Lecture 2 Josh Frieman I Jayme Tiomno School of Cosmology Rio de Janeiro Brazil July 2010 Hoje V Recent SN Surveys and Current Constraints on Dark Energy VI Fitting SN Ia Light Curves amp Cosmology ID: 571939
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Cosmology with Supernovae:
Lecture 2
Josh FriemanI Jayme Tiomno School of Cosmology, Rio de Janeiro, BrazilJuly 2010Slide2
Hoje
V. Recent SN Surveys and Current Constraints on Dark EnergyVI. Fitting SN Ia
Light Curves & Cosmology VII. Systematic Errors in SN Ia Distances2Slide3
Coming Attractions
VIII. Host-galaxy correlationsIX. SN Ia Theoretical Modeling
X. SN IIp DistancesXI. Models for Cosmic AccelerationXII. Testing models with Future Surveys: Photometric classification, SN Photo-z’s, & cosmology
3Slide4
Luminosity
Time
m
15
15 days
Empirical Correlation: Brighter
SNe
Ia
decline more
slowly
and are bluer
Phillips 1993Slide5
SN
Ia
Peak Luminosity
Empirically correlated
with Light-Curve
Decline
Rate and Color
Brighter
Slower,
Bluer
Use to reduce
Peak Luminosity
Dispersion:
Phillips
1993
Peak Luminosity
Rate of decline
Garnavich, etalSlide6
6
Type
Ia
SNPeak Brightnessas calibratedStandard Candle
Peak brightness
correlates with
decline rate
Variety of algorithms for modeling these
correlations:
corrected dist. modulus
After correction,
~
0.16
mag
(
~8%
distance error)
Luminosity
TimeSlide7
7
Published Light Curves for Nearby Supernovae
Low-
z
SNe
:
Anchor Hubble diagram
Train Light-curve fitters
Need well-sampled, well-calibrated, multi-band light curvesSlide8
Low-z Data
8Slide9
9
Correction for Brightness-Decline relation reduces scatter in nearby SN
Ia
Hubble Diagram
Distance modulus for
z
<<1
:
Corrected distance modulus is
not
a direct observable: estimated from a model for light-curve shape
Riess
etal
1996Slide10
10
Acceleration Discovery Data:
High-z SN Team10 of 16 shown; transformed to SN rest-frame
Riess etalSchmidt etal
V
B+1Slide11
Riess, etal High-z
Data (1998)
11Slide12
High-
z Supernova Team data (1998)
12Slide13
Likelihood Analysis
This assume
13
Goliath
etal
2001Slide14
14
High-
z SN Team
Supernova Cosmology ProjectSlide15
1998-2010 SN Ia Synopsis
Substantial increases in both quantity and quality of SN Ia
data: from several tens of relatively poorly sampled light curves to many hundreds of well-sampled, multi-band light curves from rolling surveys Extension to previously unexplored redshift ranges: z>1 and 0.1<z<0.3Extension to previously underexplored rest-frame wavelengths (Near-infrared)
Vast increase in spectroscopic dataIdentification of SN Ia subpopulations (host galaxies)Entered the systematic error-dominated regime, but with pathways to reduce systematic errors
15Slide16
16
Supernova Legacy Survey (2003-2008)
Megaprime
Mosaic CCD camera
Observed 2 1-sq deg regions every 4 nights
~400+
spectroscopically
confirmed
SNe
Ia
to measure
w
Used 3.6-meter CFHT
/“
Megacam
”
36
CCDs
with good
blue response
4 filters
griz
for good
K-
corrections and color measurement
Spectroscopic follow-up on 8-
10m telescopesSlide17
8 nights/yr:
LBL/Caltech
DEIMOS/LRIS for types, intensive study, cosmology with
SNe
II-P
Keck
120 hr/yr: France/UK
FORS 1&2 for types,
redshifts
VLT
120 hr/yr: Canada/US/UK
GMOS for types,
redshifts
Gemini
3 nights/yr: Toronto
IMACS for host
redshifts
Magellan
Spectra
SN Identification
Redshifts
Slide18
Power of a Rolling Search
SNLS Light
curvesSlide19
19
First-Year SNLS Hubble Diagram
SNLS 1
st Year Results
Astier
et al. 2006
Using 72
SNe
from SNLS
+40 Low-
zSlide20
20
Wood-
Vasey
, etal (2007), Miknaitis, etal
(2007):
results from ~60 ESSENCE
SNe
(+Low-
z
)Slide21
21
60 ESSENCE SNe
72 SNLS SNeSlide22
22Slide23
Higher-z SNe
Ia from ACS
Z=1.39
Z=1.23
Z=0.46
Z=0.52
Z=1.03
50
SNe
Ia
, 25 at
z
>1
Riess
,
etalSlide24
24
(m
-M)
HST GOODS Survey (
z
> 1) plus
compiled ground-based
SNe
Riess
etal
2004Slide25
Supernova Cosmology Project
SN
Ia Union Compilation
Kowalski et al.,
ApJ
, 2008
Data tables and updates at http://
supernova.lbl.gov
/UnionSlide26
26
Likelihood
Analysis with
BAO and CMB
PriorsSlide27
27
Recent Dark Energy Constraints
Improved SN constraints
Inclusion of constraints from WMAP Cosmic Microwave Background Anisotropy (Joana) and SDSS Large-scale Structure (Baryon Acoustic Oscillations; Bruce, Daniel)
Only statistical errors shown
assuming
w
= −1Slide28
28Slide29
29
Only statistical errors shown
assuming flat Univ. and constant
wSlide30
SNLS Preliminary 3rd year Hubble Diagram
Conley et al, Guy
etal
(2010): results with ~252 SNLS SNe
Independent analyses with 2 light-curve fitters: SALT2,
SiFTOSlide31
Results
published
from 2005 season
Frieman, et al (2008); Sako
, et al (2008)
Kessler, et al 09; Lampeitl et al 09; Sollerman et al 09Slide32
32
SDSS II Supernova Survey Goals
Obtain few hundred high-quality* SNe Ia light curves in the `redshift desert’ z~0.05-0.4 for continuous Hubble diagramProbe Dark Energy in z regime complementary to other surveys
Well-observed sample to anchor Hubble diagram, train light-curve fitters, and explore systematics of SN Ia distances Rolling search: determine SN/SF rates/properties vs. z, environment
Rest-frame
u
-band templates for z >1 surveys
Large survey volume: rare & peculiar SNe, probe outliers of population
*high-cadence, multi-band, well-calibratedSlide33
Spectroscopic follow-up telescopes
R.
Miquel
, M.
Molla
, L.
GalbanySlide34
Search Template Difference
g
r
i
Searching For Supernovae
2005
190,020
objects scanned
11,385
unique candidates
130
confirmed
Ia
2006
14,441
scanned
3,694
candidates
193
confirmed
Ia
2007
175
confirmed
Ia
Positional match to remove movers
Insert fake SNe to monitor efficiencySlide35
35
SDSS SN Photometry
Holtzman etal (2008)Slide36
B.
Dilday
500+
spec confirmed
SNe
Ia
+ 87 conf. core
collapse plus >1000 photometric
Ia’s
with host
z’sSlide37
Spectroscopic Target Selection
2 Epochs
SN Ia Fit
SN Ibc Fit
SN II Fit
Sako etal 2008
Slide38
Spectroscopic Target Selection
2 Epochs
SN Ia Fit
SN Ibc Fit
SN II Fit
31 Epochs
SN Ia Fit
SN Ibc Fit
SN II Fit
Fit with
template
library
Classification
>90%
accurate after
2-3 epochs
Redshifts
5-10%
accurate
Sako etal 2008
Slide39
SN and Host Spectroscopy
MDM 2.4m
NOT 2.6m
APO 3.5m
NTT 3.6m
KPNO 4m
WHT 4.2m
Subaru 8.2m
HET 9.2m
Keck 10m
Magellan 6.5m
TNG
3.5m
SALT
10m SDSS 2.5m
2005+2006
Determine SN Type and
RedshiftSlide40
Spectroscopic Deconstruction
SN model
Host galaxy modelCombined model
Zheng
, et al (2008) Slide41
Fitting SN Ia Light Curves
Multi-color Light Curve Shape (MLCS2k2)
Riess, etal 96, 98; Jha,
etal 2007SALT-II Guy, etal
05,08
41Slide42
fit parameters
Time of maximum
distance modulus
host gal extinction
stretch/decline rate
time-dependent model “vectors”
trained on Low-
z
SNe
∆ <0: bright, broad
∆ >0: faint,
narrow,
redder
MLCS2k2
Light
-curve
Templates
in rest-frame
j
=UBVRI
;
built from ~100
well-observed, nearby
SNe
Ia
observed
passbandSlide43
43
Host Galaxy Dust Extinction
Extinction:
Empirical Model for wavelength dependence:
MLCS:
A
V
is a fit parameter, but
R
V
is usually fixed to a global value (sharp prior) since it’s usually not well determined SN by SN
Cardelli
etal
89 (CCM)Slide44
44
Host Galaxy Dust Extinction
Jha
Milky
Way avg.
Historically, MLCS used Milky Way average of
R
V
=3.1
Growing evidence that this doesn’t represent SN host galaxy population wellSlide45
Extract
RV by matching colors of SDSS SNe to MLCS simulations
Use nearly complete (spectroscopic + photometric) sampleMLCS previously used Milky Way
avg RV=3.1Lower R
V
more consistent with SALT color law and other recent SN
R
V
estimates
D. CinabroSlide46
Carnegie Supernova Project: Low-
z
CSP is a follow-up projectGoal: optical/NIR light-curves and spectro-photometry for> 100 nearby SNIa> 100 SNII
> 20 SNIbcFilter set: BV + u’g’r’i’ + YJHKUnderstand SN physicsUse as standard candles.Calibrate distant SN
Ia
sampleSlide47
CSP Low-z Light Curves
Folatelli
, et al. 2009
Contreras, et al. 2009: 35 optical light curves (25 with NIR) Slide48
Varying Reddening Law?
Folatelli
et al. (2009)
2005A2006XSlide49
Local Dust?
Goobar
(2008): higher density of dust grains in a shell surrounding the SN: multiple scattering steepens effective dust law
(also Wang)
Folatelli
et al. (2009)
Two Highly Reddened
SNeSlide50
Priors & Efficiencies
Determine priors and efficiencies from data and Monte Carlo simulationsSlide51
Priors & Efficiencies
Determine priors and efficiencies from data and Monte Carlo simulations
Inferred P(
A
V
)
Inferred P(
)Slide52
Model Spectroscopic & Photometric Efficiency
Redshift distribution for all SNe passing photometric selection cuts (spectroscopically complete sample)
Data
Need to model biases due to what’s missingDifficult to model spectroscopic selectionSlide53
Model Selection FunctionSlide54
Include Selection FunctionSlide55
Monte Carlo Simulations match data distributions
Use recorded observing conditions (local sky, zero-points, PSF, etc)Slide56
56Slide57
57
Show likelihood plots for MLCS
MLCS fit to one of the
ESSENCE SNeSlide58
58
prior
Marginalized
PDFs
μ
distribution approximated by Gaussian for cosmology fitSlide59
59
MLCS Likelihood Contours for this object