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Published March 10, 2020 | Submitted + Published
Journal Article Open

The Influence of Late-stage Nuclear Burning on Red Supergiant Supernova Light Curves

Abstract

Many Type II supernovae (SNe) show hot early (~30 days) emission, and a diversity in their light curves extending from the Type IIP to the Type IIL, which can be explained by interaction with dense and confined circumstellar material (CSM). We perform hydrodynamical simulations of red supergiants to model the ejection of CSM caused by wave heating during late-stage nuclear burning. Even a small amount of deposited energy (10⁴⁶–10⁴⁷ erg), which is roughly that expected due to waves excited by convection in the core, is sufficient to change the shapes of SN light curves and bring them into better agreement with observations. As a test case, we consider the specific example of supernova (SN) 2017eaw, which shows that a nuclear burning episode is able to explain the light curve if it occurs ~150–450 days prior to core collapse. Due to the long timescale that it takes for the low-energy shock to traverse the star, this would manifest as a pre-SN outburst ~50–350 days prior to the full-fledged SN. Applying work like this to other SNe will provide a direct connection between the SN and pre-SN outburst properties, which can be tested by future wide field surveys. In addition, we show that our models can qualitatively explain the short-lived "flash-ionization" lines seen in the early spectra of many Type II SNe.

Additional Information

© 2020. The American Astronomical Society. Received 2019 December 20; revised 2020 February 16; accepted 2020 February 19; published 2020 March 9. V.M. acknowledges helpful discussions with David Radice, James Stone, and Adam Burrows. A.L.P. acknowledges financial support for this research from a Scialog award made by the Research Corporation for Science Advancement. This research is funded in part by a Rose Hills Innovator Grant, and by grant HST-AR-15021.001-A. Numerical simulations were performed using Della cluster of Princeton University, as well as computing services of the Institute for Computational and Data Sciences of the Pennsylvania State University.

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