1. 1 SDSS-II Supernova Survey Josh Frieman Leopoldina Dark Energy Conference October 8, 2008 See also: poster by Hubert Lampeitl, talk by Bob Nichol

2. 2 SN Models and Observations SN cosmology based on a purely empirical approach (Phillips) SN observations over the last decade have strengthened evidence for cosmic acceleration, but dark energy constraints now dominated by systematic errors SNe will be one of 3 dark energy probes pursued by JDEM Reaching JDEM level of precision for SNe will require improved control of systematics Improved SN modeling, better empirical approaches to estimating SN distances, and better data are all important weapons in the arsenal to reduce systematics Current empirical distance estimators are limited by the paucity of high-quality input/training data. The situation is improving (CfA, CSP, KAIT, SNF, SDSS), but we need better, homogeneous data at low/intermediate redshifts and a systematic approach to ingesting them to build better empirical estimators. Will current ground-based SN surveys deliver what we need for JDEM?

3. Published Light Curves for Nearby Supernovae Nearby SNe used to train distance estimators and anchor Hubble diagram Heterogeneous published sample, subject to various selection biases

4. 4 Cosmic Acceleration Discovery from High-redshift SNe Ia SNe at z~0.5 are 25% fainter than in an open Universe with same value of  m   = 0.7   = 0.  m = 1. Technological Redshift Desert: Possible photometric offsets between low- and high-redshift data Desert still there 10 years later

5. 5 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 Spectroscopic follow-up for redshifts, SN typing, and to study diversity of SN features Probe Dark Energy and systematics in redshift range complementary to other surveys Well-observed, homogeneous sample to anchor Hubble diagram & train distance estimators Large survey volume: rare & peculiar SNe, probe outliers of population to test SN models

6. Frieman, et al (2008); Sako, et al (2008)

7. Spectroscopic follow-up telescopes R. Miquel, M. Molla CfA team P. Challis, G. Narayan, R. Kirshner

8. Search Template Difference grigri Searching For Supernovae 2005 –118,693 objects scanned –10,937 unique candidates –130 confirmed Ia 2006 –14,430 scanned –3,694 candidates –193 confirmed Ia 2007  13,613 scanned  3,962 candidates –175 confirmed Ia Positional match to remove movers Insert fake SNe to monitor efficiency

9. B. Dilday

10. Redshift Distribution for SNe Ia and counting

11. Well-sampled, multi-band light curves, including measurements before peak light SDSS SN Light- curves Holtzman et al (2008)

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

13. 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

14. 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 6m TNG 3.5m SALT 10m 2005+2006

15. SDSS SN Ia Spectra ~1000 spectra taken over 3 seasons Zheng et al (2008)

16. Fitting SN light curves I: MLCS2k2 Multicolor Light Curve Shape (Riess et al '98; Jha et al '07) Model SN light curves as a single parameter family, trained on low-z UBVRI data from the literature Assumes SN color variations are due to dust extinction, subject to prior fit parameters time-dependent model “vectors” Time of maximum distance modulus dust law extinction stretch/decline rate P(A v )

17. MLCS2k2 model templates ∆ = -0.3: bright, broad ∆ = +1.2: faint, narrow Jha et al, 2007

18. 18 Fitting SN Light curves II: SALT2 Fit each light curve using rest-frame spectral surfaces*: Transform to observer frame: Light curves fit individually, but distances only estimated globally: *Not trained just on low-redshift data; distances are cosmology-dependent, flat priors on model parameters Global fit parameters, determined along with cosmological parameters color term Guy et al light-curve shape

19. 19 Light Curve Fitting with MLCS2k2 and SALT2

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

21. 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

22. Extract A V Distribution from SDSS (no prior)

23. Extract R V distribution from SDSS SN data MLCS previously used Milky Way avg R V =3.1 Lower R V more consistent with SALT2 color law Not conventional dust

24. Preliminary Cosmology Results Kessler, Becker, et al. 2008 w open

25. 25 Issues with rest-frame U band Data vs. SALT2 Model Residuals Similar Low-z vs. High-z discrepancy seen in MLCS MLCS trained only on Low-z, SALT2 model dominated by SNLS Similar differences seen in rest-frame UV spectra (Foley et al) epoch

26. 26 SN Ia vs. Host Galaxy Properties: I Smith et al Bright SN Luminosity/Decline Rate Faint

27. 27 SN Ia vs. Host Galaxy Properties: II Smith et al Color/reddening Is reddening local to the SN environment?

28. 28 SN Ia vs. Host Galaxy Properties: III Smith et al Preliminary Two SN Ia Populations? Implications for SN cosmology: host-galaxy population evolution

29. Future: Improved SN Ia Distances Train Fitters Fit Cosmology