Late Light Curves of Type Ia SNe

Author(s):  
Peter A. Milne ◽  
G. Grant Williams
Keyword(s):  
1994 ◽  
Vol 147 ◽  
pp. 186-213
Author(s):  
J. Isern ◽  
R. Canal

AbstractIn this paper we review the behavior of growing stellar degenerate cores. It is shown that ONeMg white dwarfs and cold CO white dwarfs can collapse to form a neutron star. This collapse is completely silent since the total amount of radioactive elements that are expelled is very small and a burst of γ-rays is never produced. In the case of an explosion (always carbonoxygen cores), the outcome fits quite well the observed properties of Type Ia supernovae. Nevertheless, the light curves and the velocities measured at maximum are very homogeneous and the diversity introduced by igniting at different densities is not enough to account for the most extreme cases observed. It is also shown that a promising way out of this problem could be the He-induced detonation of white dwarfs with different masses. Finally, we outline that the location of the border line which separetes explosion from collapse strongly depends on the input physics adopted.


2021 ◽  
Vol 502 (3) ◽  
pp. 4112-4124
Author(s):  
Umut Burgaz ◽  
Keiichi Maeda ◽  
Belinda Kalomeni ◽  
Miho Kawabata ◽  
Masayuki Yamanaka ◽  
...  

ABSTRACT Photometric and spectroscopic observations of Type Ia supernova (SN) 2017fgc, which cover the period from −12 to + 137 d since the B-band maximum are presented. SN 2017fgc is a photometrically normal SN Ia with the luminosity decline rate, Δm15(B)true  = 1.10 ± 0.10 mag. Spectroscopically, it belongs to the high-velocity (HV) SNe Ia group, with the Si ii λ6355 velocity near the B-band maximum estimated to be 15 200 ± 480 km s−1. At the epochs around the near-infrared secondary peak, the R and I bands show an excess of ∼0.2-mag level compared to the light curves of the normal velocity (NV) SNe Ia. Further inspection of the samples of HV and NV SNe Ia indicates that the excess is a generic feature among HV SNe Ia, different from NV SNe Ia. There is also a hint that the excess is seen in the V band, both in SN 2017fgc and other HV SNe Ia, which behaves like a less prominent shoulder in the light curve. The excess is not obvious in the B band (and unknown in the U band), and the colour is consistent with the fiducial SN colour. This might indicate that the excess is attributed to the bolometric luminosity, not in the colour. This excess is less likely caused by external effects, like an echo or change in reddening but could be due to an ionization effect, which reflects an intrinsic, either distinct or continuous, difference in the ejecta properties between HV and NV SNe Ia.


2020 ◽  
Vol 493 (4) ◽  
pp. 5617-5624
Author(s):  
Doron Kushnir ◽  
Eli Waxman

ABSTRACT The finite time, τdep, over which positrons from β+ decays of 56Co deposit energy in type Ia supernovae ejecta lead, in case the positrons are trapped, to a slower decay of the bolometric luminosity compared to an exponential decline. Significant light-curve flattening is obtained when the ejecta density drops below the value for which τdep equals the 56Co lifetime. We provide a simple method to accurately describe this ‘delayed deposition’ effect, which is straightforward to use for analysis of observed light curves. We find that the ejecta heating is dominated by delayed deposition typically from 600 to 1200 d, and only later by longer lived isotopes 57Co and 55Fe decay (assuming solar abundance). For the relatively narrow 56Ni velocity distributions of commonly studied explosion models, the modification of the light curve depends mainly on the 56Ni mass-weighted average density, 〈ρ〉t3. Accurate late-time bolometric light curves, which may be obtained with JWST far-infrared (far-IR) measurements, will thus enable to discriminate between explosion models by determining 〈ρ〉t3 (and the 57Co and 55Fe abundances). The flattening of light curves inferred from recent observations, which is uncertain due to the lack of far-IR data, is readily explained by delayed deposition in models with $\langle \rho \rangle t^{3} \approx 0.2\, \mathrm{M}_{\odot }\, (10^{4}\, \textrm{km}\, \textrm{s}^{-1})^{-3}$, and does not imply supersolar 57Co and 55Fe abundances.


1991 ◽  
Vol 145 ◽  
pp. 21-38
Author(s):  
K. Nomoto ◽  
T. Shigeyama ◽  
T. Tsujimoto

Theoretical models of supernova explosions of various types are reviewed to obtain heavy element yields from supernovae. We focus on new models for SN 1987A, and Type Ia, Ib, and Ic supernovae. Maximum brightness and decline rate of their light curves suggest that 12–18 M⊙ stars produce larger amount of 56Ni than more massive stars. We discuss relative roles of various types of supernovae in the chemical evolution of galaxies.


2020 ◽  
Vol 499 (3) ◽  
pp. 4312-4324
Author(s):  
Alexandra Kozyreva ◽  
Luke Shingles ◽  
Alexey Mironov ◽  
Petr Baklanov ◽  
Sergey Blinnikov

ABSTRACT We systematically explore the effect of the treatment of line opacity on supernova light curves. We find that it is important to consider line opacity for both scattering and absorption (i.e. thermalization, which mimics the effect of fluorescence). We explore the impact of the degree of thermalization on three major types of supernovae: Type Ia, Type II-peculiar, and Type II-plateau. For this we use the radiative transfer code stella and analyse broad-band light curves in the context of simulations done with the spectral synthesis code artis and in the context of a few examples of observed supernovae of each type. We found that the plausible range for the ratio between absorption and scattering in the radiation hydrodynamics code stella is (0.8–1):(0.2–0), i.e. the recommended thermalization parameter is 0.9.


2020 ◽  
Vol 497 (3) ◽  
pp. 2974-2991
Author(s):  
Marcelo Vargas dos Santos ◽  
Miguel Quartin ◽  
Ribamar R R Reis

ABSTRACT The efficient classification of different types of supernovae is one of the most important problems for observational cosmology. However, spectroscopic confirmation of most objects in upcoming photometric surveys, such as the the Rubin Observatory Legacy Survey of Space and Time, will be unfeasible. The development of automated classification processes based on photometry has thus become crucial. In this paper, we investigate the performance of machine learning (ML) classification on the final cosmological constraints using simulated light-curves from the Supernova Photometric Classification Challenge, released in 2010. We study the use of different feature sets for the light-curves and many different ML pipelines based on either decision-tree ensembles or automated search processes. To construct the final catalogues we propose a threshold selection method, by employing a bias-variance tradeoff. This is a very robust and efficient way to minimize the mean squared error. With this method, we were able to obtain very strong cosmological constraints, which allowed us to keep $\sim 75{{\ \rm per\ cent}}$ of the total information in the Type Ia supernovae when using the SALT2 feature set, and $\sim 33{{\ \rm per\ cent}}$ for the other cases (based either on the Newling model or on standard wavelet decomposition).


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