The Astrophysical Journal Supplement Series, 199:25 (23pp), 2012 April doi:10.1088/0067-0049/199/2/25
C
2012. The American Astronomical Society. All rights reserved. Printed in the U.S.A.
THE CLUSTER LENSING AND SUPERNOVA SURVEY WITH HUBBLE: AN OVERVIEW
Marc Postman
1
,DanCoe
1
, Narciso Ben
´
ıtez
2
, Larry Bradley
1
, Tom Broadhurst
3
, Megan Donahue
4
, Holland Ford
5
,
Or Graur
6
, Genevieve Graves
7
, Stephanie Jouvel
8
, Anton Koekemoer
1
, Doron Lemze
5
, Elinor Medezinski
5
,
Alberto Molino
2
, Leonidas Moustakas
9
, Sara Ogaz
1
, Adam Riess
1,5
, Steve Rodney
5
, Piero Rosati
10
, Keiichi Umetsu
11
,
Wei Zheng
5
, Adi Zitrin
6
, Matthias Bartelmann
12
, Rychard Bouwens
13
, Nicole Czakon
8
, Sunil Golwala
8
,OleHost
14
,
Leopoldo Infante
15
, Saurabh Jha
16
, Yolanda Jimenez-Teja
2
, Daniel Kelson
17
, Ofer Lahav
14
, Ruth Lazkoz
3
,
Dani Maoz
6
, Curtis McCully
16
, Peter Melchior
18
, Massimo Meneghetti
19
, Julian Merten
12
, John Moustakas
20
,
Mario Nonino
21
, Brandon Patel
16
, Enik
¨
oReg
¨
os
22
, J ack Sayers
8
, Stella Seitz
23
, and Arjen Van der Wel
24
1
Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21208, USA; postman@stsci.edu
2
Instituto de Astrof
´
ısica de Andaluc
´
ıa (CSIC), C/Camino Bajo de Hu
´
etor 24, Granada 18008, Spain
3
Department of Theoretical Physics, University of the Basque Country, P. O. Box 644, 48080 Bilbao, Spain
4
Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824, USA
5
Department of Physics and Astronomy, The Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA
6
School of Physics and Astronomy, Tel Aviv University, Tel-Aviv 69978, Israel
7
Department of Astronomy, University of California, 601 Campbell Hall, Berkeley, CA 94720, USA
8
Jet Propulsion Laboratory, California Institute of Technology, MS 169-327, Pasadena, CA 91109, USA
9
ESO-European Southern Observatory, D-85748 Garching bei M
¨
unchen, Germany
10
Institute of Astronomy and Astrophysics, Academia Sinica, P. O. Box 23-141, Taipei 10617, Taiwan
11
Institut f
¨
ur Theoretische Astrophysik, ZAH, Albert-Ueberle-Straß e 2, 69120 Heidelberg, Germany
12
Leiden Observatory, Leiden University, P. O. Box 9513,2300 RA Leiden, The Netherlands
13
Department of Physics, Mathematics, and Astronomy, California Institute of Technology, Pasadena, CA91125, USA
14
Department of Physics & Astronomy. University College London, Gower Street, London WCIE 6 BT, UK
15
Departamento de Astrono
´
ıa y Astrof
´
ısica, Pontificia Universidad Cat
´
olica de Chile, V. Mackenna 4860, Santiago 22, Chile
16
Department of Physics and Astronomy, Rutgers University, 136 Frelinghuysen Rd., Piscataway, NJ 08854, USA
17
Observatories of the Carnegie Institution of Washington, Pasadena, CA 91 101, USA
18
Center for Cosmology and Astro-Particle Physics, & Department of Physics; The Ohio State University, 191 W. Woodruff Ave., Columbus, Ohio 43210, USA
19
INAF, Osservatorio Astronomico di Bologna, & INFN, Sezione di Bologna; Via Ranzani 1, I-40127 Bologna, Italy
20
Center for Astrophysics and Space Sciences, University of California at San Diego. 9500 Gilman Dr., MC 0424, La Jolla, CA 92093, USA
21
INAF-Osservatorio Astronomico di Trieste, via G.B. Tiepolo 11, 40131 Trieste, Italy
22
European Laboratory for Particle Physics (CERN). CH-1211, Geneva 23, Switzerland
23
Universit
¨
ats-Sternwarte, M
¨
unchen, Scheinerstr. 1, D-81679 M
¨
unchen. Germany
24
Max-Planck Institute for Astronomy, K
¨
onigstuhl 17, D-69117, Heidelberg, Germany
Received 2011 June 16; accepted 2011 December 5; published 2012 March 14
ABSTRACT
The Cluster Lensing And Supernova survey with Hubble (CLASH) is a 524-orbit Multi-Cycle Treasury Program
to use the gravitational lensing properties of 25 galaxy clusters to accurately constrain their mass distributions.
The survey, described in detail in this paper, will definitively establish the degree of concentration of dark matter
in the cluster cores, a key prediction of structure formation models. The CLASH cluster sample is larger and less
biased than current samples of space-based imaging studies of clusters to similar depth, as we have minimized
lensing-based selection that favors systems with overly dense cores. Specifically, 20 CLASH clusters are solely
X-ray selected. The X-ray-selected clusters are massive (kT > 5 keV) and, in most cases, dynamically relaxed.
Five additional clusters are included for their lensing strength (θ
Ein
> 35
at z
s
= 2) to optimize the likelihood of
finding highly magnified high-z (z>7) galaxies. A total of 16 broadband filters, spanning the near-UV to near-IR,
are employed for each 20-orbit campaign on each cluster. These data are used to measure precise (σ
z
∼ 0.02(1 + z))
photometric redshifts for newly discovered arcs. Observations of each cluster are spread over eight epochs to enable
a search for Type Ia supernovae at z>1 to improve constraints on the time dependence of the dark energy equation
of state and the evolution of supernovae. We present newly re-derived X-ray luminosities, temperatures, and Fe
abundances for the CLASH clusters as well as a representative source list for MACS1149.6 + 2223 (z = 0.544).
Key words: dark energy – dark matter – Galaxy: evolution – Galaxy: formation – gravitational lensing: strong –
gravitational lensing: weak
Online-only material: color figures, machine-readable table
1. INTRODUCTION
The universe has proven to be far more intriguing in its
composition than we knew it to be even just 14 years ago. It
is a “dark” universe where ∼23% of its mass-energy density is
made up of weakly interacting (and, as yet, undetected) non-
baryonic particles (a.k.a. dark matter, DM) and ∼73% is as
yet unknown physics (a.k.a. dark energy) that is driving an
accelerated expansion of the metric (e.g., Komatsu et al. 2011;
Riess et al. 2011). TheHubble Space Telescope (HST) has played
a key role in providing evidence for and constraining the nature
of both of these mysterious dark components (e.g., Riess et al.
1998; Perlmutter et al. 1999; Clowe et al. 2006).
Clusters of galaxies, by virtue of their position at the high end
of the cosmic mass power spectrum, provide a powerful way to
constrain the frequency of high amplitude perturbations in the
primordial density field. As such, they play a direct and funda-
mental role in testing cosmological models and in constraining
1
The Astrophysical Journal Supplement Series, 199:25 (23pp), 2012 April Postman et al.
the properties of DM, providing unique and independent tests
of any viable cosmology and structure formation scenario, and
possible modifications of the laws of gravity. A key ingredient
of such cluster-based cosmological tests is the mass distribu-
tion of clusters, both on (sub) Mpc scales and across the range
of populations. The best and highest resolution maps of DM
distribution in massive galaxy clusters come from observations
of strong gravitational lensing made by the Advanced Camera
for Surveys (ACS; Ford et al. 2003) on board HST. As part
of a Guaranteed Time Observation (GTO) program, deep (20-
orbit) multiband (4–6 filter) observations were obtained for five
galaxy clusters: A1689 (Broadhurst et al. 2005; Limousin et al.
2007; Coe et al. 2010), A1703 (Limousin et al. 2008; Saha &
Read 2009; Zitrin et al. 2010), A2218 (El
´
ıasd
´
ottir et al. 2007),
CL0024+1654 (Jee et al. 2007; Zitrin et al. 2009; Umetsu et al.
2010), and MS1358+6245 (Zitrin et al. 2011c). These results
have contributed to a reported tension between observed and
simulated galaxy cluster DM halos. Observed clusters appear to
have denser cores than simulated clusters of similar total (virial)
mass (e.g., Broadhurst et al. 2008;Ogurietal.2009; Sereno et al.
2010). Understanding the true constraints from observed con-
centration and mass profile measurements on ΛCDM structure
formation models is one of the important problems that can be
tackled with a deep, high angular resolution imaging survey of
a significantly larger and more homogeneously selected sample
of galaxy clusters.
In 2009 May, NASA successfully executed the final planned
Hubble Servicing mission, SM4, with the installation of the
Wide Field Camera 3 (WFC3; Kimble et al. 2008) and the repair
of ACS. Shortly thereafter, the Hubble Multi-Cycle Treasury
(MCT) Program was conceived to permit ambitious programs
(>500 orbits) with broad scientific potential that could not be
accomplished within the constraints of a single HST observing
cycle and that would take full advantage of the final era of a
newly refurbished HST.
The Cluster Lensing and Supernova survey with Hubble
(CLASH) was one of three MCT programs selected. CLASH
has four main science goals.
1. Measure the profiles and substructures of DM in galaxy
clusters with unprecedented precision and resolution.
2. Detect Type Ia supernovae (SNe Ia) out to redshift z ∼ 2.5
to measure the time dependence of the dark energy equation
of state and potential evolutionary effects in the SNe
themselves.
3. Detect and characterize some of the most distant galaxies
yet discovered at z>7.
4. Study the internal structure and evolution of the galaxies in
and behind these clusters.
To accomplish these objectives, the CLASH program targets 25
massive galaxy clusters and will image each in 16 passbands
using WFC3/UVIS, WFC3/IR, and ACS
/WFC. CLASH has
been allocated 524 HST orbits, spread out over cycles 18, 19, and
20. The majority of these orbits (474) are for cluster imaging
and, simultaneously, for the parallel SN search program. An
additional 50 orbits were allocated as a reserve for SN follow-
up observations. Based on a current census of the HST data
archive, CLASH will produce a six-fold increase in the number
of lensing clusters observed to a depth of 20 orbits and, more
importantly, will vastly increase the number of lensing clusters
with extensive multiband HST imaging.
Motivations for each of the main CLASH science goals are
provided in Sections 2.1–2.4. Subsequent sections describe the
cluster sample (Section 3), survey design (Section 4), data
pipeline (Section 5), and supporting observations using other
facilities (Section 6). Data products intended for public distri-
bution to the community are briefly described in Section 7.AB
magnitudes are used throughout (Oke 1974). The cosmologi-
cal parameters H
o
= 100 h km s
−1
Mpc
−1
,h= 0.7, Ω
m
=
0.30, and Λ = 0.70 are assumed in this paper.
2. SCIENTIFIC MOTIVATION
2.1. Galaxy Cluster Dark Matter Profiles and Formation Times
Recent observations suggest that galaxy clusters formed
earlier in our universe than in simulated ΛCDM universes. These
observations include the detection of perhaps unexpectedly
massive galaxy clusters at z>1 (Stanford et al. 2006;
Eisenhardt et al. 2008; Jee et al. 2009, 2011; Huang et al.
2009; Rosati et al. 2009; Papovich et al. 2010; Schwope et al.
2010; Gobat et al. 2011; Foley et al. 2011) and the finding that
some clusters at intermediate redshift (z ∼ 0.3) have denser
cores than clusters of similar mass produced in simulations
(Broadhurst et al. 2008; Broadhurst & Barkana 2008; Oguri
et al. 2009; Richard et al. 2010; Sereno et al. 2010; Zitrin et al.
2011a). While the evidence to date for early cluster growth is
suggestive, possible explanations include departures from the
Gaussian initial density fluctuation spectrum or higher levels
of dark energy in the past, so-called Early Dark Energy (EDE;
Fedeli & Bartelmann 2007; Sadeh & Rephaeli 2008; Francis
et al. 2009; Grossi & Springel 2009). If a significant quantity of
EDE (for example, Ω
DE
∼ 0.1atz = 6) suppressed structure
growth in the early universe, then clusters would have had to
start forming sooner to yield the numbers we observe today.
These scenarios remain allowable within current observational
constraints as described in the above papers, although some non-
Gaussian models can be ruled out by using the cosmic X-ray
background measurements (Lemze et al. 2009). The CLASH
data permit significant advances to be made toward supporting
or rejecting observational evidence for early cluster growth by
measuring core densities for a larger, less biased sample of
clusters.
In cosmological simulations, cold-dark-matter-(CDM)-
dominated halos of all masses consistently evolve to have a
roughly “universal” density profile that steepens with radius.
Functional forms that fit such a profile well include the “NFW”
profile (Navarro et al. 1996, 1997) and the Einasto/S
´
ersic pro-
file (S
´
ersic 1963; Einasto 1965; Navarro et al. 2004, 2010).
Furthermore, each simulated halo’s core density is related to the
background density of the universe at the halo’s formation time.
Halos that form later, including the most massive galaxy clus-
ters, are found to have the least dense cores in a relative sense.
Determining the relationship between the shape and depth of a
halo’s gravitational potential and its total mass as a function of
time thus provides fundamental constraints on structure forma-
tion.
In practice, the relative core densities, or “concentrations,”
are measured (both in simulated and observed halos) as c
vir
=
r
vir
/r
−2
, a ratio between the virial radius and the inner radius
at which the density slope of the fitted profile is isothermal
(ρ ∝ r
−2
). Analyses of gravitational lensing spanning such a
large range of radii allow one to map the (primarily dark) matter
profiles of observed halos and measure their concentrations.
DM profiles are best mapped in cluster cores using strong-
lensing analysis of multiband HST imaging. The lensing-
based mass profile mapping is extended to the virial radii of
2
The Astrophysical Journal Supplement Series, 199:25 (23pp), 2012 April Postman et al.
Figure 1. Mass profiles measured for four well-studied, strongly lensing galaxy
clusters that are not included in the CLASH sample. All have similar mass
profiles as measured from Hubble observations of strong-lensing and Subaru
observations of weak-lensing distortion and magnification (Umetsu et al. 2011a,
their Figure 6). The averaged mass profile is in remarkably good agreement
with the standard NFW form (Umetsu et al. 2011b, their Figure 1) as shown
by the gray area (2σ confidence interval of the NFW fit), though with a higher
concentration than predicted from cosmological simulations. Both strong- and
weak-lensing probes are required to map the continuously steepening mass
profile from the inner core (∼10 kpc h
−1
) out to beyond the virial radius
(∼2Mpch
−1
).
(A color version of this figure is available in the online journal.)
Figure 2. Joint strong- and weak-lensing analyses are required to obtain tight
constraints on cluster concentrations as shown here for CL0024+17, a non-
CLASH cluster (Umetsu et al. 2010, from their Figures 15 and 17). Confidence
levels of 68.3%, 95.4%, and 99.7% are plotted in the c
vir
–M
vir
plane.
(A color version of this figure is available in the online journal.)
clusters using weak-lensing analysis of wider field ground-based
multiband imaging, such as from Subaru. Results from Umetsu
et al. (2011a, 2011b) for four of the currently best-studied (non-
CLASH) clusters are shown in Figure 1.
Joint modeling of strong and weak lensing (SL+WL) yields
significantly better constraints on concentrations than either
probe alone. Quantitatively, Meneghetti et al. (2010) found their
joint SL+WL analyses of simulated clusters yield concentra-
tions to ∼11% accuracy, while WL-only and SL-only analy-
ses yielded ∼33% and ∼59% scatters, respectively. Figure 2
Figure 3. Mass profiles of the best-studied clusters to date are revealed to have
higher central density concentrations than simulated clusters of similar mass
and redshift. Reconciliation may be within reach given results from the latest
simulations and an estimated lensing bias which CLASH will avoid. The plotted
lines are mean concentrations at three different redshifts for clusters in these
simulations as calculated from the fitting formulae provided in those papers.
However, note that halos of this great mass are rare (or even non-existent at
these redshifts) in these simulations, so these results are mainly extrapolations,
as designatedby the dashed lines. The thinner dashed lines aboveillustrate a 50%
observational bias applied to the Prada et al. (2011) results. This bias has been
roughly estimated for non-CLASH clusters such as these which were selected
for study based on exceptional lensing strength (Hennawi et al. 2007; Oguri &
Blandford 2009; Meneghetti et al. 2010, 2011). Results from the currently best-
studied clusters are plotted here as squares (Umetsu et al. 2011a) and circles
(Oguri et al. 2009) labeled with abbreviated names and described further in
Table 1.
(A color version of this figure is available in the online journal.)
demonstrates how SL and WL analyses combine to yield ro-
bust constraints on the mass and concentration of CL0024+17
(Umetsu et al. 2010).
The best-studied (SL+WL) galaxy clusters to date have
been found to have overly high concentrations (dense cores)
compared to halos in N-body simulations with similar masses,
as shown in Figure 3 (Broadhurst et al. 2008;Ogurietal.2009;
Sereno et al. 2010) and detailed in Table 1. Recent simulations
(Prada et al. 2011) yield cluster concentrations that are over 50%
higher than previous simulations (e.g., Duffy et al. 2008). This
is a product of upturns as a function of both mass and redshift
found in these newer simulations, which are not yet understood.
Similarly, clusters have also been found to have somewhat larger
than expected Einstein radii, a direct and particularly accurate
measure of the projected mass in a halo’s core (Broadhurst &
Barkana 2008; Richard et al. 2010; Zitrin et al. 2011a, 2011b).
Unfortunately, the best-studied clusters to date have also
been among the strongest gravitational lenses known. Such
lensing-selected clusters are highly biased toward halos with
high concentrations, both intrinsically and as projected on the
sky due to halo elongation along the line of sight (Hennawi et al.
2007; Oguri & Blandford 2009; Meneghetti et al. 2010, 2011).
These biases are estimated to lead to systematically higher
concentrations by as much as 50% or more. However, such
a bias is insufficient to account for the discrepancy between
the observations and the predictions, that is until a very recent
analysis of new simulations was performed (see Figure 3 and
discussion below).
More robust conclusions require analysis of a larger, unbiased
cluster sample. Progress toward this goal has been made by
3
The Astrophysical Journal Supplement Series, 199:25 (23pp), 2012 April Postman et al.
Tab l e 1
Concentration Measurements for Previously Well-studied Lensing-selected Clusters
Constraints
a
Publication Cluster zM
vir
c
vir
χ
2
/dof
(10
15
M
h
−1
)
SL+WL+mag Umetsu et al. (2011a)
b
A1689 0.187 1.34
+0.20
−0.16
13.82
+1.72
−1.62
4.73/17
SL+WL+mag Umetsu et al. (2011a)
b
A1703 0.281 1.29
+0.22
−0.19
6.89
+1.04
−0.91
7.14/19
SL+WL+mag Umetsu et al. (2011a)
b
A370 0.375 2.26
+0.26
−0.23
4.56 ± 0.33 14.07/24
SL+WL+mag Umetsu et al. (2011a)
b
CL0024+17 0.395 1.37
+0.20
−0.18
7.77
+0.97
−0.87
11.47/20
SL+WL+mag Umetsu et al. (2011a)
b
RXJ1347.5−1145 0.451 1.73
+0.12
−0.11
5.96
+0.37
−0.35
45.06/25
RE+WL Oguri et al. (2009) SDSS J1531+3414 0.335 0.7
+0.29
−0.24
7.9
+3.0
−1.5
8.1/6
RE+WL Oguri et al. (2009) SDSS J1446+3032 0.464 0.8
+0.3
−0.22
8.3
+3.9
−3.1
6.4/6
RE+WL Oguri et al. (2009) SDSS J2111−0115 0.637 0.9
+0.41
−0.32
14.1
+25.9
−9.3
7.5/6
Notes. Of these eight clusters, only one—RXJ1347.5−1145—is in the CLASH sample.
a
SL = strong lensing; WL = weak lensing; mag = magnification bias (number counts); RE = Einstein radius.
b
The parameters here are from standard NFW fits to profiles in Umetsu et al. (2011a), and not from the generalized NFW (gNFW) fits
giveninthatpaper.
LoCuSS, the Local Cluster Substructure Survey (Smith et al.
2005). A large sample of 165 clusters between 0.15 <z<0.30
was selected based on X-ray brightness. Strong-lensing analyses
of 20 of these based on HST imaging (mostly single-band
“snapshots”) were presented by Richard et al. (2010). Okabe
et al. (2010a) published weak-lensing analyses of 30 LoCuSS
clusters, 22 of these being more “secure” based on multiband
Subaru imaging, and 9 of these 22 overlapping with the Richard
et al. (2010) subset. Subaru images are especially desirable for
WL studies as they enable excellent galaxy shape measurements
to be performed over a wide area. Multiband imaging is
also critical to properly select background galaxies and avoid
significantly diluting the weak-lensing signal (and thus the virial
mass and concentration) with unlensed foreground galaxies
(Medezinski et al. 2007, 2010). Stacked WL-only analyses have
been performed on large cluster samples (Johnston et al. 2007;
Mandelbaum et al. 2008), but we re-emphasize the need for
combining strong + weak lensing analyses in addition to cross-
comparisons with mass profile estimates from other techniques.
For example, mass concentrations can be measured from X-
ray profiles (Buote et al. 2007; Ettori et al. 2010), although
these are subject to uncertainties due to assumptions about
hydrostatic equilibrium. Importantly, cluster elongation along
the line of sight (a potential bias in concentration measurements)
can be measured by the combination of lensing and X-ray
analysis (Morandi et al. 2011; Newman et al. 2011). The caustic
technique (Diaferio & Geller 1997; Rines & Diaferio 2006)is,
like lensing-based methods, independent of the dynamical state
of the cluster and also provides an important cross check on mass
estimates from lensing and gas kinematics. Further discussion
of some of the previous results from various methods is given in
Comerford & Natarajan (2007), Coe (2010), Rines et al. (2010),
and King & Mead (2011).
Reconciliation of high observed concentrations with results
from simulations may ultimately come from significantly reduc-
ing the observational sample bias along with finding higher con-
centrations in simulated clusters. Baryons, for example, are cur-
rently absent from those cosmological simulations large enough
to produce massive clusters. Baryons can result in significant
“adiabatic contraction” on galaxy scales, however they consti-
tute a much smaller fraction of the mass on cluster scales. Simu-
lations including baryons show that cluster halo concentrations
are likely only varied by ∼10% or so relative to DM-only ha-
los, and that the direction of variation (increase or decrease) is
not even clear, depending on the gas physics assumed (Duffy
et al. 2010; Mead et al. 2010; King & Mead 2011). Nonetheless,
measuring the velocity dispersion of the brightest cluster galaxy
(BCG) as an additional constraint on the inner (80 kpc) mass
profile, where its stellar mass is a non-negligible component of
the matter distribution, provides for a more thorough mapping
of the total mass profile. Some clusters have been shown to have
inner mass profiles that are shallower than NFW (Sand et al.
2004, 2008; Newman et al. 2009, 2011
). Deviations of cluster
mass profiles from NFW or Einasto forms at small and/or large
radii may slightly bias concentration measurements (Oguri &
Hamana 2011). The use of multiple probes of the matter distri-
bution, as CLASH is designed to do, will enable such biases to
be measured and the significance of deviations determined.
More recently, an analysis by Prada et al. (2011) found that
clusters in the Bolshoi and MultiDark simulations have con-
centrations ∼50% higher than clusters in previous simulations.
While the robustness of this new result is still being assessed, it
raises the possibility that the combination of new observations of
an unbiased sample of clusters and new simulations may be able
to bridge the concentration gap. We stress here that estimates of
the observational bias from previous cluster studies have large
uncertainties and likely vary for each cluster. CLASH will deter-
mine mass profiles and concentrations for a new cluster sample
free of lensing selection bias. As we demonstrate in Section 3.2,
CLASH is designed to detect (or rule out) with 99% statistical
confidence average deviations of 15% or more from predicted
concentrations. However, given the outstanding uncertainties in
the expected concentrations, we prefer to recast the problem as
follows: CLASH will deliver robust observational concentration
measurements for a sample of clusters that simulations will be
tasked to reproduce. Ultimately, this will lead to a better calibra-
tion of many mass estimation techniques and, consequently, to
a better understanding of structure formation on cluster scales
and perhaps of our cosmological model as well.
2.2. Improved Constraints on the Dark Energy Equation of
State and SNe Evolution
The biggest cosmological surprise in decades came from
observations of high-redshift SNe Ia, providing the first evidence
that the expansion of the universe now appears to be accelerating
(Riess et al. 1998; Perlmutter et al. 1999), and indicating that the
universe is dominated by “dark energy.” The presence of dark
4
The Astrophysical Journal Supplement Series, 199:25 (23pp), 2012 April Postman et al.
Figure 4. Dark energy and evolution sensitivity. Three models consistent with
current data are shown: w
0
= 0.9, w
a
= 0 (black); w
0
= 1, w
a
= 0.8 (red);
and w
0
= 1,w
a
= 0 (dashed). Also plotted blue is the SNe Ia evolution model
of Dom
´
ınguez et al. (2001), where the peak luminosity changes 3% per solar
mass change in the donor star. The error bars show the constraints at present, as
projected after CLASH, and if HST continues to collect SNe Ia at the present
rate for seven years.
(A color version of this figure is available in the online journal.)
energy has galvanized cosmologists as they seek to understand
it. Observations of high-redshift SNe Ia have continued to lead
the way in measuring the properties of dark energy (e.g., Riess
et al. 2011). The goal for cosmologists now is to measure
the equation of state of dark energy, w = P/(ρc
2
), and its
time variation in the hope of discriminating between viable
explanations. A departure of the present equation of state, w
0
,
from −1 or a detection of its variation, ∂w/∂z, would invalidate
an innate vacuum energy (i.e., the cosmological constant) as the
source of dark energy and would point toward a present epoch
of “weak inflation.” A difference between the expansion history
and the growth history of structure expected for w(z) would
point toward a breakdown in general relativity as the cosmic
scale factor approaches unity.
HST paired with ACS is a unique tool in this investigation,
providing the only means to collect SNe Ia at 1 <z<1.5,
which, in turn, provide the only constraints we have to date
on the time variation of w. From the 23 SNe Ia at z>1
with HST data (Riess et al. 2004, 2007) we have learned: (1)
that cosmic expansion was once decelerating before it recently
began accelerating, (2) that dark energy, i.e., an energy density
with w<0, was already present during this prior decelerating
phase, (3) that SNe Ia at a look-back time of 10 Gyr appear
both spectroscopically and photometrically similar to those seen
locally, and (4) no rapid change is seen in w(z) and thus no
departure is yet seen from the cosmological constant, though the
constraint on the time variation remains an order of magnitude
worse than on the w
0
.
SNe Ia play a central role, not only as distance indicators for
cosmography, but also as major contributors to cosmic metal
production and distribution. The measurement of high-redshift
SN Ia rates is therefore integral to understanding the history
of chemical enrichment. The high-redshift rate cannot easily
be predicted from the star formation rate because the nature
and, hence, the timescales of the process behind the growth
of the white dwarf toward the Chandrasekhar mass are not
known. The two leading, competing scenarios are accretion
from a close binary companion—the single-degenerate scenario
(Whelan & Iben 1973; Nomoto 1982), or merger with another
white dwarf, following loss of orbital energy and angular
momentum by emission of gravitational waves—the double-
Tab l e 2
Estimated Number of z 1 SNe Ia to be Discovered During
the CLASH Program
Redshift Estimated Number
Range of SNe Ia Found
1.0 z<1.5 7–11
1.5 z<2.0 4–13
2.0 z 2.70–4
1.0 z 2.7 11–28
Notes. These estimates are the 68% confidence intervals, accounting
for both statistical and systematic uncertainties. The assumed SN Ia
rate is from Graur et al. (2011).
degenerate scenario (Iben & Tutukov 1984; Webbink 1984).
One way to constrain the different progenitor scenarios is to
measure the delay-time distribution (DTD) of SNe Ia. This is
the distribution of times that elapse between a brief burst of star
formation and the subsequent SN Ia explosions. Observations
have suggested variousdifferent forms for the DTD (e.g., Dahlen
et al. 2004, 2008; Mannucci et al. 2006; Pritchet et al. 2008).
However, a number of more recent measurements and analyses
point to a DTD that is a power law of index ≈−1 (Totani
et al. 2008; Maoz et al. 2010, 2011; Maoz & Badenes 2010;
Brandt et al. 2010; Horiuchi & Beacom 2010; Graur et al. 2011).
Specifically, Graur et al. (2011) have shown that such a DTD fits
well the measured SN Ia rate out to z ≈ 2. Small sample sizes
are the major limiting factor at high redshifts. Large high-z SN
samples are therefore needed to resolve the issue, and control
possible biases in cosmological studies (due to the evolving
SN Ia channel mix).
The CLASH survey uses ACS in parallel with the cluster
program to continue the discovery of SNe Ia at 1 <z<1.5, the
objects which tell us about the variation in w. With WFC3
in parallel, CLASH will yield SNe Ia at 1.5 <z<2.5.
Observations in this fully matter-dominated epoch provide the
unique chance to test SN Ia distance measurements for the
deleterious effects of evolution independent of our ignorance
of dark energy (Riess & Livio 2006). Because the SNe Ia
are detected when these cameras are in parallel, they are far
from the cluster core (∼2 Mpc at the median cluster redshift
of z = 0.4) and, hence, the effects of lensing are small (and
correctable), making the SNe usable for improving the limits
on the redshift variation of the dark energy equation of state.
At z<1, SN Ia distance measurements are most sensitive to
the static component of dark energy, w
0
.At1<z<1.5, the
measurements are most sensitive to the dynamic component, w
a
.
By z>1.5, the measurements are most sensitive to evolution
if present (e.g., the changing C/O ratio of the donor star),
providing the means to diagnose and calibrate the degree of
SN Ia evolution in dark energy measurements. Figure 4 shows
how variations in the DE equation of state or an evolution in the
white dwarf C/O ratio can change the SN Ia distance modulus
as a function of redshift.
Accounting for the systematic uncertainty introduced by the
cosmic star formation history, Graur et al. (2011) predicted the
SN Ia rate out to higher redshifts (their Figure 13), which we use
here, in conjunction with the CLASH observational parameters,
to estimate the number of z 1 SNe Ia that will be discovered
over the course of this program. These estimates are presented
in Table 2 and are the 68% confidence intervals, accounting for
both statistical and systematic uncertainties.
5