SN 2018agk: A Prototypical Type Ia Supernova with a Smooth Power-law Rise in Kepler (K2)

Abstract We present the 30 minutes cadence Kepler/K2 light curve of the Type Ia supernova (SN Ia) SN 2018agk, covering approximately one week before explosion, the full rise phase, and the decline until 40 days after peak. We additionally present ground-based observations in multiple bands within the same time range, including the 1 day cadence DECam observations within the first ∼5 days after the first light. The Kepler early light curve is fully consistent with a single power-law rise, without evidence of any bump feature. We compare SN 2018agk with a sample of other SNe Ia without early excess flux from the literature. We find that SNe Ia without excess flux have slowly evolving early colors in a narrow range (g − i ≈ −0.20 ± 0.20 mag) within the first ∼10 days. On the other hand, among SNe Ia detected with excess, SN 2017cbv and SN 2018oh tend to be bluer, while iPTF16abc’s evolution is similar to normal SNe Ia without excess in g − i. We further compare the Kepler light curve of SN 2018agk with companion-interaction models, and rule out the existence of a typical nondegenerate companion undergoing Roche lobe overflow at viewing angles smaller than 45°.

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ASASSN-18tb: a most unusual Type Ia supernova observed by TESS and SALT

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stz1445 ◽

2019 ◽

Vol 487 (2) ◽

pp. 2372-2384 ◽

Cited By ~ 16

Author(s):

P J Vallely ◽

M Fausnaugh ◽

S W Jha ◽

M A Tucker ◽

Y Eweis ◽

...

Keyword(s):

Light Curve ◽

Power Law ◽

Type Ia Supernovae ◽

Normal Type ◽

Type Ia Supernova ◽

Type Ia ◽

Early Rise ◽

Ia Supernovae ◽

Best Fit ◽

Unusual Type

ABSTRACT We present photometric and spectroscopic observations of the unusual Type Ia supernova ASASSN-18tb, including a series of Southern African Large Telescope spectra obtained over the course of nearly six months and the first observations of a supernova by the Transiting Exoplanet Survey Satellite. We confirm a previous observation by Kollmeier et al. showing that ASASSN-18tb is the first relatively normal Type Ia supernova to exhibit clear broad (∼1000 km s−1) H α emission in its nebular-phase spectra. We find that this event is best explained as a sub-Chandrasekhar mass explosion producing $M_{\mathrm{ Ni}} \approx 0.3\,\, \rm {M}_\odot$. Despite the strong H α signature at late times, we find that the early rise of the supernova shows no evidence for deviations from a single-component power-law and is best fit with a moderately shallow power law of index 1.69 ± 0.04. We find that the H α luminosity remains approximately constant after its initial detection at phase +37 d, and that the H α velocity evolution does not trace that of the Fe iii λ4660 emission. These suggest that the H α emission arises from a circumstellar medium (CSM) rather than swept-up material from a non-degenerate companion. However, ASASSN-18tb is strikingly different from other known CSM-interacting Type Ia supernovae in a number of significant ways. Those objects typically show an H α luminosity two orders of magnitude higher than what is seen in ASASSN-18tb, pushing them away from the empirical light-curve relations that define ‘normal’ Type Ia supernovae. Conversely, ASASSN-18tb exhibits a fairly typical light curve and luminosity for an underluminous or transitional SN Ia, with MR ≈ −18.1 mag. Moreover, ASASSN-18tb is the only SN Ia showing H α from CSM interaction to be discovered in an early-type galaxy.

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A Precise Distance Indicator: Type Ia Supernova Multicolor Light‐Curve Shapes

The Astrophysical Journal ◽

10.1086/178129 ◽

1996 ◽

Vol 473 (1) ◽

pp. 88-109 ◽

Cited By ~ 493

Author(s):

Adam G. Riess ◽

William H. Press ◽

Robert P. Kirshner

Keyword(s):

Light Curve ◽

Type Ia Supernova ◽

Type Ia ◽

Distance Indicator ◽

Indicator Type

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Early Blue Excess from the Type Ia Supernova 2017cbv

Proceedings of the International Astronomical Union ◽

10.1017/s1743921318002193 ◽

2017 ◽

Vol 14 (S339) ◽

pp. 47-49

Author(s):

G. Hosseinzadeh

Keyword(s):

Light Curve ◽

Standard Type ◽

Circumstellar Material ◽

Type Ia Supernova ◽

Unusual Distribution ◽

Photometric Observations ◽

Type Ia ◽

Optical Light Curve ◽

Ia Supernovae

AbstractThis paper presented very early, high-cadence photometric observations of the nearby Type Ia SN 2017cbv. The light-curve is unique in that during the first five days of observations it has a blue bump in the U, B, and g bands which is clearly resolved by virtue of our photometric cadence of 5.7 hr during that time span. We modelled the light-curve as the combination of an early shock of the supernova ejecta against a non-degenerate companion star plus a standard Type Ia supernova component. Our best-fit model suggested the presence of a subgiant star 56 R⊙ from the exploding white dwarf, although that number is highly model-dependent. While the model matches the optical light-curve well, it over-predicts the flux expected in the ultraviolet bands. That may indicate that the shock is not a blackbody, perhaps because of line blanketing in the UV. Alternatively, it could point to another physical explanation for the optical blue bump, such as interaction with circumstellar material or an unusual distribution of the element Ni. Early optical spectra of SN 2017cbv show strong carbon absorption as far as day –13 with respect to maximum light, suggesting that the progenitor system contained a significant amount of unburnt material. These results for SN 2017cbv illustrate the power of early discovery and intense follow-up of nearby supernovæ for resolving standing questions about the progenitor systems and explosion mechanisms of Type Ia supernovæ.

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A type Ia supernova at the heart of superluminous transient SN 2006gy

Science ◽

10.1126/science.aaw1469 ◽

2020 ◽

Vol 367 (6476) ◽

pp. 415-418 ◽

Cited By ~ 5

Author(s):

Anders Jerkstrand ◽

Keiichi Maeda ◽

Koji S. Kawabata

Keyword(s):

Binary System ◽

Light Curve ◽

Supernova Explosion ◽

Large Mass ◽

Late Time ◽

Standard Type ◽

Circumstellar Material ◽

Common Envelope ◽

Type Ia Supernova ◽

Type Ia

Superluminous supernovae radiate up to 100 times more energy than normal supernovae. The origin of this energy and the nature of the stellar progenitors of these transients are poorly understood. We identify neutral iron lines in the spectrum of one such supernova, SN 2006gy, and show that they require a large mass of iron (≳0.3 solar masses) expanding at 1500 kilometers per second. By modeling a standard type Ia supernova hitting a shell of circumstellar material, we produce a light curve and late-time iron-dominated spectrum that match the observations of SN 2006gy. In such a scenario, common envelope evolution of a progenitor binary system can synchronize envelope ejection and supernova explosion and may explain these bright transients.

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deepSIP: linking Type Ia supernova spectra to photometric quantities with deep learning

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa1706 ◽

2020 ◽

Vol 496 (3) ◽

pp. 3553-3571

Author(s):

Benjamin E Stahl ◽

Jorge Martínez-Palomera ◽

WeiKang Zheng ◽

Thomas de Jaeger ◽

Alexei V Filippenko ◽

...

Keyword(s):

Deep Learning ◽

Light Curve ◽

Data Augmentation ◽

Data Sets ◽

Curve Shape ◽

Learning Standards ◽

Type Ia Supernova ◽

Type Ia ◽

Augmentation Strategy ◽

Hyperparameter Selection

ABSTRACT We present deepSIP (deep learning of Supernova Ia Parameters), a software package for measuring the phase and – for the first time using deep learning – the light-curve shape of a Type Ia supernova (SN Ia) from an optical spectrum. At its core, deepSIP consists of three convolutional neural networks trained on a substantial fraction of all publicly available low-redshift SN Ia optical spectra, on to which we have carefully coupled photometrically derived quantities. We describe the accumulation of our spectroscopic and photometric data sets, the cuts taken to ensure quality, and our standardized technique for fitting light curves. These considerations yield a compilation of 2754 spectra with photometrically characterized phases and light-curve shapes. Though such a sample is significant in the SN community, it is small by deep-learning standards where networks routinely have millions or even billions of free parameters. We therefore introduce a data-augmentation strategy that meaningfully increases the size of the subset we allocate for training while prioritizing model robustness and telescope agnosticism. We demonstrate the effectiveness of our models by deploying them on a sample unseen during training and hyperparameter selection, finding that Model I identifies spectra that have a phase between −10 and 18 d and light-curve shape, parametrized by Δm15, between 0.85 and 1.55 mag with an accuracy of 94.6 per cent. For those spectra that do fall within the aforementioned region in phase–Δm15 space, Model II predicts phases with a root-mean-square error (RMSE) of 1.00 d and Model III predicts Δm15 values with an RMSE of 0.068 mag.

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