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Register a new userSupernovae in the Subaru Deep Field: An Initial Sample, and Type Ia Rate, out to Redshift 1.6
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Large samples of high-redshift supernovae (SNe) are potentially powerful probes of cosmic star formation, metal enrichment, and SN physics. We present initial results from a new deep SN survey, based on re-imaging in the R, i, z bands, of the 0.25 deg2 Subaru Deep Field (SDF), with the 8.2-m Subaru telescope and Suprime-Cam. In a single new epoch consisting of two nights of observations, we have discovered 33 candidate SNe, down to a z-band magnitude of 26.3 (AB). We have measured the photometric redshifts of the SN host galaxies, obtained Keck spectroscopic redshifts for 17 of the host galaxies, and classified the SNe using the Bayesian photometric algorithm of Poznanski et al. (2007) that relies on template matching. After correcting for biases in the classification, 55% of our sample consists of Type Ia supernovae and 45% of core-collapse SNe. The redshift distribution of the SNe Ia reaches z ~ 1.6, with a median of z ~ 1.2. The core-collapse SNe reach z ~ 1.0, with a median of z ~ 0.5. Our SN sample is comparable to the Hubble Space Telescope/GOODS sample both in size and redshift range. The redshift distributions of the SNe in the SDF and in GOODS are consistent, but there is a trend (which requires confirmation using a larger sample) for more high-z SNe Ia in the SDF. This trend is also apparent when comparing the SN Ia rates we derive to those based on GOODS data. Our results suggest a fairly constant rate at high redshift that could be tracking the star-formation rate. Additional epochs on this field, already being obtained, will enlarge our SN sample to the hundreds, and determine whether or not there is a decline in the SN Ia rate at z >~ 1.
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We present measurements of the rates of high-redshift Type Ia supernovae derived from the Subaru/XMM-Newton Deep Survey (SXDS). We carried out repeat deep imaging observations with Suprime-Cam on the Subaru Telescope, and detected 1040 variable objects over 0.918 deg$^2$ in the Subaru/XMM-Newton Deep Field. From the imaging observations, light curves in the observed $i$-band are constructed for all objects, and we fit the observed light curves with template light curves. Out of the 1040 variable objects detected by the SXDS, 39 objects over the redshift range $0.2 < z < 1.4$ are classified as Type Ia supernovae using the light curves. These are among the most distant SN Ia rate measurements to date. We find that the Type Ia supernova rate increase up to $z sim 0.8$ and may then flatten at higher redshift. The rates can be fitted by a simple power law, $r_V(z)=r_0(1+z)^alpha$ with $r_0=0.20^{+0.52}_{-0.16}$(stat.)$^{+0.26}_{-0.07}$(syst.)$times 10^{-4} {rm yr}^{-1}{rm Mpc}^{-3}$, and $alpha=2.04^{+1.84}_{-1.96}$(stat.)$^{+2.11}_{-0.86}$(syst.).
The observed delay-time distribution (DTD) of Type-Ia supernovae (SNe Ia) is a valuable probe of SN Ia progenitors and physics, and of the role of SNe Ia in cosmic metal enrichment. The SN Ia rate in galaxy clusters as a function of cluster redshift is an almost-direct measure of the DTD, but current estimates have been limited out to a mean redshift z=1.1, corresponding to time delays, after cluster star-formation, of over 3.2 Gyr. We analyze data from a Hubble Space Telescope monitoring project of 12 galaxy clusters at z=1.13-1.75, where we discover 29 SNe, and present their multi-band light curves. Based on the SN photometry and the apparent host galaxies, we assess cluster membership and SN type, finding 11 cases that are likely SNe Ia in cluster galaxies and 4 more cases which are possible but not certain cluster SNe Ia. We conduct simulations to estimate the SN detection efficiency, the experiments completeness, and the photometric errors, and perform photometry of the cluster galaxies to derive the cluster stellar masses. Separating the cluster sample into high-z and low-z bins, we obtain rest-frame SN Ia rates per unit formed stellar mass of $2.2 ^{+2.6}_{-1.3}times 10^{-13}{rm yr}^{-1}{rm M}_odot^{-1}$ at a mean redshift z=1.25, and $3.5^{+6.6}_{-2.8} times 10^{-13}{rm yr}^{-1}{rm M}_odot^{-1}$ at z=1.58. Combining our results with previous cluster SN Ia rates, we fit the DTD, now down to delays of 1.5 Gyr, with a power-law dependence, $t^alpha$, with $alpha=-1.30^{+0.23}_{-0.16}$. We confirm previous indications for a Hubble-time-integrated SN Ia production efficiency that is several times higher in galaxy clusters than in the field, perhaps caused by a peculiar stellar initial mass function in clusters, or by a higher incidence of binaries that will evolve into SNe Ia.
Supernova (SN) rates are a potentially powerful diagnostic of star formation history (SFH), metal enrichment, and SN physics, particularly in galaxy clusters with their deep, metal-retaining potentials, and simple SFH. However, a low-redshift cluster SN rate has never been published. We derive the SN rate in galaxy clusters at 0.06<z<0.19, based on type Ia supernovae (SNe Ia) that were discovered by the Wise Observatory Optical Transient Survey. As described in a separate paper, a sample of 140 rich Abell clusters was monitored, in which six cluster SNe Ia were found and confirmed spectroscopically. Here, we determine the SN detection efficiencies of the individual survey images, and combine the efficiencies with the known spectral properties of SNe Ia to calculate the effective visibility time of the survey. The cluster stellar luminosities are measured from the Sloan Digital Sky Survey (SDSS) database in the griz SDSS bands. Uncertainties are estimated using Monte-Carlo simulations in which all input parameters are allowed to vary over their known distributions. We derive SN rates normalized by stellar luminosity, in SNU units (SNe per century per 10^10 L_sun) in five photometric bandpasses, of 0.36+/-(0.22,0.14)+/-0.02 (B), 0.351+/-(0.210,0.139)+/-0.020 (g), 0.288+/-(0.172,0.114)+/-0.018 (r), 0.229+/-(0.137,0.091)+/-0.014 (i), 0.186+/-(0.111,0.074)+/-0.010 (z), where the quoted errors are statistical and systematic, respectively. The SN rate per stellar mass unit, derived using a color-luminosity-mass relation, is 0.098+/-(0.059,0.039)+/-0.009 SNe (century 10^10 M_sun)^-1. The low cluster SN rates we find are similar to, and consistent with, the SN Ia rate in local elliptical galaxies.
We present the analysis of the first set of low-redshift Type Ia supernovae (SNe Ia) by the Carnegie Supernova Project. Well-sampled, high-precision optical (ugriBV) and near-infrared (NIR; YJHKs) light curves obtained in a well-understood photometric system are used to provide light-curve parameters, and ugriBVYJH template light curves. The intrinsic colors at maximum light are calibrated to compute optical--NIR color excesses for the full sample, thus allowing the properties of the reddening law in the host galaxies to be studied. A low value of Rv~1.7, is derived when using the entire sample of SNe. However, when the two highly reddened SNe in the sample are excluded, a value Galactic standard of Rv~3.2 is obtained. The colors of these two events are well matched by a reddening model due to circumstellar dust. The peak luminosities are calibrated using a two-parameter linear fit to the decline rates and the colors, or alternatively, the color excesses. In both cases, dispersions in absolute magnitude of 0.12--0.16 mag are obtained, depending on the filter-color combination. In contrast to the results obtained from color excesses, these fits give Rv~1--2, even when the two highly reddened SNe are excluded. This discrepancy suggests that, beyond the normal interstellar reddening produced in the host galaxies, there is an intrinsic dispersion in the colors of SNe Ia which is correlated with luminosity but independent of the decline rate. Finally, a Hubble diagram is produced by combining the results of the fits for each filter. The resulting scatter of 0.12 mag appears to be limited by peculiar velocities as evidenced by the strong correlation between the distance-modulus residuals among the different filters. The implication is that the actual precision of SN Ia distances is 3--4%.
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