb.
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JWST observations of SN 2024abup: First Detection of CO in a broad-lined Type Ic Supernova and Constraints on r-process Nucleosynthesis
JWST spectra of SN 2024abup show CO, C, O, and Mg features plus possible dust emission, with no clear r-process signatures identified via SUMO modeling.