1 Cosmology Results from the SDSS Supernova Survey David Cinabro SMU 15 September 2008.

1

Cosmology Results from the SDSS Supernova Survey

David Cinabro

SMU

15 September 2008

2

Contents

Cosmology and Dark Energy Intro

SDSS Supernova Survey (2005-07)

Hubble Diagram Analysis & Results (1st-year SDSS data + external)

3

Primary Motivation for Supernova Surveys:

measure expansionhistory of the Universe:in particular, the role of

dark energy

4

Expansion BasicsH(z)2 = H0

2 i i (1+z)3(1+w) where w (equation of state parameter) is pressure/density

0.7 =

constant

-1Cosmological constant (Best current guess)

~ 10-5(1+z)41/3Radiation (CMB)

0.3M(1+z)3v2/c2 ~ 0Matter (dark, baryon, relic )

at

z=0

Evolution

with zw

Source of expansion

5

Methods to Measure H(z)H(z)2 = i i (1+z)3(1+w)

Systematics of galaxy-shear measurements

Weak lensing

galaxy vs. dark matter clusteringgalaxy clustering; power spectrum or clumpiness (Baryon Acoustic Oscillatons)

Need to know cluster-mass selection function.

count galaxy clusters vs redshift.

Large dispersion in brightness.

Evolution? Dust? SN Model?

SN brightness vs. redshift

DifficultiesMethod

6

7

Hubble Diagram Basics

Expansion historydepends on and M

8



What we measurewith SNe

… relative toempty universe

mag = –2.5log(L /4dL2).

dL = (1+z)∫dz/H(z) for flat universe.Distance modulus: 5log(dL/10pc)

9



What we measurewith SNe

… relative toempty universe

10

w-sensitivity with Supernova

11

w-Quest with Supernova

SDSS

SNLS, ESSENCE w = –0.9 gives 4% variation from w = –1

redshift

12

Surveys

compilation from Riess et. al., AJ 607(2004):

Calan Tololo, HZT, SCP, CfA, Higher-Z, ACS.

0 0.5 1.0 1.5 2.0 redshift

m ag

M=1 =0

M=1 =0

1990s

Development & discovery phase (Hi-z, SCP).Lightcurve quality limited by telescope time.

13

Surveys 2000s

Much more telescope time rolling searches & more

passbands.

(SNLS, ESSENCE, SDSS)compilation from Riess et. al., AJ 607(2004):


0 0.5 1.0 1.5 2.0 redshift

m ag

SNLS 1st year sample (Astier 2005)

plus ~ 40 low-z SNe from literature

M=1 =0

M=1 =0

1990s


14

Surveys



0 0.5 1.0 1.5 2.0 redshift

m ag



M=1 =0

M=1 =0

1990s


2000s


passbands.

(SNLS, ESSENCE, SDSS)

15

Surveys



0 0.5 1.0 1.5 2.0 redshift

m ag



M=1 =0

M=1 =0

SDSS surveyfills gap & addslow-z SNe

1990s


2000s


passbands.

(SNLS, ESSENCE, SDSS)

16

SN papers becoming “Methodology” papers

as surveys contribute smaller fraction of total SNe Ia

• Astier06: SNLS contributes ~ 70 of 110

• Kowalski 2008:

contributes 8 of 307 SNe Ia

• SDSS 2008: contributes 100 of 240

17

AJ 135, 338 (2008)

Meet the SDSS-II Supernova Team

18

SDSS-II Supernova Survey: Sep 1 - Nov 30, 2005-2007 (1 of 3 SDSS projects for 2005-2008)

GOAL: Few hundred high-quality type Ia SNe lightcurves in redshift range 0.05-0.35

SAMPLING: ~300 sq deg in ugriz (3 million galaxies every two nights)

SPECTROSCOPIC FOLLOW-UP: HET, ARC 3.5m, MDM, Subaru, WHT, Keck, NTT, KPNO, NOT, SALT, Magellan, TNG

19

SDSS Data FlowOne full night collects 800 fields (ugriz per field) 200 GB

one raw g-field (0.150)Each ‘search’ field is compared to a 2-year old ‘template’ field … things that go “boom” are extracted for human scanning.

Ten dual-CPU servers at APO process g,r,i data (2400 fields) in ~ 20 hrs.

(can you find a confirmed SN Ia ?)

20

SDSS Data FlowOne full night collects 800 fields (ugriz per field) 200 GB

one raw g-field (0.150)

21

SDSS Manual Scanning

z=0.05 : also followed by SNF and CSP

search template subtr

g

r

i

22




g

r

i


23




g

r

i



24

z = 0.09 z = 0.20

z = 0.29 z = 0.36search template subtr search template subtr

g

r

i

25

Lightcurve Fits Update in Real Time

day

mag

mag

mag

2 epochs 30 epochs

26

Lightcurve Fits Update in Real Time

day

mag

mag

mag

2 epochs 30 epochs

> 90% of photometric Ia

candidates were spectroscopicallyconfirmed to be

SN Ia

27

Follow-up Spectral id

Observer wavelength (Å)



Flu

x

Flu

x


HH

28

1516Probable SN Ia

37

193

267

3,700

14,400

20062005

18Confirmed SN other (Ib, Ic, II)

130Confirmed SN Ia

180 Candidates with ≥1 spectra

11,400 SN candidates

190,000Objects scanned

Survey Scan Stats Sako et al., AJ 135, 348 (2008)

29

Survey Scan Stats Sako et al., AJ 135, 348 (2008)

1516Probable SN Ia

37

197

267

3,700

14,400

20062005

18Confirmed SN other (Ib, Ic, II)

130Confirmed SN Ia

180 Candidates with ≥1 spectra

11,400 SN candidates

190,000Objects scanned

5221

93

498

736

19,000

220,000

Total2007

38

171

289

3,967

15,200

Plus ~ 1000 photometric SN Ia: we have 200 host-galaxy redshifts and still observing …

30

SN Fakes

Fake SN Ia were inserted into the images in real time to measure software & scanning efficiencies.

Here is a fakethat was missed !


g

r

i

31

SDSS-SN Redshift & Cadence

2005+20062005+2006+2007

32

SDSS-SN Redshift & Cadence

2005+20062005+2006+2007

Temporaledge effects:SNe peak too early or too late.May relax cutslater.

33

SDSS Rate for SN Ia with z < 0.12:2005 sample Dilday et al.,

arXiv:0801.3297Motivation: understand nature of SN

progenitorsContributions:

16 spectroscopically confirmed Ia (26 before cuts)

1 photometric-id with host spec-Z

34

Unbinned Likelihood Fit SDSS result: Dilday 2008 previous results with spectroscopic confirmation idem, but unclear efficiency (exclude from our fit)

Rate ~ (1+z)1.5 ± 0.6

35

SDSS SN Ia Rate: in progress

Spectro-Confirmed

Photometricid + host z

Photometric id only

~ 350 and larger syst-error

statistics vs.systematics

36

SDSS Hubble Diagram Analysis: Samples Include

• SDSS 2005 (~ 100)

• Low redshift from literature (26 or 44)

• SNLS published (~ 70)

• ESSENCE published (~ 60)

37

Supernova Photometry from Fit

SNcalibstars

FIT-DATA: all images (few dozen ugriz)

FIT-MODEL: galaxy + stars + SN + sky

FIT PROPERTIES: gal + stars: same in every image SN: variable in every image gal + stars + SN: PSF-smeared

NO PIXEL RE-SAMPLING ! no pixel correlations proper stat. errors

SDSS image

(Holtzman et. al., 2008, submitted)

38

Extensive Photometry Tests Include:

• Recover zero flux pre-explosion

• Recover star mags

• Recover flux from fake SN

39

Analysis Overview• Use both MLCS2k2 & SALT2 methods

(competing SNIa models)• Evaluate systematic uncertainties

40

Analysis Overview• Use both MLCS2k2 & SALT2 methods• Evaluate systematic uncertainties• Five sample-combinations

a) SDSS-only

b) SDSS + ESSENCE + SNLS

c) Nearby + SDSS

d) Nearby + SDSS + ESSENCE + SNLS

e) Nearby + ESSENCE + SNLS

}no nearby sample; SDSS is lowz anchor

SDSS ishigh-z sample

(nominal)

(compare to WV07)

41

Lightcurve Fit: Brief Introduction

• Fit data to parametric model (or template) to get shape and color.

• Use shape and color to “standardize” intrinsic luminosity.

SDSS dataFit model

42

Comparison of Lightcurve Fit Methods

dust + intrinsic (no assump)host-galaxy dustcolor variations

noneExtinction AV > 0Fitting prior

spectral surface vs. tU,B,V,R,I mag vs. trest-frame model

SALT2/SNLS

(Guy07)

MLCS2k2

(Jha 2007)property

43




not neededwarp composite SN Ia spectrum from Hsiao

K-correction


from global fitFit-param for each SN Iadistance modulus

SALT2

(Guy07)

MLCS2k2

(Jha 2007)property

44




not neededwarp composite SN Ia spectrum from Hsiao

K-correction


Turn-key code, but crucial SNLS spectra are private

requires highly trained chef

Training availability

black box provided by J.Guy of SNLS

wrote our own fitter with improvements & options

Fitter availability

all SNe used in trainingz < .1 : SN lum & shape

SDSS: RV, AV

Training

from global fitFit-param for each SN Iadistance modulus

SALT2

(Guy07)

MLCS2k2

(Jha 2007)property

vectors

45

SDSS SN Ia Lightcurves @z = 0.09 z = 0.19 z = 0.36

data-- fit model

46

Hubble Diagram

46995663

47

Cosmology Fit for w and M

(illustration with all 4 Ia samples)

CMB

(WM

AP 5ye

ar)

SN

Ia

BA

O

(s)

Comoving sep (h-1 Mpc)

BAO:Eisenstein et al., 2005

48

Fit Residuals

redshift

± .17 mag error added in fit, but not in plot)

49

Fit Residuals

?

smaller 2 is partly due to inefficiency from spectroscopic targeting.

50

a) SDSS-only


c) Nearby + SDSS

d) Nearby +SDSS + ESSENCE + SNLS


Systematicuncertainties forMLCS method.

Uncertainties forSALT2 nearly finished ...

Total systematic uncertainty 0.22 0.11 0.15 0.09 0.09Statistical uncertainty 0.25 0.12 0.18 0.10 0.12

51

Total Error Contours(stretch stat-contour along BAO+CMB axis

w

M

SDSS-only

systematic tests68% stat-error68% total-error

52

Results

MLCS Total-error contours

M

w

SALT2 stat-error contours(expect stat ~ syst )

53

Results

M

w

ESSENCE: Wood-Vasey, AJ 666, 694 (2007)SNLS: Astier, AJ 447, 31 (2006)

MLCS Total-error contours

54

Questions to Ponder

Q1: Why does our MLCS-based w-result

differ by ~ 0.3 compared to WV07

(same method & same data) ?

Q2: Why do MLCS and SALT2 results differ

when high-redshift samples

(ESSENCE + SNLS) are included ?

55

Questions to PonderQ1: Why does our MLCS-based w-result differ by ~ 0.3 compared to WV07 (same method & same data) ?

w ~ .1 : different “RV” to describe host-galaxy extinction w ~ .1 : account for spectroscopic inefficiency w ~ .1 : require z > .025 instead of z > .015 to avoid Hubble anomalyMisc: different Bessell filter shifts, fit in flux, Vega BD17

Note: changes motivated by SDSS-SN observations !

Note: changes do NOT commute; depends on sequence.

56

Dust Law: RV = AV/E(B-V)and A() from

Previous MLCS-based analyses assumed RV = 3.1 (global parameter)

Growing evidence points to RV ~ 2:

SALT2 “” (RV+1) = 2 - 2.5

LOWZ studies (Nobili 08: RV = 1.8) individual SN with NIR (Krisciunas)

We have evaluated RV with our own SDSS data

Cardelli, Clayton, Mathis ApJ, 345, 245 (1989)

57

Dust Law: RV = AV/E(B-V)To measure a global property of SN Ia, need sample with well-understood efficiency

Spec-confirmed SN Ia sample has large (spec) inefficiencythat is not modeled by the sim.

58

Dust Law: RV = AV/E(B-V)To measure a global property of SN Ia, need sample with well-understood efficiency

Spec-confirmed SN Ia sample has large (spec) inefficiencythat is not modeled by the sim.

z < .3“Dust sample”

Solution: include photometric SNe Ia with host-galaxy redshift !

59

Dust Law: RV = AV/E(B-V)

Method: minimizedata-MC chi2 forcolor vs. epoch

60

Dust Law: RV = AV/E(B-V)

SDSS Result:RV = 1.9 ± 0.2stat ± 0.6syst

Consistent with SALT2 and other SN-based studies.

Method: minimizedata-MC chi2 forcolor vs. epoch

61

Spectroscopic Inefficiency

• Simulate all known effects using REAL observing conditions

• Compare data/sim redshift distributions

• Difference attributed to spectroscopic ineff.

62

Spectroscopic efficiency modeled as exp(-mV/) Eff(spec) is included in fitting prior … Assign w-syst error = 1/2 change from this effect

63

a) SDSS-only


c) Nearby + SDSS

d) Nearby + SDSS + ESSENCE + SNLS


Spectroscopic efficiency modeled as exp(-mV/) Eff(spec) is included in fitting prior … Assign w-syst error = 1/2 change from this effect

64

Hubble Anomalya.k.a “Hubble Bubble”

Conley et. al. astro-ph/0705.0367

?

• Hubble anomaly in LOWZ sample: cz=7500 km/s

• About x2 smaller with RV=1.9 (compared to RV=3.1), but still there

• Error bars reflect RMS spread

fit from data; calc calculated from concordance model

65

Hubble Anomaly

• SDSS data suggests z >.025 (instead of .015) to avoid Hubble anomaly.

• Reduces LOWZ sample from 44 to 26 SNe Ia.

• Increases w by ~ .1 ;

• Add .05 to w-syst

(2005 only)

66

MLCS vs. SALT2 Now that we use MLCS-RV value consistent with

SALT2-, cosmology results become more discrepant ! Puzzling ??

Ignore fitting prior & allow AV < 0 w = .06 << MLCS-SALT2 discrepancy

Discrepancy is from model, NOT from SDSS data

Guesses: difference is in the training or problem in treating efficiency in one of the methods

Comparisons still in progress

67

What Next?

Primordial Neutrinos?

CMB PolarizationIR Observation

SN Expansion History

Weak Lensing

Clusters

LSST

JDEM

68

Conclusions Paper in preparation (with 99 SDSS SNe Ia, z = 0.05-0.40) side-by-side comparisons of MLCS vs. SALT2

SDSS “photometric SNe Ia + zhost ” are used to measure dust properties (RV) … important step toward using photo-SN in Hubble diagram, and quantifying survey efficiency.

SDSS SN with z < .15 may help understand low-z Hubble anomaly.

Need publicly available training codes to optimize training and evaluate systematic errors.

all SDSS-based analysis (fitter & sim) is publicly available now … data available with paper.

Three-season SDSS SN survey is done. Still acquiring host-galaxy redshifts to improve measurement of dust properties and for more SN Ia on the Hubble diagram.

1 Cosmology Results from the SDSS Supernova Survey David Cinabro SMU 15 September 2008.

Documents