Unsupervised clustering of Type II supernova light curves

As new facilities come online, the astronomical community will be provided with extremely large datasets of well-sampled light curves (LCs) of transient objects. This motivates systematic studies of the light curves of supernovae (SNe) of all types, including the early rising phase. We performed unsupervised k-means clustering on a sample of 59 R-band Type II SN light curves and find that 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. We find that performing clustering on the first two components of a principal component analysis gives equivalent results to the analysis using the full LC morphologies. This may indicate 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 and is sensitive to the uncertainty on the time of first light. 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, our analysis shows that the outliers are actually composed of slowly-evolving SN IIb, demonstrating the potential use of such methods. The slowly-evolving SNe IIb may arise from single massive progenitors.Comment: Comments welcome. Fixed small typo

The Redshift Distribution of Type-Ia Supernovae: Constraints on Progenitors and Cosmic Star Formation History

We use the redshift distribution of type-Ia supernovae (SNe) discovered by the Supernova Cosmology Project to constrain the star formation history (SFH) of the Universe and SN Ia progenitor models. Given some of the recent determinations of the SFH, the observed SN Ia redshift distribution indicates a long (>~1 h^-1 Gyr) mean delay time between the formation of a stellar population and the explosion of some of its members as SNe Ia. For example, if the Madau et al. (1998) SFH is assumed, the delay time tau is constrained to be tau > 1.7 (tau > 0.7) h^-1 Gyr at the 95%(99%) confidence level (CL). SFHs that rise at high redshift, similar to those advocated by Lanzetta et al. (2002), are inconsistent with the data at the 95% CL unless tau > 2.5 h^-1 Gyr. Long time delays disfavor progenitor models such as edge-lit detonation of a white dwarf accreting from a giant donor, and the carbon core ignition of a white dwarf passing the Chandrasekhar mass due to accretion from a subgiant. The SN Ia delay may be shorter, thereby relaxing some of these constraints, if the field star formation rate falls, between z=1 and the present, less sharply than implied, e.g., by the original Madau plot. We show that the discovery of larger samples of high-z SNe Ia by forthcoming observational projects should yield strong constraints on the progenitor models and the SFH. In a companion paper (astro-ph/0309797), we demonstrate that if SNe Ia produce most of the iron in galaxy clusters, and the stars in clusters formed at z~2, the SN Ia delay time must be lower than 2 Gyr. If so, then the Lanzetta et al. (2002) SFH will be ruled out by the data presented here.Comment: MNRAS, accepte

Supernovae in Deep Hubble Space Telescope Galaxy Cluster Fields: Cluster Rates and Field Counts

We have searched for high-redshift supernova (SN) candidates in multiple deep Hubble Space Telescope (HST) archival images of nine galaxy-cluster fields. We detect six apparent SNe, with I814 between 21.6 and 28.4 mag. There is roughly 1 SN per deep (flux limit I814 > 26 mag), doubly-imaged, WFPC2 cluster field. Two SNe are associated with cluster galaxies (at redshifts z=0.18 and z=0.83), three are probably in galaxies not in the clusters (at z=0.49, z=0.60, and z=0.98), and one is at unknown z. After accounting for observational efficiencies and uncertainties (statistical and systematic) we derive the rate of type-Ia SNe within the projected central 500 kpc of rich clusters: R=0.20(+0.84)(-0.19) SNu in clusters at z=0.18 to 0.37, and R=0.41(+1.23)(-0.39) SNu in clusters at z=0.83 to 1.27 (95 per cent confidence interval; H_0=50; 1 SNu = 1 SN per century per 10^10 L_B_sun). Combining the two redshift bins, the SN rate at a mean redshift of z=0.41 is R(z=0.41) = 0.30(+0.58)(-0.28) SNu. The upper bounds argue against SNe-Ia being the dominant source of the large iron mass observed in the intra-cluster medium. We also compare our observed counts of field SNe (i.e., non-cluster SNe of all types) to recent model predictions. The observed field count is zero or one SN with I814 < 26 mag, and 1 to 3 SNe with I814 < 27 mag. These counts are about two times lower than some of the predictions. Since the counts at these magnitudes are likely dominated by type-II SNe, our observations may suggest obscuration of distant SNe-II, or a SN-II luminosity distribution devoid of a large high-luminosity tail.Comment: MNRAS, in press. Small modifications in final version include redshifts for all five detected SN host galaxies, upward revision of cluster SN-Ia rates, and some changes in field SN count

Bayesian single-epoch photometric classification of supernovae

Ongoing supernova (SN) surveys find hundreds of candidates that require confirmation for their various uses. Traditional classification based on follow- up spectroscopy of all candidates is virtually impossible for these large samples. The use of Type Ia SNe as standard candles is at an evolved stage that requires pure, uncontaminated samples. However, other SN survey applications, such as measuring cosmic SN rates, could benefit froma classification of SNe on a statistical basis, rather than case by case. With this objective in mind, we have developed the SN-ABC, an automatic Bayesian classifying algorithm for supernovae. We rely solely on single- epoch multiband photometry and host-galaxy (photometric) redshift information to sort SN candidates into the two major types, Ia and core-collapse supernovae. We test the SN-ABC performance on published samples of SNe from the Supernova Legacy Survey (SNLS) and GOODS projects that have both broadband photometry and spectroscopic classification (so the true type is known). The SN- ABC correctly classifies up to 97% (85%) of the Type Ia (II-P) SNe in SNLS, and similar fractions of the GOODS SNe, depending on photometric redshift quality. Using simulations with large artificial samples, we find similarly high success fractions for Types Ia and II-P, and reasonable (~75%) success rates in classifying Type Ibc SNe as core-collapse. Type IIn SNe, however, are often misclassified as Type Ia. In deep surveys, SNe Ia are best classified at redshifts z ≳ 0.6 or when near maximum. Core-collapse SNe (other than Type IIn) are best recognized several weeks after maximum, or at z ≾ 0.6. Assuming the SNe are young, as would be the case for rolling surveys, the success fractions improve by a degree dependent on the type and redshift. The fractional contamination of a single-epoch photometrically selected sample of SNe la by core-collapse SNe varies between less than 10% and as much as 30%, depending on the intrinsic fraction and redshift distribution of the core-collapse SNe in a given survey. The SN-ABC also allows the rejection of SN "impostors" such as active galactic nuclei (AGNs), with half of the AGNs we simulate rejected by the algorithm. Our algorithm also supplies a good measure of the quality of the classification, which is valuable for error estimation