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Comment: Submitted to the New Online Journal of Instrumentation, JINST. Work Supported by USDOE Grant Nos: DE-FG02-94ER40870, DE-FG02-91ER40609, DE-FG02-97ER41033, and DE-FG02-97ER41046
We present convincing evidence of unburned carbon at photospheric velocities in new observations of 5 Type Ia supernovae (SNe Ia) obtained by the Nearby Supernova Factory. These SNe are identified by examining 346 spectra from 124 SNe obtained before +2.5 d relative to maximum. Detections are based on the presence of relatively strong C II 6580 absorption "notches" in multiple spectra of each SN, aided by automated fitting with the SYNAPPS code. Four of the 5 SNe in question are otherwise spectroscopically unremarkable, with ions and ejection velocities typical of SNe Ia, but spectra of the fifth exhibits high-velocity (v > 20,000 km/s) Si II and Ca II features. On the other hand, the light curve properties are preferentially grouped, strongly suggesting a connection between carbon-positivity and broad band light curve/color behavior: ...
We present data from the Supernova Cosmology Project for five high redshift Type Ia supernovae (SNe Ia) that were obtained using the NICMOS infrared camera on the Hubble Space Telescope. We add two SNe from this sample to a rest-frame I-band Hubble diagram, doubling the number of high redshift supernovae on this diagram. This I-band Hubble diagram is consistent with a flat universe (ΩM, ΩΛ) = (0.29, 0.71). A homogeneous distribution of large grain dust in the intergalactic medium (replenishing dust) is incompatible with the data and is excluded at the 5σ confidence level, if the SN host galaxy reddening is corrected assuming RV = 1.75. We use both optical and infrared observations to compare photometric properties of distant SNe Ia with those of nearby objects. We find generally good agreement with the expected color evolution for all ...
We present a new measurement of the volumetric rate of Type Ia supernova up to a redshift of 1.7, using the Hubble Space Telescope (HST) GOODS data combined with an additional HST dataset covering the North GOODS field collected in 2004. We employ a novel technique that does not require spectroscopic data for identifying Type Ia supernovae (although spectroscopic measurements of redshifts are used for over half the sample); instead we employ a Bayesian approach using only photometric data to calculate the probability that an object is a Type Ia supernova. This Bayesian technique can easily be modified to incorporate improved priors on supernova properties, and it is well-suited for future high-statistics supernovae searches in which spectroscopic follow up of all candidates will be impractical. Here, the method is validated on both gro...
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