Cosmology of Photometrically- Classified Type Ia Supernovae Campbell et al. 2013 arxiv:1211.4480 Heather Campbell Collaborators: Bob Nichol, Chris D'Andrea, Mat Smith, Masao Sako and all the SDSS-II SN collaboration
Outline Background Motivation Data SDSS-II SN survey & BOSS host galaxy redshift Photometric Classification Simulation optimization Data Hubble Diagram Cosmology
Background: Type Ia Supernovae Standard candles Used to discover the accelerated expansion of the Universe Distance Modulus (m-m) =0.3, =0.0 (m-m) - (m-m) 44 42 40 38 36 34 1.0 0.5 0.0-0.5 Low-redshift (z < 0.15) SNe: CfA & other SN follow-up Calan/Tololo SN Search High-redshift (z > 0.15) SNe: High-Z SN Search Team Supernova Cosmology Project =0.3, =0.7 =0.3, =0.0 =1.0, =0.0 0.01 0.10 1.00 z effective m B mag residual standard deviation 24 22 20 18 16 14 1.5 1.0 0.5 0.0-0.5-1.0-1.5 Calan/Tololo (Hamuy et al, A.J. 1996) Supernova Cosmology Project 6 4 2 0-2 -4-6 (c) 0.0 0.2 0.4 0.6 0.8 1.0 redshift z Riess et al. 1998 Perlmutter et al. 1999 (, ) = ( 0, 1 ) (0.5,0.5) (0, 0) ( 1, 0 ) (1, 0) (1.5, 0.5) (2, 0) (a) (b) Flat (, ) = (0, 1 ) = 0 (0.28, 0.72) (0, 0) (0.5, 0.5 ) (0.75, 0.25 ) (1, 0)
Background: Current Results Best current cosmological constraints combination of SNe Ia data: SDSS-II SN survey and SNLS combined with BAO and CMB All SNe Ia are spectroscopically confirmed Sullivan et al. 2011 Kessler et al. 2009
Background: Classification Spectroscopic classification Lack of Hydrogen and Helium features Presence of Si II absorption line at 6150A
Motivation: Classification How do we do SN surveys in the future? Too many SNe for spectroscopic follow up - DES (4000 SNe for cosmology) - Gaia (6000 low redshift SNe) - LSST (100,000 SNe) SDSS + BOSS is a case study Aim: Show cosmological constraints are possible and competitive
SDSS-II Supernova Project Sako et al. in prep Sept-Nov between 2005 and 2007 The Sloan Telescope Regularly scanned Stripe 82 Database of 10,000s of transient objects SDSS-II SN survey 504 spectroscopically confirmed Type Ia Cosmological analysis of the first year SDSS-II data
SDSS-II Supernova Project Sako et al. in prep Sept-Nov between 2005 and 2007 Stripes in Sloan Regularly scanned Stripe 82 Database of 10,000s of transient objects SDSS-II SN survey 504 spectroscopically confirmed Type Ia Cosmological analysis of the first year SDSS-II data The SDSS observes the sky in stripes. Each of these eight long stripes contains millions of galaxies.
BOSS Host Galaxy Follow-up Olmstead et al. in prep 3323 galaxies with accurate redshifts 1) Probability of being a SNe (2382) 2) Random sample of transients (941) Plates drilled with 1000 holes Spectroscopic redshift Anchors SNe Ia on Hubble diagram Improves classification and light curve fit Large sample of Host galaxy spectra: investigate intrinsic scatter
PSNID Photometric Classification Fits templates to the light curves to find lowest Χ2 Calculated Bayesian probabilities of the SN being a Type Ia, Type Ib/c or Type II χ 2 E Ia = P ( z)e 2 dz da v dt max d Δ m 15 d µ, P(z )= 1 e (z z ext) 2 2 /2 σ z. 2 πσ z E type P Ia = E Ia + E Ibc +E II BOSS host galaxy redshift used as a prior
Classification optimization using Simulations Public SDSS-II SN Simulations (Kessler) created using SNANA Redshift range and observing conditions to replicate SDSS-II SN survey 100 100 Ia Ia 80 80 Ia, Ia and FoM 60 40 20 Ia (W=1) FoM(W=5) Ia, Ia and FoM 60 40 20 Ia (W=1) FoM(W=5) SIMULATION 0 0.0 0.2 0.4 0.6 0.8 1.0 P Ia cut High efficiency, low purity SIMULATION 0 0.5 1.0 1.5 2.0 2.5 3.0 r 2 cut Higher purity, low efficiency
Classification optimization using Simulations 0.6 0.4 Color-magnitude cut 0.2 2.0 Color 0.0 1.5 SIMULATION -0.2 1.0-0.4-0.6 SIMULATION g - r 0.5-5 -4-3 -2-1 0 1 2 3 4 5 X 1 SATL2 parameter cut 0.0-0.5 16 18 20 22 24 26 g
Simulations Results Selection cut Contamination Efficiency PIa <Pnon Ia (PSNID) 41.8% 99.1% Χ2 <1.2 (PSNID) 27.8% 91.% X1 and color cut (SALT2) 8.3% 71.6% Color-magnitude cut (SALT2) 3.9% 70.8% 44 SNe Ia Non-Ia SNe Distance Modulus 42 40 38 SIMULATION Hubble Residuals (mag) 36 1 0-1 0.0 0.1 0.2 0.3 0.4 0.5 Redshift
Data: Classification 0.6 0.4 Color-magnitude cut Color 0.2 0.0 1.5 DATA -0.2 1.0-0.4-0.6 DATA g - r 0.5-5 -4-3 -2-1 0 1 2 3 4 5 x 1 SALT2 parameter cut 0.0-0.5 17 18 19 20 21 22 23 24 g
Bias Tests: Contamination Bias True cosmology contaminates that are Photometrically-classified as SNe Ia, with all selection criteria Redshift dependent contamination bias is small Contamination Bias 0.05 0.04 0.03 0.02 0.01 0.00-0.01-0.02-0.03-0.04 SIMULATION -0.05 0.1 0.2 0.3 0.4 0.5 Redshift
Bias Tests: Malmquist Bias Malmquist Bias correction 30,000 SNe Ia SNANA simulations Model for the Malmquist bias as a function of redshift. µ corr (z)=a e bz +c Malmquist Bias 0.05 0.00-0.05-0.10-0.15 SIMULATION -0.20 0.0 0.1 0.2 0.3 0.4 0.5 Redshift
Photometric Hubble Diagram 752 SN Ia (SDSS-II SN light curves + BOSS host redshifts) 44 185 spec Ia 524 non-spec Ia DATA Distance Modulus Hubble Residuals (mag) 42 40 38 36 1 0-1 Malmquist Bias correction 0.0 0.2 0.4 0.6 Redshift 2.5 times larger than SDSS-II SN spectroscopic SNe Ia sample
Cosmological Constraints 1.2 DATA SNLS3 Cosmological constraints comparable to SNLS3 (SN data only) 1.0 0.8 FLAT BOSS Photometric SNe Ia 0.6 Statistics only-result favors an accelerating Universe at 99.96% confidence 0.4 0.2 0.0 0.0 0.2 0.4 0.6 0.8 M ACCELERATION
Cosmological Constraints 0.0-0.6 DATA DATA -0.5-0.8-1.0 w -1.5 BOSS Photometric SNe Ia w -1.0-2.0-2.5 SDSS Spec-Ia (z<0.3) -1.2 SNLS3 BOSS Photometric SNe Ia -3.0 0.0 0.2 0.4 0.6 0.8 1.0 m SHOES H0 prior -1.4 0.25 0.30 0.35 m SDSS DR7 LRGs + WMAP7 CMB power spectrum + SHOES H0 prior
Summary 3392 BOSS redshifts of SDSS-II SN candidate host galaxies Largest single survey Hubble diagram (752) using only photometric classification 3.9% contamination: shows it has no effect on our cosmological constraints Cosmological constraints comparable to spectroscopic surveys Demonstrates the potential of photometrically classified SN Ia samples in improving cosmological constraints This sample can be used to test systematics, which will be important for the next generation of SNe Ia surveys.