Connecting the Light Curves of Type IIP Supernovae to the Properties of their Progenitors

Abstract

Observations of core-collapse supernovae (CCSNe) reveal a wealth of information about the dynamics of the supernova ejecta and its composition but reveal very little direct information about the progenitor star. Constraining properties of the progenitor and the explosion, such as explosion energy, requires coupling the observations with a theoretical model of the explosion. Here, we use a new model for driving turbulenceaided neutrino-driven core-collapse supernovae in 1D (STIR) which contains a non-parametric treatment of the neutrino transport while also accounting for turbulence and convection. We couple this with the SuperNova Explosion Code to produce bolometric light curves from a landscape of CCSNe driven from self-consistent CCSN simulations with robust neutrino transport. We compare our results to several well observed bolometric light curves of Type IIP CCSNe and find that our best fitting models differ from those found in previous studies using thermal bomb explosions, indicating ZAMS masses as much as about 10M greater than previous studies. Using our large sample of 136 self-consistent CCSN explosions, we explore correlations between observable features of the light curves and properties of the progenitor star. Among other significant correlations, we find a robust linear relationship between light curve plateau luminosity and the iron core mass of the progenitor. This relationship allows for properties of the core of the progenitor to be constrained for the first time from photometry alone.

Supernova Preshock Neutronization Burst as a Probe of Non-Standard Neutrino Interactions

“…In addition, we also note the preshock burst is only slightly dependent on the progenitor mass, consistent with the earlier findings [19,20,33]. Nevertheless, the property of the progenitor can be constrained in practice with multimessenger signals [34][35][36][37][38] once the source is detected. Moreover, it is interesting to note that the more realistic three-dimension (3D) SN simulations [39] give very similar predictions on the neutronization burst during the early stage of CCSN as the one-dimension (1D) simulations adopted here, further justifying the robustness of the preshock burst as a probe of ε f .…”

A low-energy explosion yields the underluminous Type IIP SN 2020cxd

“…13) and the ejected mass of radioactive nickel, but this SN adds to the evidence that such correlations exist. Explosion models of neutrino-driven Fe CC indeed predict that in this ZAMS mass regime, where the iron core mass scales with ZAMS mass, lower explosion energies and thus ejecta velocities are expected from lower ZAMS mass stars, which also eject less mass (Barker et al 2021). Fig.…”

Impact of diffusion coefficients in phenomenological turbulent simulations of core-collapse supernovae

“…1D+ simulations are robust tools for parametric studies of the core-collapse supernovae, though there are still many problems. A variety of applications are possible, for example; progenitor dependence (Couch et al 2020); neutrino, gravitational wave, optical light curves (Warren et al 2020;Barker et al 2021); detailed neutrino emission (Suwa et al 2019;Mori et al 2021); EOS dependence (Yasin et al 2020;Nakazato et al 2021); BH formation (da Silva Schneider et al 2020); quark star phase transition (Fischer et al 2020); emission of axionlike particle (Fischer et al 2016) and so on. We hope that this study will take us one step closer to understanding a vast and untapped area of the research field.…”

“…This approach not only would reproduce the dynamics close to the results of 3D simulations, but also is useful to determine the landscape of explodability (e.g., the progenitor dependence), taking advantage of its low computational cost as in other phenomenological approaches. Predicting the neutrino luminosity, the frequency of the gravitational waves, and the optical light curves, 1D+ is also used in the context of multi-messenger observation of core-collapse supernova (Warren et al 2020;Barker et al 2021).…”