Cosmology with Supernovae Lecture 2 Josh Frieman I

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

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

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

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

Brighter Slower, Bluer Use to reduce Peak Luminosity Dispersion: Peak Luminosity SN Ia Peak Luminosity Empirically correlated with Light-Curve Decline Rate and Color Phillips 1993 Garnavich, etal Rate of decline

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 Type Ia SN Peak Brightness as calibrated Standard Candle Time 6

Published Light Curves for Nearby Supernovae Low-z SNe: Anchor Hubble diagram Train Lightcurve fitters Need wellsampled, wellcalibrated, multi -band light curves 7

Low-z Data 8

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 9

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

Riess, etal High-z Data (1998) 11

High-z Supernova Team data (1998) 12

Likelihood Analysis This assume Goliath etal 2001 13

High-z SN Team Supernova Cosmology Project 14

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, multiband 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

Supernova Legacy Survey (2003 -2008) 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 -10 m telescopes Megaprime Mosaic CCD camera 16

Magellan VLT Spectra SN Identification Redshifts 120 hr/yr: France/UK FORS 1&2 for types, redshifts 3 nights/yr: Toronto IMACS for host redshifts Gemini Keck 8 nights/yr: LBL/Caltech DEIMOS/LRIS for types, intensive study, cosmology with SNe II-P 120 hr/yr: Canada/US/UK GMOS for types, redshifts

Power of a Rolling Search SNLS Light curves

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

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

60 ESSENCE SNe 72 SNLS SNe 21

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Higher-z SNe Ia from ACS Z=1. 39 Z=0. 46 Z=0. 52 Z=1. 23 50 SNe Ia, 25 at z>1 Z=1. 03 Riess, etal

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

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

Likelihood Analysis with BAO and CMB Priors 26

Recent Dark Energy Constraints Improved SN constraints Inclusion of constraints from WMAP Cosmic Microwave Background Anisotropy (Joana) and SDSS Largescale Structure (Baryon Acoustic Oscillations; Bruce, Daniel) assuming w = − 1 Only statistical errors shown 27

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

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: SALT 2, Si. FTO

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

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 32

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

Searching For Supernovae Search g Template Difference • 2005 – 190, 020 objects scanned – 11, 385 unique candidates – 130 confirmed Ia • 2006 r – 14, 441 scanned – 3, 694 candidates – 193 confirmed Ia • 2007 – 175 confirmed Ia i • Positional match to removers • Insert fake SNe to monitor efficiency

SDSS SN Photometry Holtzman etal (2008) 35

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

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

Spectroscopic Target Selection 2 Epochs 31 Epochs SN Ia Fit SN Ibc Fit Fit with template library Classification >90% accurate after 2 -3 epochs Redshifts 5 -10% accurate SN II Fit Sako etal 2008

SN and Host Spectroscopy MDM 2. 4 m NOT 2. 6 m APO 3. 5 m Determine NTT 3. 6 m KPNO 4 m SN Type WHT 4. 2 m and Subaru 8. 2 m Redshift HET 9. 2 m Keck 10 m Magellan 6. 5 m TNG 3. 5 m SALT 10 m SDSS 2. 5 m 2005+2006

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

Fitting SN Ia Light Curves • Multi-color Light Curve Shape (MLCS 2 k 2) Riess, etal 96, 98; Jha, etal 2007 • SALT-II Guy, etal 05, 08 41

MLCS 2 k 2 Light-curve Templates in rest-frame j=UBVRI; built from ~100 well-observed, nearby SNe Ia ∆ <0: bright, broad ∆ >0: faint, narrow, redder time-dependent model “vectors” trained on Low-z SNe observed passband fit parameters Time of maximum distance modulus host gal extinction stretch/decline rate

Host Galaxy Dust Extinction • Extinction: • Empirical Model for wavelength dependence: • MLCS: AV is a fit parameter, but RV is usually fixed to a global value (sharp prior) since it’s usually not well determined SN by SN Cardelli etal 89 (CCM) 43

Host Galaxy Dust Extinction Historically, MLCS used Milky Way average of RV=3. 1 Growing evidence that this doesn’t represent SN host galaxy population well Milky Way avg. Jha 44

Extract RV by matching colors of SDSS SNe to MLCS simulations • Use nearly complete (spectroscopic + photometric) sample • MLCS previously used Milky Way avg RV=3. 1 • Lower RV more consistent with SALT color law and other recent SN RV estimates D. Cinabro

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

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

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

Local Dust? Goobar (2008): higher density of dust grains in a shell surrounding the SN: multiple scattering steepens effective dust law (also Wang) Two Highly Reddened SNe Folatelli et al. (2009)

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

Inferred P(AV) Inferred P( ) Priors & Efficiencies Determine priors and efficiencies from data and Monte Carlo simulations

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

Model Selection Function

Include Selection Function

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

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

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

MLCS Likelihood Contours for this object 59