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

2. 2 Contents  Cosmology and Dark Energy Intro  SDSS Supernova Survey (2005-07)‏  Hubble Diagram Analysis & Results (1st-year SDSS data + external)‏

3. 3 Primary Motivation for Supernova Surveys: measure expansion history of the Universe: in particular, the role of dark energy

4. 4 Expansion Basics H(z) 2 = H 0 2  i  i (1+z) 3(1+w) where w (equation of state parameter) is pressure/density 0.7   = constant Cosmological constant (Best current guess)‏ ~ 10 -5   (1+z) 4 1/3Radiation (CMB)‏ 0.3  M (1+z) 3 v 2 /c 2 ~ 0Matter (dark, baryon, relic )‏  at z=0 Evolution with zw Source of expansion

5. 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

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7. 7 Hubble Diagram Basics Expansion history depends on   and  M

8. 8 Hubble Diagram Basics Expansion history depends on   and  M What we measure with SNe … relative to empty universe mag = –2.5log( L /4  d L 2 ). d L = (1+z)∫dz/H(z) for flat universe. Distance modulus: 5log(d L /10pc) ‏

9. 9 Hubble Diagram Basics Expansion history depends on   and  M What we measure with SNe … relative to empty universe

10. 10 w-sensitivity with Supernova

11. 11 w-Quest with Supernova SDSS SNLS, ESSENCE w = –0.9 gives 4% variation from w = –1 redshift

12. 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  ma g  M =1   =0 1990s Development & discovery phase (Hi-z, SCP). Lightcurve quality limited by telescope time.

13. 13 Surveys 2000s Much more telescope time  rolling searches & more passbands. (SNLS, ESSENCE, SDSS)‏ 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  ma g SNLS 1st year sample (Astier 2005) plus ~ 40 low-z SNe from literature  M =1   =0 1990s Development & discovery phase (Hi-z, SCP). Lightcurve quality limited by telescope time.

14. 14 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  ma g SNLS 1st year sample (Astier 2005) plus ~ 40 low-z SNe from literature  M =1   =0 1990s Development & discovery phase (Hi-z, SCP). Lightcurve quality limited by telescope time. 2000s Much more telescope time  rolling searches & more passbands. (SNLS, ESSENCE, SDSS)‏

15. 15 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  ma g SNLS 1st year sample (Astier 2005) plus ~ 40 low-z SNe from literature  M =1   =0 SDSS survey fills gap & adds low-z SNe 1990s Development & discovery phase (Hi-z, SCP). Lightcurve quality limited by telescope time. 2000s Much more telescope time  rolling searches & more passbands. (SNLS, ESSENCE, SDSS)‏

16. 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. 17 AJ 135, 338 (2008)‏ Meet the SDSS-II Supernova Team

18. 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. 19 SDSS Data Flow One full night collects 800 fields (ugriz per field)  200 GB one raw g-field (0.15 0 )‏ 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. 20 SDSS Data Flow One full night collects 800 fields (ugriz per field)  200 GB one raw g-field (0.15 0 )‏

21. 21 SDSS Manual Scanning z=0.05 : also followed by SNF and CSP search template subtr g r i

22. 22 SDSS Manual Scanning z=0.05 : also followed by SNF and CSP search template subtr g r i

23. 23 SDSS Manual Scanning z=0.05 : also followed by SNF and CSP search template subtr g r i

24. 24 z = 0.09z = 0.20 z = 0.29 z = 0.36 search template subtr g r i

25. 25 Lightcurve Fits Update in Real Time day mag 2 epochs 30 epochs

26. 26 Lightcurve Fits Update in Real Time day mag 2 epochs 30 epochs > 90% of photometric Ia candidates were spectroscopically confirmed to be SN Ia

27. 27 Follow-up Spectral id Observer wavelength (Å)‏ Flux Observer wavelength (Å)‏ HH HH

28. 28 1516Probable SN Ia 37 193 267 3,700 14,400 20062005 18Confirmed SN other (Ib, Ic, II)‏ 130Confirmed SN Ia 180Candidates with ≥1 spectra 11,400SN candidates 190,000Objects scanned Survey Scan Stats Sako et al., AJ 135, 348 (2008)

29. 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 180Candidates with ≥1 spectra 11,400SN 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. 30 SN Fakes Fake SN Ia were inserted into the images in real time to measure software & scanning efficiencies. Here is a fake that was missed ! search template subtr grigri

31. 31 SDSS-SN Redshift & Cadence 2005+2006 2005+2006+2007

32. 32 SDSS-SN Redshift & Cadence 2005+2006 2005+2006+2007 Temporal edge effects: SNe peak too early or too late. May relax cuts later.

33. 33 SDSS Rate for SN Ia with z < 0.12: 2005 sample  Dilday et al., arXiv:0801.3297 Motivation: understand nature of SN progenitors Contributions: 16 spectroscopically confirmed Ia (26 before cuts)‏ 1 photometric-id with host spec-Z

34. 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. 35 SDSS SN Ia Rate: in progress Spectro- Confirmed Photometric id + host z Photometric id only ~ 350 and larger syst- error statistics vs. systematics

36. 36 SDSS Hubble Diagram Analysis: Samples Include SDSS 2005 (~ 100)‏ Low redshift from literature (26 or 44)‏ SNLS published (~ 70)‏ ESSENCE published (~ 60)‏

37. 37 Supernova Photometry from Fit SN  calib stars 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. 38 Extensive Photometry Tests Include: Recover zero flux pre-explosion Recover star mags Recover flux from fake SN

39. 39 Analysis Overview Use both MLCS2k2 & SALT2 methods (competing SNIa models)‏ Evaluate systematic uncertainties

40. 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 is high-z sample (nominal)‏ (compare to WV07)‏

41. 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 data Fit model

42. 42 Comparison of Lightcurve Fit Methods dust + intrinsic (no assump)‏host-galaxy dustcolor variations noneExtinction A V > 0Fitting prior spectral surface vs. tU,B,V,R,I mag vs. trest-frame model SALT2/SNLS (Guy07)‏ MLCS2k2 (Jha 2007)‏property

43. 43 Comparison of Lightcurve Fit Methods dust + intrinsic (no assump)‏host-galaxy dustcolor variations noneExtinction A V > 0Fitting prior not neededwarp composite SN Ia spectrum from Hsiao K-correction spectral surface vs. tU,B,V,R,I mag vs. trest-frame model from global fitFit-param for each SN Iadistance modulus SALT2 (Guy07)‏ MLCS2k2 (Jha 2007)‏property

44. 44 Comparison of Lightcurve Fit Methods dust + intrinsic (no assump)‏host-galaxy dustcolor variations noneExtinction A V > 0Fitting prior not neededwarp composite SN Ia spectrum from Hsiao K-correction spectral surface vs. tU,B,V,R,I mag vs. trest-frame model 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: R V, A V Training from global fitFit-param for each SN Iadistance modulus SALT2 (Guy07)‏ MLCS2k2 (Jha 2007)‏property vectors

45. 45 SDSS SN Ia Lightcurves @ z = 0.09 z = 0.19 z = 0.36 data -- fit model

46. 46 Hubble Diagram 46 99 56 63

47. 47 Cosmology Fit for w and  M (illustration with all 4 Ia samples)‏ CMB (WMAP 5year)‏ SN Ia BAO  (s)‏ Comoving sep (h -1 Mpc)‏ BAO: Eisenstein et al., 2005

48. 48 Fit Residuals redshift ±.17 mag error added in fit, but not in plot)‏

49. 49 Fit Residuals ? smaller  2 is partly due to inefficiency from spectroscopic targeting.

50. 50 a) SDSS-only b) SDSS + ESSENCE + SNLS c) Nearby + SDSS d) Nearby +SDSS + ESSENCE + SNLS e) Nearby + ESSENCE + SNLS Systematic uncertainties for MLCS method. Uncertainties for SALT2 nearly finished... Total systematic uncertainty 0.22 0.11 0.15 0.09 0.09 Statistical uncertainty 0.25 0.12 0.18 0.10 0.12

51. 51 Total Error Contours (stretch stat-contour along BAO+CMB axis w MM SDSS-only systematic tests 68% stat-error 68% total-error

52. 52 Results MLCS Total-error contours MM w SALT2 stat-error contours (expect  stat ~  syst )‏

53. 53 Results MM w  ESSENCE: Wood-Vasey, AJ 666, 694 (2007)‏ SNLS: Astier, AJ 447, 31 (2006)‏ MLCS Total-error contours

54. 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. 55 Questions to Ponder Q1: Why does our MLCS-based w-result differ by ~ 0.3 compared to WV07 (same method & same data) ?   w ~.1 : different “R V ” to describe host-galaxy extinction   w ~.1 : account for spectroscopic inefficiency   w ~.1 : require z >.025 instead of z >.015 to avoid Hubble anomaly Misc: 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. 56 Dust Law: R V = A V /E(B-V) and A( ) from  Previous MLCS-based analyses assumed R V = 3.1 (global parameter)‏  Growing evidence points to R V ~ 2:  SALT2 “  ” (R V +1) = 2 - 2.5  LOWZ studies (Nobili 08: R V = 1.8)‏  individual SN with NIR (Krisciunas)‏  We have evaluated R V with our own SDSS data Cardelli, Clayton, Mathis ApJ, 345, 245 (1989)‏

57. 57 Dust Law: R V = A V /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) inefficiency that is not modeled by the sim.

58. 58 Dust Law: R V = A V /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) inefficiency that is not modeled by the sim. z <.3 “Dust sample” Solution: include photometric SNe Ia with host-galaxy redshift !

59. 59 Dust Law: R V = A V /E(B-V)‏ Method: minimize data-MC chi2 for color vs. epoch

60. 60 Dust Law: R V = A V /E(B-V)‏ SDSS Result: R V = 1.9 ± 0.2 stat ± 0.6 syst Consistent with SALT2  and other SN-based studies. Method: minimize data-MC chi2 for color vs. epoch

61. 61 Spectroscopic Inefficiency Simulate all known effects using REAL observing conditions Compare data/sim redshift distributions Difference attributed to spectroscopic ineff.

62. 62  Spectroscopic efficiency modeled as exp(-m V /  )‏  Eff(spec) is included in fitting prior …  Assign w-syst error = 1/2 change from this effect

63. 63 a) SDSS-only b) SDSS + ESSENCE + SNLS c) Nearby + SDSS d) Nearby + SDSS + ESSENCE + SNLS e) Nearby + ESSENCE + SNLS  Spectroscopic efficiency modeled as exp(-m V /  )‏  Eff(spec) is included in fitting prior …  Assign w-syst error = 1/2 change from this effect

64. 64 Hubble Anomaly a.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. 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. 66 MLCS vs. SALT2  Now that we use MLCS-R V value consistent with SALT2- , cosmology results become more discrepant ! Puzzling ??  Ignore fitting prior & allow A V < 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. 67 What Next? Primordial Neutrinos? CMB Polarization IR Observation SN Expansion History Weak Lensing Clusters LSST JDEM

68. 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 + z host ” 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.