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arXiv:astro­ph/0406546
v1
24
Jun
2004
Draft version July 29, 2004
Preprint typeset using L A T E X style emulateapj v. 11/12/01
THE HUBBLE HIGHER-z SUPERNOVA SEARCH:
SUPERNOVAE TO z  1:6 AND CONSTRAINTS ON TYPE Ia PROGENITOR MODELS a
a BASED ON OBSERVATIONS WITH THE NASA/ESA HUBBLE SPACE TELESCOPE,
OBTAINED AT THE SPACE TELESCOPE SCIENCE INSTITUTE, WHICH IS OPERATED BY
AURA, INC., UNDER NASA CONTRACT NAS 5-26555
Louis-Gregory Strolger 2 , Adam G. Riess 2 , Tomas Dahlen 2 , Mario Livio 2 ,
Nino Panagia 2;3 , Peter Challis 4 , John L. Tonry 5 , Alexei V. Filippenko 6 ,
Ryan Chornock 6 , Henry Ferguson 2 , Anton Koekemoer 2 , Bahram Mobasher 2;3 ,
Mark Dickinson 2 , Mauro Giavalisco 2 , Stefano Casertano 2 , Richard Hook 7 ,
Stephane Blondin 8 , Bruno Leibundgut 8 , Mario Nonino 9 , Piero Rosati 8 , Hyron Spinrad 6 ,
Charles C. Steidel 10 , Daniel Stern 11 , Peter M. Garnavich 12 , Thomas Matheson 4 ,
Norman Grogin 13 , Ann Hornschemeier 13 , Claudia Kretchmer 13 , Victoria G. Laidler 14 ,
Kyoungsoo Lee 13 , Ray Lucas 2 , Duilia de Mello 13 , Leonidas A. Moustakas 2 ,
Swara Ravindranath 2 , Marin Richardson 2 , and Edward Taylor 15
2 Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (email: strolger@stsci.edu).
3 Aôliated with the Space Telescope Division of the European Space Agency, ESTEC, Noordwijk, the
Netherlands.
4 Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138.
5 University of Hawaii, Institute for Astronomy, 2680 Woodlawn Drive, Honolulu, HI 96822.
6 Department of Astronomy, University of California, 601 Campbell Hall, Berkeley, CA 94720-3411.
7 Space Telescope - European Coordinating Facility, European Southern Observatory, Karl Schwarzschild
Str.-2, D-85748, Garching, Germany.
8 European Southern Observatory, Karl-Schwarzschild-Str. 2, D-85748, Garching, Germany.
9 INAF, Astronomical Observatory of Trieste, Via Tiepolo 11 34131 Trieste, Italy
10 Department of Astronomy, California Institute of Technology, MS 105-24, Pasadena, CA 91125.
11 Jet Propulsion Laboratory, MS 169-506, California Institute of Technology, Pasadena, CA 91109.
12 University of Notre Dame, 225 Nieuwland Science Hall, Notre Dame, IN 46556
13 Johns Hopkins University, Dept. of Physics and Astronomy, 3400 N. Charles Street, Baltimore, MD 21218.
14 Computer Sciences Corporation at Space Telescope Science Institute, 3700 San Martin Drive, Baltimore,
MD 21218.
15 University of Melbourne, School of Physics, Victoria 3010, Australia.
Draft version July 29, 2004
ABSTRACT
We present results from the Hubble Higher-z Supernova Search, the rst space-based open eld survey
for supernovae (SNe). In cooperation with the Great Observatories Origins Deep Survey, we have used
the Hubble Space Telescope with the Advanced Camera for Surveys to cover  300 square arcmin in the
area of the Chandra Deep Field South and the Hubble Deep Field North on ve separate search epochs
(separated by  45 day intervals) to a limiting magnitude of F850LP  26. These deep observations
have allowed us to discover 42 SNe in the redshift range 0:2 < z < 1:6. As these data span a large
range in redshift, they are ideal for testing the validity of Type Ia supernova progenitor models with the
distribution of expected \delay times," from progenitor star formation to SN Ia explosion, and the SN
rates these models predict. Through a Bayesian maximum likelihood test, we determine which delay-
time models best reproduce the redshift distribution of SNe Ia discovered in this survey. We nd that
models that require a large fraction of \prompt" (less than 2 Gyr) SNe Ia poorly reproduce the observed
redshift distribution and are rejected at > 95% condence. We nd that Gaussian models best t the
observed data for mean delay times in the range of 3 to 4 Gyr.
Subject headings: Surveys|supernovae: general
1. introduction
Type Ia supernovae (SNe Ia) have proven that they are
unequivocally suited as precise distance indicators, ideal
for probing the vast distances necessary to measure the ex-
pansion history of the Universe. The results of the High-z
Supernova Search Team (Riess et al. 1998) and the Su-
pernova Cosmology Project (Perlmutter et al. 1999) have
astonishingly shown that the Universe is not decelerating
(and therefore not matter dominated), but is apparently
accelerating, driven apart by a dominant negative pres-
sure, or \dark energy." Complementary results from the
cosmic microwave background by WMAP (Bennett et al.
2003) and large-scale structure from 2dF (Peacock et al.
2001; Percival et al. 2001; Efstathiou et al. 2002) congru-
ously show evidence for a low matter
density(
M = 0:3)
and a non-zero cosmological
constant(
 = 0:7), but nei-
ther directly require the presence of dark energy.
However, it is possible that there are astrophysical ef-
fects which allow SNe Ia to appear systematically fainter
1

2 L. -G. Strolger et al.
with distance and therefore mimic the most convincing ev-
idence for the existence of dark energy. A pervasive screen
of \gray dust" scattered within the intergalactic medium
could make SNe Ia seem dim, but show little corresponding
reddening (Aguirre 1999). Alternatively, the progenitor
systems of SNe Ia could be changing with time, resulting
in evolving populations of events, and possibly necessitat-
ing modications to the empirical correlations which are
currently used to make SNe Ia precise standard candles.
To date, the investigations of either eect have only pro-
vided contrary evidence, disfavoring popular intergalactic
dust models (Riess et al. 2000), and statistically showing
strong similarity in SN Ia characteristics at all age epochs,
locally and at hzi  0:5 (Riess et al. 1998; Perlmutter et al.
1999; Riess et al. 2000; Aldering, Knop, & Nugent 2000;
Sullivan et al. 2003), but neither has been conclusively
ruled out.
A simple test of the high-redshift survey results would be
to search for SNe Ia at even higher redshifts, beyond z  1.
In the range 1 < z < 2, we should observe SNe Ia explod-
ing in an epoch of cosmic deceleration, thus becoming rela-
tively brighter than at lower redshifts. This is expected to
be unmistakably distinguishable from simple astrophysical
challenges to the SN Ia conclusion. Indeed, results from 19
SNe Ia observed in the range 0:7 < z < 1:2 from the latest
High-z Supernova survey (Tonry et al. 2003) and in the IfA
survey (Barris et al. 2004) show indications of past decel-
eration, but these SNe represent the highest-redshift bin
attainable from the ground, in which condent identica-
tion and light-curve parameters are pushed to their limits.
To thoroughly and reliably survey SNe Ia at 1 < z < 2,
and to perform the follow-up observations necessary for
such a study, requires observing deeper than can be fea-
sibly done with the ground-based telescopes. However,
with the Hubble Space Telescope (HST) and the Advanced
Camera for Surveys (ACS), a higher-z SN survey is prac-
tical. Through careful planning, the Great Observatories
Origins Deep Survey (GOODS) has been designed to ac-
commodate a deep survey for SNe with a specic emphasis
on the discovery and follow-up of z & 1 SNe Ia.
We discovered 42 SNe over the 8-month duration of the
survey. We also measured redshifts, both spectroscopic
and photometric, for all but one of the SN host galaxies.
For the rst time, we have a signicant sample of SNe Ia
spanning a large range in redshift, from a complete survey
with well understood systematics and limitations. Cer-
tainly this has allowed for precise measurement of the SN
rates and the rate evolution with redshift (See Dahlen et al.
2004), but it also allows for a comparison of the observed
SN Ia rate history to the star-formation rate history, and
thus an analysis of SN Ia assembly time, or \delay time,"
relative to a single burst of star formation. By explor-
ing the range and distribution of the time from progenitor
formation to SN Ia explosion that is required by the data,
we can provide clues to the nature of the mechanism (or
mechanisms) which produce SNe Ia.
We describe the Hubble Higher-z Supernova Search
(HHZSS) project in x 2, along with image processing and
reduction, transient detection, and SN identication meth-
ods. In x 3 we show the results of the survey, including
discovery information on all SNe, and multi-epoch, multi-
band photometry of SNe over the search epochs of the
survey. In x 4 we report on observational constraints on
the inherent SN frequency distribution, or the distribu-
tion \delay times" for SN Ia progenitors, and discuss the
implied constraints on the model SN Ia progenitor sys-
tems. Elsewhere, we report on the rates of SNe Ia and
core-collapse SNe, the comparison of these measured rates
to those made by other surveys, and to the predicted SN
formation-rate history partly predicted from the analysis
in this paper (Dahlen et al. 2004). In another paper we
report on the constraints of cosmological parameters and
the nature of high-z SNe Ia (Riess et al. 2004b).
2. goods and the \piggyback" transient survey
GOODS was designed to combine extremely deep multi-
wavelength observations to trace the galaxy formation his-
tory and the nature and distribution of light from star for-
mation and active nuclei (Giavalisco et al. 2004a). Using
HST/ACS, it has probed the rest-frame ultraviolet (UV)
to optical portion of high-redshift galaxies through obser-
vations in the F435W , F606W , F775W , and F850LP
bandpasses, with a goal of achieving extended source sen-
sitivities only 0.5{0.8 mag shallower than the original Hub-
ble Deep Field observations (Williams et al. 1996). Images
were obtained in 15 overlapping \tiled" pointings, cover-
ing a total eective area of  150 square arcmin per eld.
Two elds with high ecliptic latitude were observed, the
Chandra Deep Field South (CDFS) and the Hubble Deep
Field North (HDFN), to provide complementary data from
missions in other wavelengths (Chandra X-ray Observa-
tory, XMM-Newton, Spitzer Space Telescope) and to al-
low ground-based observations from both hemispheres (see
Figures 1 and 2).
The GOODS observations in the F850LP band were
scheduled over 5 epochs separated by  45 days to accom-
modate a \piggybacking" transient survey. This baseline
is ideal for selecting SNe Ia near peak at z  1, and SNe Ia
on the rise at z > 1:3, as the risetime (from explosion to
maximum brightness) for SNe Ia is  20 days in the rest
frame (Riess et al. 1999). The baseline also insures that
no SN in the desired redshift range will have suôcient
time to rise within our detection threshold, and then fall
beyond detection before the eld is revisited, maximizing
the overall yield.
Intentionally, the GOODS lter selections were nearly
ideal for the detection, identication, and analysis of high-
redshift SNe Ia. For a SN Ia at z  1, the F850LP band
covers nearly the same part of the SED as the rest-frame B
band. The K-correction, or the correction of the observed
ux to some rest-frame bandpass (e.g., F850LP to B), is
thus relatively small.
Monte Carlo simulations of the survey, assuming de-
tection limits based on the  2100 s exposure times per
epoch (using the ACS Exposure Time Calculator) and the
desired baseline between epochs, implied that the distribu-
tion of SNe Ia would be centered at z  1, with  1=3 to
1=2 of the events occurring in the 1 < z < 2 range. Scal-
ing from other lower-z SN survey yields, it was expected
that a total of 30{50 SNe of all types would be discovered,
and that  1=2 of them would be SNe Ia. These numbers
implied that we could expect to nd  6 to 8 SNe Ia in
the range of 1:2 . z . 1:8, which could be suôcient for
an initial investigation of cosmology in the deceleration

The Hubble Higher-z Supernova Search Project 3
epoch.
2.1. Image Processing and Search Method
The success of this survey has been due, in large part,
to the rapid processing and delivery of data, and the rapid
post-processing by a reliable pipeline. The exposures con-
stituting a single tile in a single passband arrived from
HST within 6 to 18 hours after observation (with an aver-
age of  10 hours), and fully processed (dierenced with
previous epochs) within a few hours after arrival. In gen-
eral, the complete multi-wavelength data for a single tile
were fully searched for candidate SNe within a day after
observation.
The individual exposures of a tile in a given epoch were
reduced (bias-subtracted and at-eld corrected) through
the calacs standard ACS calibration pipeline. The well-
dithered subexposures (or CR splits; see below) were then
corrected for geometric distortions and combined using
the multidrizzle pipeline (Koekemoer et al. 2002). For
the survey, we maintained the physical pixel size of 0:05 00
pixel 1 for the discovery of transients.
A key feature of this pipeline is its identication and
removal of cosmic rays (CRs) and hot pixels. Each 2100 s
exposure in F850LP consisted of 4 individual 520 s CR
splits, each dithered by small osets. In each of the CR
splits, the CR contamination, at the time of the survey,
was as high as  1% of all pixels, and hot pixels accounted
for an additional  1% (Riess 2002). With such a high in-
cidence of CRs and hot pixels, averaging (or taking the
median) over the few CR splits would not adequately re-
move these potential confusion sources. Instead, we used
the minmed algorithm described in Mack et al. (2003). Ba-
sically, of the pixels in each CR split covering the same area
of sky, the highest-value pixel was rejected. The median
of the remaining three pixels was then compared to the
minimum-value pixel. If the minimum pixel was within
6 of the median, then the median value was kept, oth-
erwise the minimum value was used. A second pass was
performed, repeating the minmed rejection on pixels neigh-
boring those which had been previously replaced with min-
imum values (indicating CR or hotpixel impact), but at
a lower threshold to remove \halos" around bright CRs.
The result was that each pixel of the output combined im-
age was either the median or the minimum of the input
values. Admittedly, the combined result was less sensi-
tive than can be obtained in a straight median, but the
multidrizzle algorithm (with minmed) did successfully
reject > 99% of CRs and hot pixels after combination.
The search was conducted in 8 campaigns (4 campaigns
for each of the HDFN and the CDFS surveys) by dier-
encing images from contiguous epochs. For a given tile
in a eld, images covering the same area from the previ-
ous epoch were aligned (registered) using the sources in
the tiles. Catalogs of the pixel centroids and instrumen-
tal magnitudes of sources on each image were made using
SExtractor (Bertin & Arnouts 1996) and fed into a trian-
gle matching routine (starmatch, courtesy of B. Schmidt)
which determined the linear registration transformation
from one epoch to the next. The typical precision of the
registrations was 0.2{0.3 pixels root-mean square (RMS),
and the point-spread function (PSF) in each epoch of ob-
servation remained nominally at 0.10{0:13 00 FWHM. The
combination of precise registration and nearly constant
PSF allowed for images to be subtracted directly, with-
out the need for image convolution.
Several examples of the image subtraction quality are
shown in Figures 3, 4, and 5. In ideal situations, only
transient sources remain in the residual image on a nearly
zero-level background. However, in practice there were
many situations which produced non-transient residuals.
Although extensive care was taken to remove many CRs
and hot pixels in the image processing, these artifacts did
occasionally slip past the rejection algorithms, specically
when multiple eects were coincident on the same area of
sky. For example, for a given pixel in each of the four CR
splits covering the same area of sky, the probability that
the pixels were impacted by a CR in 3 of the 4 exposures
is approximately 1 in 10 6 . Roughly 20 pixels in the com-
bined 20 million pixel array would show CR residuals after
passing through the multidrizzle algorithm. In addition,
\breathing" in the optical path, focus drift, and the slight
change in the pixel scale across the image plane have all
led to small yet detectable variations in the PSF. Some-
times bright compact objects were over-subtracted in the
wings of their radial proles and under-subtracted in the
inner 1{2 pixels. Other instrumental sources of confusion
include diraction spikes, correlated noise from multiple
image resampling, and slight registration errors due to the
lack of sources over a large registration area.
The non-trivial abundance of false positives required rig-
orous residual inspection methods. We therefore searched
the subtracted images redundantly to minimize false detec-
tion biases and to maximize recovery of elusive, faint tran-
sients. An automated routine was performed to identify
PSF-like residuals which were well separated ( 2 pixels)
from known saturated pixels, and above  4 5 of the sky
background. The inherent nature of this routine prohibits
the detection of nonstellar residuals, faint residuals, resid-
uals near bright stars or nuclei (which may be saturated),
or residuals in areas where the RMS of the background
could not be easily determined by the automated routine.
Therefore, so that no potential SNe were lost, several hu-
man searchers visually inspected each subtracted image.
At least two pairs of searchers independently scoured a
few residual tiles. Visual searching of only a few tiles in-
sured that it was done thoroughly, and helped to alleviate
monotony and fatigue.
Candidate SNe found by the software and the searchers
were then scrutinized based on the following set of criteria
to select SNe and further reject instrumental (and astro-
nomical) false positives:
(1) Misregistration: Areas with . 10 detectable sources
per arcmin 2 are typically poorly registered (& 0:5 pixel
RMS). Sources in these areas of the subtracted images are
under-subtracted on one side, and over-subtracted on the
other. If the total ux in an aperture encompassing the
source was not signicantly greater than a few times the
background RMS, it was assumed that the residual was an
artifact of misregistration.
(2) Cosmic ray residuals: The number of pixels in
 2100 s combined images that still contain CRs due to
impacts on the same regions of sky on one or more individ-
ual  520 s exposures is roughly 4500 2  (0:01) N , where
N is the number of impacted exposures (out of 4). This

4 L. -G. Strolger et al.
number can grow slightly when considering hot pixels and
bright pixels with CRs. To further reject these artifacts,
we required that candidates have no more than one con-
stituent exposure aected by CRs or hot pixels.
(3) Stellar prole: Residuals in the subtracted images
were required to show a radial prole consistent with the
PSF ( 2 pixels FWHM). Narrower proles were consid-
ered to be stacked noise (if not residual CRs) and wider
proles were typically poor subtractions from misregistra-
tions, breathing, or focus drift.
(4) Multiple epochs of detection: It was required that
each candidate be detected (to within 5) on each of
the CR split exposures that were not impacted by CRs
or hot pixels at the relevant location. Additional weight
was given to candidates that were clearly detected in the
F775W band, or additionally in the F606W band. How-
ever, this was not a strict criterion as it was expected that
SNe Ia at higher redshifts would become less detectable in
the bluer wavelengths (see Section 2.2).
(5) Variable galactic nuclei: Sources that were . 1 pixel
from their host nuclei were considered potential active
galactic nuclei (AGNs) and typically not included in the
target of opportunity follow-up program (see x2.3). How-
ever, these residuals were followed over subsequent search
epochs, and in all but one case, suôcient photometric evi-
dence (see x2.2) was found to classify them as SNe. Bright
residuals that were coincident with the nuclei of galax-
ies were also compared with known X-ray sources from
the Chandra Deep Field South and Chandra Deep Field
North 1 Megasecond catalogs (Brandt et al. 2001; Giacconi
et al. 2002). Indeed, the only variable source unidentied
by spectroscopic or photometric means was identied as
known X-ray source, and therefore rejected as the only
conrmed optically variable AGN in the survey.
(6) Solar-system objects and slow-moving stars: We re-
quired that our candidates show no proper motion. As-
suming we were sensitive to 1/2-pixel shifts, the proper
motion of any candidate could not be more than 0:025 00
over the  2100 s combined exposure, or ! < 0:043 00 hr 1
(0.1 deg yr 1 ). Hypothetically, if a source was bound to
the Sun (with tangential velocity  30 km hr 1 ), then
its distance would have to be D > [(30 km s 1 )/(0.043"
hr 1 )], or greater than 3; 400 AU. In addition, if the object
was illuminated by re ected sunlight, then its apparent
magnitude (m) would be related to its angular diameter
() by
m = m + 5 log(=2D); (1)
where m is the apparent magnitude of the Sun. Since
 must be consistent with the PSF ( 0:1 00 ), the source
would have to be  4 times larger than Jupiter (at the
distance assumed from the limits on proper motion), and
the apparent magnitude of the source would have to be
m  55 mag! Alternatively, using the limiting magnitude
for the survey, m lim  26 (see x 4.2), the source would
have to have an angular size of  > 18 ô in order to have
been lit by the Sun at its assumed distance.
A similar argument can be made for slow-moving stars.
Since ! = 0:043 00 hr 1 is the fastest a source could move
without being detected, in the  45 days since the eld was
last observed the source could have moved < 1000 pixels.
Our survey was clearly sensitive to negative residuals as
well as positive ones (a fact indicated by the frequent dis-
covery of SNe declining in brightness since the previous
epoch). We saw no negative candidates which were de-
tected within 1000 pixels of a positive source on the same
epoch of observation.
Most of these SNe have been observed on more than
one epoch, and all but two were detected within 3:5 00 of a
galaxy (presumably the host). It would be highly unlikely
for any of these to be objects moving within the Solar
System or the Galaxy.
2.2. Identication of SNe and Redshift Determination
SNe are generally classied by the presence or absence
of particular features in their optical spectra (see Filip-
penko 1997 for a review). Historically, the primary divi-
sion in type has been by the absence (SNe I) or presence
(SNe II) of hydrogen in their spectra, but the classica-
tion currently extends to at least 7 distinct subtypes (SN
IIL, IIP, IIn, IIb, Ia, Ib, and Ic). It is now generally ac-
cepted that the explosion mechanism is a more physical
basis by which to separate SNe. SNe Ia probably arise
from the thermonuclear explosion of carbon-oxygen white
dwarf stars, while all other types of SNe are produced by
the core collapse of massive stars (& 10M ).
There can be considerable challenges in the ground-
based spectroscopic identication of high-redshift SNe. As
the principal goal of this survey has been to acquire many
SNe Ia at z > 1, a fundamental prerequisite was that we
could make condent identications of at least this SN
type. Much to our benet, HST with the ACS G800L
grism provides superb spectra with signicantly higher
signal-to-noise ratio (S/N) than can currently be achieved
from the ground. Its limitation is the low spectral res-
olution (R  =
  200 per pixel, in rst order) and
the overlap of multiple spectral orders from other nearby
sources. Spectral resolution of  1500 km s 1 is not prob-
lematic for SNe with ejecta velocities of & 10; 000 km s 1 .
However, because of the spectral-order confusion and the
lack of a slit mask, the G800L grism could only be used for
SNe with substantial angular separation from their hosts
and from other nearby sources.
It was expected that SN candidates would generally be
either too faint to be spectroscopically observed from the
ground, or too close to their host galaxies or other nearby
sources to be identied with the ACS grism. We there-
fore had to rely on some secondary method by which to
identify SNe, specically to select likely SNe Ia from the
sample. The inherent dierences in the ejecta composi-
tions of SNe Ia and SNe II leads to an observable dierence
in their intrinsic early-time UV ux. As optical observa-
tions shift to the rest-frame UV for z & 1 SNe, the \UV
decit" in SNe Ia can be a useful tool for discriminat-
ing SNe Ia from SNe II, the most common types of core-
collapse (CC) SNe. Using a method pioneered by Panagia
(2003) and fully developed in Riess et al. (2004b), we use
the F850LP apparent magnitude, the F775W F850LP
and F606W F850LP colors, the measured redshift or
photometric redshift estimates (see below), and age con-
straints provided by the baseline between search epochs
to grossly identify SNe as either SNe Ia or SNe CC. This
method is only useful for z & 1 SNe near maximum light,
and is not foolproof in its identication. There are SNe CC
(e.g., luminous SNe Ib and Ic) which can occupy nearly the

The Hubble Higher-z Supernova Search Project 5
same magnitude-color space as SNe Ia. However, these
bright SN Ib/c make up only  20% of all SNe Ib/c,
which as a group are only  1=3 as plentiful as other
SNe CC (Cappellaro, Evans, & Turatto 1999).
From the ground, we have obtained spectroscopic iden-
tication of 6 SNe Ia and 1 SN CC in the redshift range
0.2{1.1 using Keck + LRIS (see Table 1). With HST/ACS
and the G800L grism, we have obtained excellent spectra
of 6 SNe Ia at z = 0:8{1.4, the most distant sample of
spectroscopically conrmed SNe; see Riess et al. (2004a).
These spectra cover only the 2500{5000  A range in the
rest frame, but they are of excellent S/N, unattainable for
such high-z SNe from the ground. These identications
also serve as an excellent proof of concept in the color-
magnitude selection.
Using Keck, the VLT, and the ACS grism, we have ob-
tained spectroscopic redshifts for 29 of the 42 SNe in our
sample. To our benet, part of the GOODS endeavor in-
volved obtaining extensive multi-wavelength photometry
spanning the U to the near-IR passbands to estimate the
photometric redshifts (\phot-z") of galaxies in the HDFN
and CDFS elds (Mobasher et al. 2004). The precision
of the phot-z from GOODS with respect to known spec-
troscopic redshifts has been within  0:1 RMS, with the
occasional instance ( 10% of a tested sample) where the
phot-z method misestimates the actual redshift by more
than 20%. In order to improve on the accuracy of the phot-
z measurements for the host galaxies, we remeasured the
multi-wavelength photometry by visually determining the
centroid of the host galaxies, and manually determining an
annulus in which the sky background is determined. This
allowed better photometric precision than was generally
achieved in the SExtractor-based automated cataloging.
Comparing the sample of 26 SN host galaxy spectroscopic
redshifts to the phot-z estimates from the improved pho-
tometry 16 resulted in a precision of 0.05 RMS (after re-
jecting two > 7 outliers), and only  5% of the sample
was misestimated by more than 10% (see Figure 6). The
redshifts of the remainder of the SN hosts (without spec-
troscopic redshifts) were determined in this way, with the
exception of SN 2002fv, whose host was not identied due
to the magnitude limits of the survey.
We t template light curves to grossly identify SNe
which were not spectroscopically identied, and were not
at z & 1 nor constrained near maximum light. Using
the light curves of SNe 1994D, 1999em, 1998S, and 1994I
as models for SNe Ia, IIP, IIL, and Ib/c (respectively),
we transformed these model SNe to the redshifts of the
observed SNe, correcting for the eects of time dilation,
and applying K-corrections to the rest-frame bandpasses
to produce light curves as they would have been seen
through the F850LP , F775W , and F606W bandpasses
at the desired redshifts. The K-corrections were deter-
mined from model spectra (Nugent, Kim, & Perlmutter
2002) for SNe Ia, and from color-age light-curve interpola-
tions for SNe CC. We have also made use of the web tool
provided by Poznanski et al. (2002) to check the derived
colors for the SNe CC. We visually determined the best-t
model light curve to the observed light curves, allowing
shifting along the time axis, magnitude osets, and ex-
tinction/reddening (assuming the Galactic extinction law)
along the magnitude axis. Best ts required consistency in
the light-curve shape and peak color (to within magnitude
limits) and in peak luminosity in that the derived absolute
magnitude in the rest-frame B band had to be consistent
with the observed distribution of absolute B-band magni-
tudes shown in Richardson et al. (2002).
Each discovered SN was given an identity rank (gold,
silver, or bronze) re ecting our condence in the identi-
cation. A gold rank indicated the highest condence that
the SN was the stated type, and it was not likely that the
SN could have been some other SN type. A silver rank in-
dicated the identity was quite condent, but the SN lacked
suôcient corroborating evidence to be considered gold. A
bronze rank indicated that there was evidence the SN type
was correct, but there was a signicant possibility that the
SN type was incorrect.
We were clearly condent of the SN type in cases where
a high S/N (& 20) spectrum conclusively revealed its type;
these SNe were gold, by denition. However, the majority
of SNe were without suôcient spectra to unambiguously
determine a type. We then used additional information on
the SN redshift, photometric data, and host-galaxy mor-
phology, seeking a consistent picture for a specic SN type.
We rst considered the possibility that a candidate was
a SN Ia. We required that the light-curv