The Most Luminous Supernovae Gal-Yam, Avishay
Annual review of astronomy and astrophysics,
08/2019, Volume:
57, Issue:
1
Journal Article
Peer reviewed
Open access
Over a decade ago, a group of supernova explosions with peak luminosities far exceeding (often by >100 times) those of normal events has been identified. These superluminous supernovae (SLSNe) have ...been a focus of intensive study. I review the accumulated observations and discuss the implications for the physics of these extreme explosions.
SLSNe can be classified into hydrogen-poor (SLSNe-I) and hydrogen-rich (SLSNe-II) events.
Combining photometric and spectroscopic analysis of samples of nearby SLSNe-I and lower-luminosity events, a threshold of
mag at peak appears to separate SLSNe-I from the normal population.
SLSN-I light curves can be quite complex, presenting both early bumps and late postpeak undulations.
SLSNe-I spectroscopically evolve from an early hot photospheric phase with a blue continuum and weak absorption lines, through a cool photospheric phase resembling spectra of SNe Ic, and into the late nebular phase.
SLSNe-II are not nearly as well studied, lacking information based on large-sample studies.
Proposed models for the SLSN power source are challenged to explain all the observations. SLSNe arise from massive progenitors, with some events associated with very massive stars (
M
). Host galaxies of SLSNe in the nearby Universe tend to have low mass and subsolar metallicity. SLSNe are rare, with rates <100 times lower than ordinary supernovae. SLSN cosmology and their use as beacons to study the high-redshift Universe offer exciting prospects.
Luminous Supernovae Gal-Yam, Avishay
Science (American Association for the Advancement of Science),
08/2012, Volume:
337, Issue:
6097
Journal Article
Peer reviewed
Open access
Supernovae, the luminous explosions of stars, have been observed since antiquity. However, various examples of superluminous supernovae (SLSNe; luminosities >7 × 10 43 ergs per second) have only ...recently been documented. From the accumulated evidence, SLSNe can be classified as radioactively powered (SLSN-R), hydrogen-rich (SLSN-II), and hydrogen-poor (SLSN-I, the most luminous class). The SLSN-II and SLSN-I classes are more common, whereas the SLSN-R class is better understood. The physical origins of the extreme luminosity emitted by SLSNe are a focus of current research.
ABSTRACT We have entered an era of massive data sets in astronomy. In particular, the number of supernova (SN) discoveries and classifications has substantially increased over the years from few tens ...to thousands per year. It is no longer the case that observations of a few prototypical events encapsulate most spectroscopic information about SNe, motivating the development of modern tools to collect, archive, organize, and distribute spectra in general and SN spectra in particular. For this reason, we have developed the Weizmann Interactive Supernova Data Repository (WISeREP)-an SQL-based database (DB) with an interactive Web-based graphical interface. The system serves as an archive of high-quality SN spectra, including both historical (legacy) data and data that are accumulated by ongoing modern programs. The archive provides information about objects, their spectra, and related metadata. Utilizing interactive plots, we provide a graphical interface to visualize data, perform line identification of the major relevant species, determine object redshifts, classify SNe, and measure expansion velocities. Guest users may view and download spectra or other data that have been placed in the public domain. Registered users may also view and download data that are proprietary to specific programs with which they are associated. The DB currently holds more than 8000 spectra, of which more than 5000 are public; the latter include published spectra from the Palomar Transient Factory (PTF), all of the SUSPECT (Supernova Spectrum) archive, the Caltech-Core-Collapse Program (CCCP), the CfA SN spectra archive, and published spectra from the University of California, Berkeley, SNDB repository. It offers an efficient and convenient way to archive data and share it with colleagues, and we expect that data stored in this way will be easy to access, increasing its visibility, usefulness, and scientific impact. We encourage the SN community worldwide to make use of the data and tools provided by WISeREP and to contribute data to be made globally available and archived for posterity.
ABSTRACT Transient detection and flux measurement via image subtraction stand at the base of time domain astronomy. Due to the varying seeing conditions, the image subtraction process is non-trivial, ...and existing solutions suffer from a variety of problems. Starting from basic statistical principles, we develop the optimal statistic for transient detection, flux measurement, and any image-difference hypothesis testing. We derive a closed-form statistic that: (1) is mathematically proven to be the optimal transient detection statistic in the limit of background-dominated noise, (2) is numerically stable, (3) for accurately registered, adequately sampled images, does not leave subtraction or deconvolution artifacts, (4) allows automatic transient detection to the theoretical sensitivity limit by providing credible detection significance, (5) has uncorrelated white noise, (6) is a sufficient statistic for any further statistical test on the difference image, and, in particular, allows us to distinguish particle hits and other image artifacts from real transients, (7) is symmetric to the exchange of the new and reference images, (8) is at least an order of magnitude faster to compute than some popular methods, and (9) is straightforward to implement. Furthermore, we present extensions of this method that make it resilient to registration errors, color-refraction errors, and any noise source that can be modeled. In addition, we show that the optimal way to prepare a reference image is the proper image coaddition presented in Zackay & Ofek. We demonstrate this method on simulated data and real observations from the PTF data release 2. We provide an implementation of this algorithm in MATLAB and Python.
Knowledge of the supernova (SN) delay time distribution (DTD)--the SN rate versus time that would follow a hypothetical brief burst of star formation--can shed light on SN progenitors and physics, as ...well as on the timescales of chemical enrichment in different environments. We compile recent measurements of the Type-Ia SN (SN Ia) rate in galaxy clusters at redshifts from z = 0 out to z = 1.45, just 2 Gyr after cluster star formation at z 3. We review the plausible range for the observed total iron-to-stellar mass ratio in clusters, based on the latest data and analyses, and use it to constrain the time-integrated number of SN Ia events in clusters. With these data, we recover the DTD of SNe Ia in cluster environments. The DTD is sharply peaked at the shortest time-delay interval we probe, 0Gyr < t < 2.2 Gyr, with a low tail out to delays of ~10 Gyr, and is remarkably consistent with several recent DTD reconstructions based on different methods, applied to different environments. We test DTD models from the literature, requiring that they simultaneously reproduce the observed cluster SN rates and the observed iron-to-stellar mass ratios. A parameterized power-law DTD of the form t --1.2?0.3 from t = 400 Myr to a Hubble time can satisfy both constraints. Shallower power laws such as t --1/2 cannot, assuming a single DTD, and a single star formation burst (either brief or extended) at high z. This implies that 50%-85% of SNe Ia explode within 1 Gyr of star formation. DTDs from double-degenerate (DD) models, which generically have ~t --1 shapes over a wide range of timescales, match the data, but only if their predictions are scaled up by factors of 5-10. Single-degenerate (SD) DTDs always give poor fits to the data, due to a lack of delayed SNe and overall low numbers of SNe. The observations can also be reproduced with a combination of two SN Ia populations--a prompt SD population of SNe Ia that explodes within a few Gyr of star formation, and produces about 60% of the iron mass in clusters, and a DD population that contributes the events seen at z < 1.5. An alternative scenario of a single, prompt, SN Ia population, but a composite star formation history in clusters, consisting of a burst at high z, followed by a constant star formation rate, can reproduce the SN rates, but is at odds with direct measurements of star formation in clusters at 0 < z < 1. Our results support the existence of a DD progenitor channel for SNe Ia, if the overall predicted numbers can be suitably increased.
Modern transient surveys have begun discovering and following supernovae (SNe) shortly after first light-providing systematic measurements of the rise of Type II SNe. We explore how analytic models ...of early shock-cooling emission from core-collapse SNe can constrain the progenitor's radius, explosion velocity, and local host extinction. We simulate synthetic photometry in several realistic observing scenarios; assuming the models describe the typical explosions well, we find that ultraviolet observations can constrain the progenitor's radius to a statistical uncertainty of 10%-15%, with a systematic uncertainty of 20%. With these observations the local host extinction (AV) can be constrained to a factor of two and the shock velocity to 5% with a systematic uncertainty of 10%. We also reanalyze the SN light curves presented by Garnavich et al. (2016) and find that KSN 2011a can be fit by a blue supergiant model with a progenitor radius of , while KSN 2011d can be fit with a red supergiant model with a progenitor radius of . Our results do not agree with those of Garnavich et al. Moreover, we re-evaluate their claims and find that there is no statistically significant evidence for a shock-breakout flare in the light curve of KSN 2011d.
ABSTRACT As new facilities come online, the astronomical community will be provided with extremely large data sets of well-sampled light curves (LCs) of transients. This motivates systematic studies ...of the LCs of supernovae (SNe) of all types, including the early rising phase. We performed unsupervised k-means clustering on a sample of 59 R-band SN II LCs and find that the rise to peak plays an important role in classifying LCs. Our sample can be divided into three classes: slowly rising (II-S), fast rise/slow decline (II-FS), and fast rise/fast decline (II-FF). We also identify three outliers based on the algorithm. The II-FF and II-FS classes are disjoint in their decline rates, while the II-S class is intermediate and "bridges the gap." This may explain recent conflicting results regarding II-P/II-L populations. The II-FS class is also significantly less luminous than the other two classes. Performing clustering on the first two principal component analysis components gives equivalent results to using the full LC morphologies. This indicates that Type II LCs could possibly be reduced to two parameters. We present several important caveats to the technique, and find that the division into these classes is not fully robust. Moreover, these classes have some overlap, and are defined in the R band only. It is currently unclear if they represent distinct physical classes, and more data is needed to study these issues. However, we show that the outliers are actually composed of slowly evolving SN IIb, demonstrating the potential of such methods. The slowly evolving SNe IIb may arise from single massive progenitors.
The recent discovery of the first four afterglows of short-hard gamma-ray bursts (SHBs) suggests that they typically result from long-lived progenitor systems. The most popular progenitor model ...invokes the merger of either double neutron star (DNS) binaries or neutron star-black hole (NS-BH) systems. Such events are strong sources of gravitational waves (GWs) and might be detected by ground-based GW observatories. In this work we combine the census of SHB observations with refined theoretical analysis to perform a critical evaluation of the compact binary model. We then explore the implications for GW detection of these events. Beginning from the measured star formation rate through cosmic time, we consider what intrinsic luminosity and lifetime distributions can reproduce the known SHB redshifts and luminosities as well as the peak flux distribution of the large BATSE SHB sample. We find the following: (1) The typical progenitor lifetime is long. Assuming lognormal lifetime distribution, the typical lifetime is >4 (1) Gyr (2 s3 s c.l.). If the lifetime distribution is a power law with index , then> -0.5 (-1) (2 s3 s c.l.). This result is difficult to reconcile with the properties of the observed Galactic DNS population, suggesting that if SHBs do result from DNS mergers, then the observed Galactic binaries do not represent the cosmic one. (2) We find that the local rate of SHBs is larger than 10 Gpc super(-3) yr super(-1) and may be higher by several orders of magnitude, significantly above previous estimates. (3) Assuming that SHBs do result from compact binaries, our predictions for the LIGO and VIRGO event rates are encouraging: the chance for detection by current facilities is not negligible, while a coincident detection of GW and electromagnetic radiation from an SHB is guaranteed for next-generation observatories.
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