1. 1 Cosmology with Supernovae: Lecture 2 Josh Frieman I Jayme Tiomno School of Cosmology, Rio de Janeiro, Brazil July 2010

2. Hoje V. Recent SN Surveys and Current Constraints on Dark Energy VI. Fitting SN Ia Light Curves & Cosmology VII. Systematic Errors in SN Ia Distances 2

3. Coming Attractions VIII. Host-galaxy correlations IX. SN Ia Theoretical Modeling X. SN IIp Distances XI. Models for Cosmic Acceleration XII. Testing models with Future Surveys: Photometric classification, SN Photo-z’s, & cosmology 3

4. Luminosity Time  m 15 15 days Empirical Correlation: Brighter SNe Ia decline more slowly and are bluer Phillips 1993

5. 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, etal

6. 6 Type Ia SN Peak Brightness as calibrated Standard 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 Time

7. 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 curves

8. Low-z Data 8

9. 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 1996

10. 10 Acceleration Discovery Data: High-z SN Team 10 of 16 shown; transformed to SN rest-frame Riess etal Schmidt etal V B+1

11. Riess, etal High-z Data (1998) 11

12. High-z Supernova Team data (1998) 12

13. Likelihood Analysis This assume 13 Goliath etal 2001

14. 14 High-z SN Team Supernova Cosmology Project

15. 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<0.3 Extension to previously underexplored rest-frame wavelengths (Near-infrared) Vast increase in spectroscopic data Identification of SN Ia subpopulations (host galaxies) Entered the systematic error-dominated regime, but with pathways to reduce systematic errors 15

16. 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 telescopes

17. 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

18. Power of a Rolling Search SNLS Light curves

19. 19 First-Year SNLS Hubble Diagram SNLS 1 st Year Results Astier et al. 2006 Using 72 SNe from SNLS +40 Low-z

20. 20 Wood-Vasey, etal (2007), Miknaitis, etal (2007): results from ~60 ESSENCE SNe (+Low-z)

21. 21 60 ESSENCE SNe 72 SNLS SNe

22. 22

23. Higher-z SNe Ia from ACS Z=1.39Z=1.23 Z=0.46Z=0.52Z=1.03 50 SNe Ia, 25 at z>1 Riess, etal

24. 24  (m-M) HST GOODS Survey (z > 1) plus compiled ground-based SNe Riess etal 2004

25. Supernova Cosmology Project SN Ia Union Compilation Kowalski et al., ApJ, 2008 Data tables and updates at http://supernova.lbl.gov/Union

26. 26 Likelihood Analysis with BAO and CMB Priors

27. 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 = −1

28. 28

29. 29 Only statistical errors shown assuming flat Univ. and constant w

30. SNLS Preliminary 3 rd year Hubble Diagram Conley et al, Guy etal (2010): results with ~252 SNLS SNe Independent analyses with 2 light-curve fitters: SALT2, SiFTO

31. Results published from 2005 season Frieman, et al (2008); Sako, et al (2008) Kessler, et al 09; Lampeitl et al 09; Sollerman et al 09

32. 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 diagram Probe 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-calibrated

33. Spectroscopic follow-up telescopes R. Miquel, M. Molla, L. Galbany

34. Search Template Difference grigri 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 efficiency

35. 35 SDSS SN Photometry Holtzman etal (2008)

36. B. Dilday 500+ spec confirmed SNe Ia + 87 conf. core collapse plus >1000 photometric Ia’s with host z’s

37. Spectroscopic Target Selection 2 Epochs SN Ia Fit SN Ibc Fit SN II Fit Sako etal 2008

38. 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

39. 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 Redshift

40. Spectroscopic Deconstruction SN model Host galaxy model Combined model Zheng, et al (2008)

41. Fitting SN Ia Light Curves Multi-color Light Curve Shape (MLCS2k2) Riess, etal 96, 98; Jha, etal 2007 SALT-II Guy, etal 05,08 41

42. 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 passband

43. 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)

44. 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 well

45. Extract R V by matching colors of SDSS SNe to MLCS simulations Use nearly complete (spectroscopic + photometric) sample MLCS previously used Milky Way avg R V =3.1 Lower R V more consistent with SALT color law and other recent SN R V estimates D. Cinabro

46. Carnegie Supernova Project: Low-z CSP is a follow-up project Goal: optical/NIR light-curves and spectro-photometry for > 100 nearby SNIa > 100 SNII > 20 SNIbc Filter set: BV + u’g’r’i’ + YJHK Understand SN physics Use as standard candles. Calibrate distant SN Ia sample

47. CSP Low-z Light Curves Folatelli, et al. 2009 Contreras, et al. 2009: 35 optical light curves (25 with NIR)

48. Varying Reddening Law? Folatelli et al. (2009) 2005A 2006X

49. 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 SNe

50. Priors & Efficiencies Determine priors and efficiencies from data and Monte Carlo simulations

51. Priors & Efficiencies Determine priors and efficiencies from data and Monte Carlo simulations Inferred P(A V ) Inferred P(  )

52. 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 missing Difficult to model spectroscopic selection

53. Model Selection Function

54. Include Selection Function

55. Monte Carlo Simulations match data distributions Use recorded observing conditions (local sky, zero-points, PSF, etc)

56. 56

57. 57 Show likelihood plots for MLCS MLCS fit to one of the ESSENCE SNe

58. 58 prior Marginalized PDFs μ distribution approximated by Gaussian for cosmology fit

59. 59 MLCS Likelihood Contours for this object