The Astrophysical Journal Supplement Series, 192:1 (29pp), 2011 January doi:10.1088/0067-0049/192/1/1
C
2011. The American Astronomical Society. All rights reserved. Printed in the U.S.A.
SUPERNOVA CONSTRAINTS AND SYSTEMATIC UNCERTAINTIES FROM THE FIRST THREE YEARS OF
THE SUPERNOVA LEGACY SURVEY
∗
A. Conley
1,2
,J.Guy
3
, M. Sullivan
4
, N. Regnault
3
,P.Astier
3
, C. Balland
3,5
,S.Basa
6
, R. G. Carlberg
1
, D. Fouchez
7
,
D. Hardin
3
,I.M.Hook
4,8
, D. A. Howell
9,10
,R.Pain
3
, N. Palanque-Delabrouille
11
, K. M. Perrett
1,12
, C. J. Pritchet
13
,
J. Rich
11
, V. Ruhlmann-Kleider
11
, D. Balam
13
, S. Baumont
14
,R.S.Ellis
4,15
, S. Fabbro
13,16
, H. K. Fakhouri
17
,
N. Fourmanoit
3
,S.Gonz
´
alez-Gait
´
an
1
, M. L. Graham
13
,M.J.Hudson
18
,E.Hsiao
17
, T. Kronborg
3
,C.Lidman
19
,
A. M. Mourao
16
,J.D.Neill
20
, S. Perlmutter
17,21
, P. Ripoche
3,17
, N. Suzuki
17
, and E. S. Walker
4,22
1
Department of Astronomy and Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4, Canada; alexander.conley@colorado.edu
2
Center for Astrophysics and Space Astronomy, University of Colorado, 593 UCB, Boulder, CO 80309-0593, USA
3
LPNHE, Universit
´
e Pierre et Marie Curie Paris 6, Universit
´
e Paris Diderot Paris 7, CNRS-IN2P3, 4 place Jussieu, 75252 Paris Cedex 05, France
4
Department of Astrophysics, University of Oxford, Keble Road, Oxford OX1 3RH, UK
5
Universit
´
e Paris 1, Orsay, F-91405, France
6
LAM, CNRS, BP8, P
ˆ
oledel’
´
Etoile Site de Ch
ˆ
ateau-Gombert, 38, rue Fr
´
ed
´
eric Joliot-Curie, 13388 Marseille Cedex 13, France
7
CPPM, CNRS-IN2P3 and Universit
´
e Aix-Marseille II, Case 907, 13288 Marseille Cedex 9, France
8
INAF-Osservatorio Astronomico di Roma, via Frascati 33, 00040 Monteporzio (RM), Italy
9
Las Cumbres Observatory Global Telescope Network, 6740 Cortona Dr., Suite 102, Goleta, CA 93117, USA
10
Department of Physics, University of California, Santa Barbra, Broida Hall, Mail Code 9530, Santa Barbara, CA 93106-9530, USA
11
CEA, Centre de Saclay, Irfu/SPP, F-91191 Gif-sur-Yvette, France
12
Network Information Operations, DRDC Ottawa, 3701 Carling Avenue, Ottawa, ON, K1A 0Z4, Canada
13
Department of Physics and Astronomy, University of Victoria, P.O. Box 3055 STN CSC, Victoria BC, V8T 1M8, Canada
14
LPSC, CNRS-IN2P3, 53 rue des Martyrs, 38026 Grenoble Cedex, France
15
Department of Astrophysics, California Institute of Technology, MS 105-24, Pasadena, CA 91125, USA
16
CENTRA-Centro M. de Astrofisica and Department of Physics, IST, Lisbon, Portugal
17
LBNL, 1 Cyclotron Road, Berkeley, CA 91125, USA
18
Department of Physics and Astronomy, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada
19
Australian Astronomical Observatory, P.O. Box 296, Epping, NSW 1710, Australia
20
California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA
21
Department of Physics, University of California, Berkeley, 366 LeConte Hall MC 7300, Berkeley, CA 94720-7300, USA
22
Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy
Received 2010 August 6; accepted 2010 October 22; published 2010 December 13
ABSTRACT
We combine high-redshift Type Ia supernovae from the first three years of the Supernova Legacy Survey (SNLS)
with other supernova (SN) samples, primarily at lower redshifts, to form a high-quality joint sample of 472 SNe
(123 low-z, 93 SDSS, 242 SNLS, and 14 Hubble Space Telescope). SN data alone require cosmic acceleration
at >99.999% confidence, including systematic effects. For the dark energy equation of state parameter (assumed
constant out to at least z = 1.4) in a flat universe, we find w =−0.91
+0.16
−0.20
(
stat
)
+0.07
−0.14
(
sys
)
from SNe only, consistent
with a cosmological constant. Our fits include a correction for the recently discovered relationship between host-
galaxy mass and SN absolute brightness. We pay particular attention to systematic uncertainties, characterizing
them using a systematic covariance matrix that incorporates the redshift dependence of these effects, as well as the
shape–luminosity and color–luminosity relationships. Unlike previous work, we include the effects of systematic
terms on the empirical light-curve models. The total systematic uncertainty is dominated by calibration terms.
We describe how the systematic uncertainties can be reduced with soon to be available improved nearby and
intermediate-redshift samples, particularly those calibrated onto USNO/SDSS-like systems.
Key words: cosmological parameters – cosmology: observations – supernovae: general
Online-only material: color figures, machine-readable table
1. INTRODUCTION
The Supernova Legacy Survey (SNLS) is a five year program
to measure the expansion history of the universe using Type Ia
supernovae (SNe Ia). The goal of this survey is to measure the
time-averaged equation of state of dark energy w to 0.05 (statisti-
cal uncertainties only) in combination with other measurements
∗
Based on observations obtained with MegaPrime/MegaCam, a joint project
of CFHT and CEA/DAPNIA, at the Canada–France–Hawaii Telescope
(CFHT) which is operated by the National Research Council (NRC) of
Canada, the Institut National des Sciences de l’Univers of the Centre National
de la Recherche Scientifique (CNRS) of France, and the University of Hawaii.
This work is based in part on data products produced at the Canadian
Astronomy Data Centre as part of the Canada–France–Hawaii Telescope
Legacy Survey, a collaborative project of NRC and CNRS.
and to 0.10 including systematic effects. The fundamental na-
ture of dark energy, which makes up 3/4 of the mass-energy
budget of the universe, remains almost completely mysterious.
A solid measurement that w =−1 (which would rule out the
cosmological constant) would have profound implications for
cosmology and particle physics. SNLS completed data acqui-
sition in 2008 June; this paper presents SN-only cosmological
results from the first three years of operation (SNLS3).
All analysis in this paper is in the context of standard
cosmological models—i.e., we assume that the universe is
homogenous on large scales and that general relativity is correct.
SNe are used to measure the cosmological parameters by
comparing their apparent brightnesses over a range of redshifts.
Hence, it is very useful to include additional SN samples besides
1
The Astrophysical Journal Supplement Series, 192:1 (29pp), 2011 January Conley et al.
SNLS in the analysis, particularly nearby SNe (z<0.1). This
paper has two primary goals. The first is to place SNe from
the literature on a common framework and demonstrate the
resulting constraints. The second is to present the systematic
uncertainties on the SN measurements in detail. This is the
second in a series of three SNLS cosmology papers based on
the first three years of data. The SNLS data sample used in this
analysis is presented in Guy et al. (2010, hereafter G10), and
the SN constraints are combined with other measurements in
M. Sullivan (2011, in preparation, hereafter S11), primarily the
WMAP7 measurement of the cosmic microwave background
(CMB), and baryon acoustic oscillations measured with galaxy
redshift surveys. In addition to these three papers, the calibration
of SNLS data is discussed in Regnault et al. (2009, hereafter
R09), and the simulations used to evaluate our survey selection
effects in Perrett et al. (2010).
Searches for additional parameters beyond light-curve shape
and color have been ongoing for two decades, and recently,
Kelly et al. (2010) have found evidence that SN residuals from
the Hubble diagram are correlated with host-galaxy mass. The
physical cause of this effect is as yet unknown. Sullivan et al.
(2010) analyze this issue using the SNLS3 sample and confirm
this result at higher significance. They find that splitting the
sample by host-galaxy mass and allowing the peak absolute
magnitudes of the two samples to differ corrects for these effects.
We adopt this approach in this paper; see Section 3.2 for details.
This effect has also been confirmed in the Sloan Digital Sky
Survey (SDSS) SN sample (Lampeitl et al. 2010).
Here, we give a brief overview of the data sets used in this
analysis. SNLS combines photometry from the deep component
of the Canada–France–Hawaii Telescope (CFHT) Legacy sur-
vey with extensive spectroscopic follow-up from the Keck, Very
Large Telescope (VLT), and Gemini telescopes to determine SN
types and measure redshifts. The photometry is carried out with
MegaCam, a 1 deg
2
imager at the prime focus of CFHT. SNLS
is a rolling search, like most other modern high-redshift surveys,
which means that the same telescope is used to simultaneously
find new SNe candidates and to follow those already discovered,
resulting in a large multiplex efficiency advantage. Details of the
procedures used to find and prioritize new SNe can be found in
Sullivan et al. (2006a), and those used to type candidates from
their spectra in Howell et al. (2005) and Balland et al. (2009).
In addition to SNLS, we consider three additional data
sets: low-z, SDSS, and high-redshift SNe from Hubble Space
Telescope (HST). The low-z SNe, which we define here as
coming from surveys with the bulk of their SN below z = 0.1,
come from a heterogeneous combination of non-rolling surveys
which generally use different telescopes to find and follow
SN candidates. The HST SNe (Riess et al. 2007)areat
higher redshifts than any of the other samples. They contribute
relatively little to measurements of
w
, but are quite useful
when trying to measure any redshift evolution of w. The SDSS
SN survey (Holtzman et al. 2008; Kessler et al. 2009, hereafter
K09) occupies intermediate redshifts (0.1 <z<0.4) between
SNLS and the low-z SN, and is also a rolling search.
As SN samples have grown in size, characterizing and in-
corporating systematic uncertainties properly has grown in im-
portance. There are many aspects of SNLS which are designed
to reduce the effects of systematic uncertainties compared with
previous SN projects, but they are still roughly comparable to
the statistical uncertainties. A major lesson of this paper is that
most of the systematic effects which limit the current analysis
are related to the current low-redshift SN sample, particularly
in terms of cross-calibration requirements. Absolute calibration
is unimportant for our purposes, but the relative calibration of
different systems and observations at different wavelengths is
critical. The current low-z sample is dominated by SNe largely
calibrated to the Landolt system (Landolt 1992), which induces
many complications in our calibration (see R09 for more de-
tails). As the low-z SN sample is replaced by better calibrated
samples in the next few years, it will be possible to cross-
calibrate the various samples much more accurately, which will
substantially increase the legacy value of the SNLS sample. The
dominant uncertainties will probably then relate to the host-
galaxy–SN brightness relation and SN modeling, particularly
the thorny issue of SN colors.
A critical step in any SN cosmological analysis is light-curve
fitting, the conversion of a time series of photometric (and pos-
sibly spectroscopic) observations into a set of model parameters
for each SN which are used to estimate a relative distance. Be-
cause SN physics is sufficiently complicated, theoretical models
have so far offered relatively little guidance for this process. As
a result, all current models used for light-curve fitting are em-
pirical in nature. In this paper, we consider updated versions
of two models: SiFTO (Conley et al. 2008) and SALT2 (Guy
et al. 2007). While these models share a common overall phi-
losophy, there are many significant differences between them.
They are compared in detail in Section 5 of G10, and we include
the differences in the light-curve parameters in our uncertainty
budget. Because these models are trained on SN data, they are
affected by the same sources of systematic effects as the cosmo-
logical analysis. This is the first analysis to include this effect in
the analysis; previous analyses have therefore underestimated
their uncertainties by holding the light-curve model fixed when
modeling systematics.
There has been some variation in the literature as to how SN
systematic effects are treated. Examples of the most common
approach, which we will refer to as the quadrature method, can
be found in Perlmutter et al. (1999), Astier et al. (2006), Wood-
Vasey et al. ( 2007), and K09. This approach has a number of
disadvantages (and the advantage that it is relatively easy to
understand). The most important disadvantage is that it is dif-
ficult for subsequent consumers of SN relative distance moduli
to incorporate systematic uncertainties into their analyses. In
this paper, we rectify these deficiencies by modeling systematic
effects using a covariance matrix.
In Section 2, we describe the data sets included in this
analysis and the steps taken to bring them onto a common
system with the SNLS data. We then describe the combined
data set in Section 3, presenting the statistical constraints on
dark energy from SNe Ia alone, and the combined statistical and
systematic uncertainties in Section 4 along with a description of
our systematics methodology. In section Section 5 we describe
individual systematic terms in detail and in Section 6 we
compare our analysis with previous ones. Finally, in Section 7
we discuss ways in which the effects discussed in this paper can
be improved with the enhanced low- and intermediate-redshift
SN data sets which will be available in the near future.
2. DATA SETS
Ideally, all SNe would be observed with a single camera
on a single telescope. While this may be possible with future
programs such as LSST or Pan-STARRS, at the moment it
is not practical because no individual telescope can currently
obtain a large sample at both high and low redshifts. To obtain
2
The Astrophysical Journal Supplement Series, 192:1 (29pp), 2011 January Conley et al.
precision cosmological constraints, particularly on w,wemust
incorporate additional SN data besides SNLS. Rather than
including all available SN data, we have chosen to incorporate
only external surveys which cover different redshift ranges
than SNLS. Other surveys which cover substantially the same
redshift range as SNLS have fewer SNe, larger photometric
uncertainties, less certain calibration, and have at most two-band
coverage (compared with the 4 bands of SNLS). Including them
in our analysis would reduce our statistical uncertainties only
marginally and would introduce additional systematic effects
which would have to be analyzed in detail. Nearby SNe are
currently the most important addition because the combination
of low redshifts and the small redshift range means that they
provide a very good constraint on the absolute magnitudes of
SNe Ia,
23
which is largely independent of the density parameters
and w.
There has been a recent encouraging trend toward providing
photometry in the natural system of the detectors used to
obtain it—that is, rather than applying linear relations to
transform instrumental magnitudes into some standard system,
the calibration is transferred from the standard system to
the instrumental system, which is much more reliable. Using
natural system photometry involves slightly more work in the
analysis, but allows for a considerable improvement in accuracy
and precision, as long as the natural system response is well
measured. We use the natural systems of the SNLS, HST, SDSS,
Hicken et al. (2009b), and Contreras et al. (2010) samples in our
analysis, which together account for ∼90% of our sample.
In this section, we present the data samples we include in
our analysis and describe the steps we carry out to bring them
to a common system with the SNLS measurements. The most
critical items relate to calibration, but in addition it is important
to understand and apply a correction for the selection biases
of the external samples (i.e., Malmquist bias). The calibration
used in this analysis relies heavily on the CALSPEC program
based on HST observations of pure hydrogen white dwarfs
(Bohlin 1996). In particular, we use BD 17
◦
4708 as our primary
reference standard. We first describe our cuts (Section 2.1),
introduce the data samples (SNLS, nearby, HST, and SDSS,
Sections 2.2–2.5), and then discuss how we estimate selection
effects for these samples (Section 2.7) and the peculiar velocity
corrections we apply to nearby SNe (Section 2.8). All of these
have related systematic uncertainties, which are discussed in
Section 5.
2.1. Selection Requirements
The selection requirements (cuts) applied in our analysis have
already been described in Section 4.5 of G10; these are de-
signed to ensure adequate phase and wavelength coverage to
allow accurate parameter measurement. A particularly impor-
tant requirement is that each SN has data between −8 and +5
rest-frame days of peak brightness to avoid biasing the recov-
ered light-curve parameters, which primarily affects the low-z
sample—see G10 for further discussion.
As is the case for SNLS, we require spectroscopic confirma-
tion of all SNe. Since each SN team has its own scheme for
spectroscopic classification, it is not clear how to best ensure
uniform classification standards without re-examining all of the
raw spectra. Based on direct comparison of spectra, the classifi-
cation requirement applied to SNLS (SN IaBalland et al. 2009,
23
More precisely, they constrain a combination of the absolute magnitudes
and the Hubble constant, H
0
; we refer to this combination as M.
or CI=3 in the scheme of Howell et al. 2005) lies somewhere
between the “gold” and “silver” classification scheme of Riess
et al. (2007) for spectroscopically typed SN. We have chosen
to largely accept the classifications of the original authors with
a few caveats. First, as is the case for all SNLS SNe, we do
not include candidates whose type is based either purely on the
photometric properties of the SN or the red color or elliptical
morphology of its host galaxy; the latter standard is particu-
larly worrisome because some nearby ellipticals show evidence
of star formation, and this fraction may increase with redshift.
Recently, Kawabata et al. (2010) have reported the discovery
of an (unusual) core-collapse SN in an elliptical host, which
strengthens our caution.
Because we apply a peculiar velocity correction, we place
our minimum redshift cut at z
cut
= 0.010, somewhat lower than
the usual value of 0.015–0.020. There has been some recent
controversy relating to the minimum allowable redshift, related
to the potential for a discontinuous step in the local expansion
rate (a so-called Hubble bubble) detected by Jha et al. (2007).
Riess et al. (2007)usez
cut
= 0.023 in order to avoid this issue,
which removes 40% of the nearby sample. Conley et al. (2007)
arguethat the Hubble bubbleis an artifact of the treatment of SNe
colors combined with selection effects in the nearby sample, and
Hicken et al. (2009a) show that adding more nearby SNe shifts
the position and sign of the putative bubble considerably. K09
take a slightly more agnostic position (Sections 9.1 and 9.2),
but find significant variation in the cosmological parameters
with the minimum redshift cut, and therefore adopt z
cut
= 0.02
while including the variation as a major source of systematic
uncertainty. We also see similar variation with z
cut
in our sample,
but find it to be consistent with shot noise, and see no evidence
that such conservatism is warranted (Section 5.3). SNe with
z<z
cut
are still used to train the light-curve fitters, since we do
not use distance information in this process.
We exclude known peculiar SNe by hand, such as SNe 2000cx
and 2002cx, rather than using automated quality of fit (χ
2
)
cuts. In our experience, the uncertainties for low-z photometry
are sufficiently inaccurate that such cuts are often misleading.
Furthermore, many low-z SNe have the occasional outlying
photometric observation which has little to no effect on the
derived parameters but which drives the χ
2
of the light-curve
fit to large values. A χ
2
-based cut will also affect different
SN samples very asymmetrically because the signal-to-noise of
the photometry varies strongly between samples, and hence
may introduce bias. Since SNe Ia are not perfect standard
candles, we should include some additional scatter in their
corrected peak magnitudes for cosmological purposes. This is
often called “intrinsic scatter” (σ
int
) even though it probably
represents the gaps in our understanding of SN physics and
photometry rather than anything truly intrinsic. It is impossible
for any fully automated set of cuts to catch all the pathologies
of such a large and diverse collection of SNe, so we have also
inspected all of the SNe by eye to remove problems not caught by
our cuts. In any case, we remove the known spectroscopically
peculiar SNe 2000cx (Li et al. 2001), 2001ay (K. Krisciunas
et al. 2011, in preparation), 2002cx (Li et al. 2003), 2005hk
(Phillips et al. 2007), 2005gj (Aldering et al. 2006), 03D3bb
(Howelletal.2006), 05D1by (which is spectroscopically similar
to SN 2001ay), and SN 2006X because it shows evidence for a
light-echo (Wang et al. 2008). Furthermore, we require all SNe
to have estimated photometric uncertainties.
We apply Chauvenet’s criterion (Taylor 1997) to reject
outliers, removing SNe for which we could expect less than half
3
The Astrophysical Journal Supplement Series, 192:1 (29pp), 2011 January Conley et al.
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4
redshift
0
5
10
15
20
25
30
N
Low−z
SDSS
SNLS
HST
−0.2 0.0 0.2 0.4
Color (mag)
0
20
40
60
80
100
N
Low−z
SDSS
SNLS
HST
Figure 1. Redshift and color histograms of the samples used in this analysis. The left panel shows the redshift histogram after all cuts are applied. The first bin for
the low-z SNe contains 108 SNe. The right panel shows the color histogram before the color cut is applied (but removing known peculiar SNe as well as those with
insufficient coverage), and the cut is indicated by the vertical dashed lines.
(A color version of this figure is available in the online journal.)
Tab le 1
Effects of Cuts on Different Samples
Data Set Initial
a
Final
b
Coverage
c
Fail
d
z Cut s Color Outliers
Low-z 323 123 99 9 54 32 31 1
SDSS 101 93 6 0 0 4 1 1
SNLS 279 242 16 10 0 6 9 3
HST 35 14 5 12 11 3 0 1
Notes. The number of SNe removed by each selection criterion. Many SNe fail
multiple cuts.
a
The initial number of SNe, after the removal of known peculiar SNe, SNe with
clear photometric inconsistencies, and SNe which have better photometry from
other samples.
b
The number of SNe satisfying all selection criterion.
c
SNe which did not have data close enough to peak brightness.
d
SNe for which the fits could not provide reliable light-curve parameters, either
because of an insufficient number of epochs of data or insufficient wavelength
coverage to measure a color.
of an event in our full sample (assuming a Gaussian distribution
of intrinsic luminosities). This corresponds to a cut at about
3.2σ , which removes six SNe: one low-z (SN 2006cj), one
from SDSS (SDSS5635), three from SNLS (03D4au, 04D4gz,
05D2ei), and one from the HST sample (McEnroe), many
more than the 0.5 objects one would expect if the distribution
were truly Gaussian. Of the SNLS SNe, two of these have
slightly less secure spectroscopic identifications (SNIa) and
are faint relative to the best fit, so may either be non-SN Ia or
spectroscopically peculiar SN Ia that our spectra were not high
enough signal-to-noise to identify. Note that lensing effects are
too small to explain these outliers, as the expected number of
3.2σ or greater outliers due to lensing is much less than 1 for
our sample.
We require the Galactic reddening along the line of sight to
satisfy E
(
B − V
)
MW
< 0.2 mag because of concerns that the
assumed Galactic value of R
V
= 3.1 might not be appropriate
for highly extinguished objects. Next, we require 0.7 <s<1.3,
where s (stretch) is the light-curve width parameter for SiFTO;
neither SiFTO nor SALT2 produce reliable fits for SNe outside
that stretch range. Finally, we require −0.25 <
(
B − V
)
Bmax
<
0.25 because of concerns that very blue SNe are not represented
in our training sample and that the colors of very red SNe may
represent a combination of different effects, both affecting the
peak magnitudes of the SNe; see Conley et al. (2008) for further
discussion of the latter. Histograms for the redshift and color
distributions of the input samples are shown in Figure 1, and
the effects of the cuts are given in Table 1. The requirement of
good coverage near the peak luminosity has the largest effect,
mostly on the low-z sample. We also considered the effects
of the sharp color cut because of concerns that a high-redshift
SN with poorly measured colors might migrate across the cut
boundary, but found that the effect was negligible.
2.2. SNLS SNe
Systematics control is fundamental to the design of SNLS.
1. Because SNe are both discovered and photometrically
followed with only one telescope, we are able to concentrate
our efforts on thoroughly understanding that system (R09).
We can also avoid the difficulties associated with combining
observations from many telescopes onto a single system.
2. The survey is in four passbands (g
M
r
M
i
M
z
M
), allowing us
to measure colors for all SNe in our survey. We measure
different rest-frame passbands at different redshifts; at low
redshifts we are mostly sensitive to B − V and at high
redshifts to U − B. These measurements must be used
in a consistent fashion at all redshifts to obtain accurate
results. We use intermediate-redshift SNLS SNe as a
consistency check of this process, since they have high-
quality measurements of both U − B and B − V (G10).
3. There are four survey fields (D1–D4) distributed in R.A. to
allow year-round coverage. The results from the four fields
can be compared with each other (S11).
4. The survey is quite deep, which limits the effects of
Malmquist bias (Perrett et al. 2010)atz<0.6, the sweet
spot for measuring a constant w.
5. We obtain spectra for all of the SNe used in our analysis,
which allows us to limit non-Ia contamination, to search
for peculiar SNe such as 03D3bb (Howell et al. 2006), and
the comparison of the spectral energy distributions (SEDs)
of nearby and distant SNe as a test for evolution (Bronder
et al. 2008; Balland et al. 2009;Walkeretal.2010).
6. We have obtained much higher signal-to-noise spectra of
a subset of our SNe which can be used to study the near-
UV properties of SNe Ia in detail and allows more detailed
spectroscopic comparisons (Ellis et al. 2008; Sullivan et al.
2009).
7. Because SNLS is a rolling search, it is possible to go
back after an SN is discovered and study early-time, pre-
discovery photometry. Therefore almost all of our SNe have
very good early-time coverage, which can be used to test
4
The Astrophysical Journal Supplement Series, 192:1 (29pp), 2011 January Conley et al.
Tab le 2
Contributions to the Low-z SN Sample
Source Initial Number
a
All Uses Cosmology Fit
Cal
´
an/Tololo 29 19 17
CfAI 22 10 7
CfAII 43 18 15
CfAIII 172 60 58
CSP 20 20 14
Other 37 23 12
Total 323 150 123
Notes. The relative contributions of various sources to the low-z sample used in
this paper.
a
After the removal of duplicates, known peculiar SNe, and SNe with problem-
atical photometry.
evolutionary models which predict changes in the early-
time behavior (Conley et al. 2006b).
8. Because the duration of our survey is much longer than
the month timescale of SNe Ia, we can construct very deep
SN-free image stacks and get accurate colors for the host
galaxies. These can be turned into estimates of the mass and
star formation history and used to study the relation between
SN properties and host-galaxy environment to search for
host-dependent systematic effects (Sullivan et al. 2006b,
2010).
9. Our photometry allows us to construct improved empirical
models of SNe Ia light curves and spectra (Guy et al. 2007;
Conley et al. 2008), which is particularly important in the
near-UV.
SNLS is designed to obtain sufficient quality data to allow us
to investigate systematic effects within our own data, as well
as enabling a great deal of non-cosmological SN science too
extensive to list here.
We take the three year SNLS sample from G10 with only one
modification: we have updated the light-curve models to account
for the 2010 February update to the CALSPEC calibration
library. This update has almost no effect on the SNLS, SDSS,
and low-z SNe, but does have some on the HST SNe, which are
observed in the near-IR.
2.3. Low-z SNe
The current nearby SN set is dominated by five main samples:
Cal
´
an/Tololo (Hamuy et al. 1996, 29 SNe), CfAI (Riess et al.
1999, 22 SNe), CfAII (Jha et al. 2006, 44 SNe), CfAIII (Hicken
et al. 2009b, 185 SNe), and CSP (Contreras et al. 2010,35
SNe). In addition, we make some use of 37 SNe with modern
photometry from a mixture of other papers. As the numbers
above show, the CfAIII sample provides almost half of the
nearby sample, and it was tempting to include only these SNe to
simplify our analysis. However, the other samples contribute
to the training of the light-curve models disproportionately
because some have denser phase coverage or sample slightly
different wavelengths, and since it is necessary to characterize
their properties and systematic uncertainties to include them
in the training, there is no point in excluding them from the
cosmological analysis. The effects of the cuts of Section 2.1 are
broken down by each nearby sample in Table 2. We do not use
rest-frame observations in the U band of the nearby sample in
our analysis for the reasons discussed in Section 2.6.
A handful of the nearby SNe have photometry from multiple
sources. This is useful for estimating the uncertainty in the
zero points of different samples (Section 5.1.2), but also forces
us to make some decisions about which data to include.
Generally, nearby SNe have dense enough light-curve coverage
that adding additional points does not improve the uncertainties
significantly. Since σ
int
24
dominates the uncertainty budget for
these SNe, including data from multiple sources is not beneficial
unless the data samples complement each other in wavelength or
light-curve phase. Unless this is the case, or one of the data sets
is clearly superior, we generally prefer to only use data from one
of the five large surveys, where we have a better understanding
of the systematic uncertainties.
For the Cal
´
an/Tololo, CfAI, and CfAII samples, the data were
transformed by the authors from the natural instrumental system
into the Landolt (1992) system using linear transformations
derived from stars in a limited color range. Since SN and
stellar spectra are quite different, these linear transformations
are incorrect when applied to SNe, and will introduce an error
in the reported magnitudes which is correlated across the SN
sample. In order to use these samples in a precision analysis, we
must determine the effective passbands of the Landolt system,
described in Appendix A.
For the CfAIII and CSP samples, natural system photometry
is available, which we use in our analysis. To use the natural
systems, we need the magnitudes of our fundamental flux
standard in their natural system. For the CfAIII sample we use
the linear transformations given in Table 2 of Hicken et al.
(2009b), after verifying that the arbitrary additive constants
are identically zero. Fortunately, our fundamental flux standard
(BD 17
◦
4708) lies in the color range well measured by
these transforms so they should be reliable, which would not
necessarily be the case for very blue stars such as Vega or
the white dwarfs used in the CALSPEC program. For the CSP
sample, the magnitudes of BD 17
◦
4708 are already given in the
natural system.
2.4. HST SNe
SNe above z = 1 are difficult to observe from the ground.
Therefore, the most successful searches in this redshift range
have been carried out with HST. SNe at such high redshifts
are useful when studying any possible time variation of w,
so we include the 2 SNe from Blakeslee et al. (2003), 16
from Riess et al. (2004), and the 22 from Riess et al. (2007)
in our sample; the latter also re-reduces the photometry from
the previous papers, taking into account the nonlinearity of the
NICMOS camera. The HST sample extends from z = 0.2–1.55.
For z<0.7 SNe, rest-frame B is measured by the F606W
filter, which has a sufficiently broad response that the U band is
effectively included as well. This is unlike any of the other
SNe in our sample, and therefore may introduce additional
systematic effects in our light-curve fitting. Because these SNe
have virtually no impact on the cosmological parameters when
compared with the hundreds of SNe from other samples in
this redshift range, we exclude the 10 HST SNe with z<0.7.
Requiring spectroscopic type confirmation eliminates five SNe,
but ensures a consistent analysis. We also exclude SNe with
z>1.4 because we cannot estimate the Malmquist bias above
this redshift (Section 2.7), which eliminates one more SN.
One cut which should be revisited for the HST sample is
the requirement of having photometry close to the epoch of
24
SNe Ia are not perfect standard candles even after correction for the
empirical width– and color–luminosity relations. σ
int
represents the remaining
scatter in distance moduli, and is discussed further in Sections 3.1 and 3.4.
5
