Type Ia Supernovae as
Probes of Dark Energy
Mark Sullivan
University of Oxford
Paris
Toronto
Ray Carlberg,
Kathy Perrett
Victoria
Chris Pritchet
USA
Andy Howell, Alex
Conley, Saul Perlmutter,
+…
Reynald Pain, Pierre Astier,
Julien Guy, Nicolas Regnault,
Christophe Balland, Delphine
Hardin,+ …
Marseille
Stephane Basa,
Dominique Fouchez
Oxford
Mark Sullivan, Isobel
Hook, + …
The SNLS
collaboration
Full list of collaborators at: http://cfht.hawaii.edu/SNLS/
Nearly a century after Einstein,
the “cosmological constant” is
back in vogue
Baryonic
Matter
5%
Dark Matter
22%
Dark
Energy
73%
Deluge of astrophysical data show the expansion of the Universe
is accelerating. What does this mean?
Gravity should act to slow the expansion
1) GR is “wrong” - modified gravity on large scales?
1) “Λ”, >70% of the Universe in an unknown form – “dark energy”
 Characterised by an equation of state, w(z) or w(a)
The standard candle
Standard candle:
GR:
L
f 
2
4dL
Measurement of flux gives distance
dL  z;w,M ,DE 

Measure apparent flux and redshift
 can infer distance and cosmology
The modern day Hubble Diagram
Fainter  Further 
Faster expansion 
More cosmological constant
Fainter
(Further)
“Distanceredshift”
relation
Different
cosmological
parameters
make different
predictions in the
distance-redshift
relation
Brighter  Nearer 
Slower expansion 
Higher mass density 
Less cosmological constant
“Nearby” standard
candles
For a given
redshift…
Universe was
smaller
White Dwarf
SNe Ia: thermonuclear
explosions of C-O white
dwarf stars
“Standard” nuclear physics
Uniform triggering mass
Bright: 10 billion suns, peak in optical
56Ni
 56Co  56Fe powers the SN Ia light-curve
Duration: a few weeks
Standardizable: 6% calibration
Brightness and homogeneity make them the best
known measure of distance, and hence dark energy
SNe are not good standard candles!
Uncorrected dispersion is ~0.5mag – or 25% in distance
Empirical linear relations exist which reduce this scatter


Brighter SNe have wider light curves
Brighter SNe have a bluer optical colour
Cosmology with SNe Ia
SNe Ia are standardised, not standard, candles:
B  mB  MB  (s 1)  c
“Measured”
maximum light
magnitude
“Standard” absolute
magnitude
“c” – optical colour
estimator corrects for
extinction and/or intrinsic
variation via β
s – “stretch” corrects
for light-curve shape
via α
 and  corrections reduce scatter from 25% to 6% in distance

mod
B
 DL z,w,M ,DE 
 
2
N

obs
B

mod 2
B

2
2
 stat
  int
A Typical SN
Peak
brightness
Colour (c)
What we need to measure
Lightcurve
width (stretch)
B  mB  MB  (s 1)  c
SNLS: Vital Statistics
2003-2008 SN survey with “MegaCam” on
CFHT
>400 high-z confirmed SNe Ia to
measure “w”
griz every 4 nights in queue mode, densely
sampled SN light curves
2000 SN detections in total
SNLS3 Hubble
Diagram
(preliminary)
~250 distant SNLS SNe Ia
128 local SNe Ia
86 SDSS-SN Ia
17 from HST
476 SNe total
SNLS+flatness+w=-1:
ΩM 0.271±0.017
Sullivan et al. 2009
SNLS3 Cosmological Constraints (Preliminary)
w  0.98
4.5% statistical errors
WMAP-5
SNe

BAO
SNLS3 + BAO + WMAP5 “shifts” + Flat
Sullivan et al. 2009
SNe Ia: Systematics and Issues
“Experimental Systematics”

Photometric calibration; contamination; Malmquist biases
Non-SN systematics

Peculiar velocities; Weak lensing
SN model and K-corrections

SED uncertainties; colour relations; light curve fitters
Extinction/Colour

Effective RV; Mix of intrinsic colour and dust
Redshift evolution in the mix of SNe

“Population drift” – environment?
Evolution in SN properties

Light-curves/Colours/Luminosities
Tractable, can be
modelled
Identified systematics in SNLS3 (preliminary)
Systematic
% <w>
Extra
error
Statistical only
4.3
…
SNLS zero points
4.5
1.3
SNLS filters
4.4
0.6
External zero points
4.7
1.9
External filters
4.5
0.8
SN colour relation
5.0
2.5
BD+17 colours
5.1
2.6
BD+17 SED
4.4
0.4
Peculiar velocities
4.4
0.5
Malmquist bias
4.4
0.7
Nicmos non-linearity
4.4
0.7
Non-Ia contamination
4.4
0.7
All systematics
6.8
5.0
Conley et al. 2009
SNLS3 Cosmological Constraints (Preliminary)
w  1.04
4.5% statistical errors
WMAP-5
SNe

BAO
SNLS3 + BAO + WMAP5 “shifts” + Flat
~5% systematic errors
~7% stat + sys errors
No evidence for
departures from w=-1
Sullivan et al. 2009
SNLS3 Cosmological Constraints (Preliminary)
w  1.04
4.5% statistical errors
WMAP-5
SNe

BAO
SNLS3 + BAO + WMAP5 “shifts” + Flat
~5% systematic errors
~7% stat + sys errors
No evidence for
departures from w=-1
Sullivan et al. 2009
Identified systematics in SNLS3 (preliminary)
Systematic
% <w>
Extra
error
Statistical only
4.3
…
SNLS zero points
4.5
1.3
SNLS filters
4.4
0.6
External zero points
4.7
1.9
External filters
4.5
0.8
SN colour relation
5.0
2.5
BD+17 colours
5.1
2.6
BD+17 SED
4.4
0.4
Peculiar velocities
4.4
0.5
Malmquist bias
4.4
0.7
Nicmos non-linearity
4.4
0.7
Non-Ia contamination
4.4
0.7
All systematics
6.8
5.0
Most uncertainties arise from combining different SN samples
Conley et al. 2009
Calibration
The single greatest challenge in SNLS3
(and probably every current SN Ia survey…)
All SNe must be placed on the same photometric system
Different SN samples are calibrated to different systems:


Historical low-redshift samples: Observed in U,B,V,R (Landolt)
High-z: Observed in g,r,i,z - calibrate to SDSS or Landolt?
Challenges:


Zeropoints (colour terms)
Filter (system) throughput
Goal: Replace low-z sample & remove dependence on Landolt
system
Identified systematics in SNLS3 (preliminary)
Systematic
% <w>
Extra
error
Statistical only
4.3
…
SNLS zero points
4.5
1.3
SNLS filters
4.4
0.6
External zero points
4.7
1.9
External filters
4.5
0.8
SN colour relation
5.0
2.5
BD+17 colours
5.1
2.6
BD+17 SED
4.4
0.4
Peculiar velocities
4.4
0.5
Malmquist bias
4.4
0.7
Nicmos non-linearity
4.4
0.7
Non-Ia contamination
4.4
0.7
All systematics
6.8
5.0
When low-redshift sample is replaced, systematics should drop below 4%
Need for a “rolling” low-z survey (e.g. PTF, Skymapper)
SNe Ia: Systematics and Issues
“Experimental Systematics”

Photometric calibration; contamination; Malmquist biases
Non-SN systematics

Peculiar velocities; Weak lensing
Tractable, can be
modelled
SN model and K-corrections

SED uncertainties; colour relations; light curve fitters
Extinction/Colour

Effective RV; Mix of intrinsic colour and dust
Redshift evolution in the mix of SNe

“Population drift” – environment?
Evolution in SN properties

“Extinction”
Light-curves/Colours/Luminosities
Increasing knowledge
of SN physics
“Population
Evolution”
Astrophysics – I: Colour correction
Dust would give a linear
relation in log/log space
Before
correction
But, slope, β, << 4.1 (MW
dust)
β≈2.9
After correction
Mixture of external
extinction and intrinsic
relation?
Properties of the dust near
SNe?
Dust in MW is different to
other galaxies?
B  mB  M B   s 1  c
SN Colour
Hubble Bubble
Latest MLCS2k2 paper (Jha 2007)

MLCS2k2 attempts to separate intrinsic
SALT
colour-luminosity and reddening
MLCS2k2
3σ decrease in Hubble constant at
≈7400 km/sec – local value of H0 high;
distant SNe too faint
NoLocal
Bubble
void in mass density?
with other
Could have significant effects on w
light-curve
measurement
fitters!
Conley et al. (2007)
“Bubble” significance versus “β”
Observed: β ~ 2-3
Standard
Dust: β ~ 4.1
Conley et al. (2007)
Astrohysics – II: SN properties and environment
Young
Old
SN light-curve shape strongly depends on host galaxy properties
Strong correlation with inferred age (or morphological type)
residual, no s correction
Demographic shifts and cosmology
SN Stretch

obs
B
 mB  M B   (s 1)  c
Plot cosmological residual without
(s-1) correction
Metallicity
See Timmes, Brown & Truran (2003) for full
story, including role of 56Fe
CNO catalysts pile up into 14N
when H-burning is completed.
During He-burning, 14N is
converted into 22Ne, neutronrich
Higher metallicity means
neutron-rich SN Ia
More neutrons during SN,
means stable 58Ni and less 56Ni
Fainter SN
CNO cycle
“Metallicity”
No trend
between HD
residual and
inferred
metallicity
Howell et al. (2008)
The next surveys
Low-z: New surveys needed to replace existing samples



Palomar Transient Factory (PTF)
5 years, the first local rolling search (“SNLS @ low-z”)
First compete census of SNe Ia in the local universe
The next surveys
Higher-z:



Dark Energy Survey (DES)
Starts 201X, “super-charged”-SNLS
Intriguing synergy with VISTA/VIDEO near-IR survey
Ultimately, JDEM or similar mission
Near-IR: Models predict smaller dispersion
Kasen et al. 2006
Current
DES/VI
DEO
JDEM/E
uclid?
Excludes effect of dust!
Summary
“SNLS3” constraints on <w>: <w>-1 to <4.5% (stat)
(inc. flat Universe, BAO+WMAP-5)
Cosmological constant is completely consistent with data
Systematics ~5%; total error ~6%; dominated by z<0.1 sample
“SNLS5” statistical uncertainty will be <4%:

400 SNLS + 200? SDSS + larger z<0.1 samples, BAO, WL
Current issues:
Photometric calibration limiting factor; will improve dramatically
Mean SN Ia properties evolve with redshift – no bias in cosmology detected
No evidence for metallicity effects
Colour corrections poorly understood
Need for z<0.1 samples with wide wavelength coverage


Replace existing sample & disentangle SN Ia colours and progenitors
PTF underway since March 2009