The Rate of Type Ia SNe at Redshift z=0.2 from SDSS-I Overlapping Fields Horesh Assaf, Dovi Poznanski, Eran Ofek, Prof. Dan Maoz SN Rates Florence Horesh et al (arXiv: )

Outline  SDSS-I Overview  SN Survey pipeline  SN candidate sample  Type Ia SN rate result  Summary

SDSS-I Overview  Imaged over 8000 Sq. degrees  Five bands: u, g, r, i, z  Limiting magnitudes of mag.  Photometric catalog of >2x10^8 objects.  Spectroscopic catalog of >7x10^5 galaxies, ~2x10^5 Quasars, ~2^10^5 stars. 

SN survey pipeline  Download overlapping image set  Image registration : WCS, cross-correlation, ZP, background and PSF matching. WCS, cross-correlation, ZP, background and PSF matching.

SN survey pipeline  Download overlapping image set  Image registration : WCS, cross-correlation, ZP, background and PSF matching. WCS, cross-correlation, ZP, background and PSF matching.  SN candidate detection in g and r bands Stars ? Asteroids ? Stars ? Asteroids ?

SN survey pipeline  Download overlapping image set  Image registration : WCS, cross-correlation, ZP, background and PSF matching. WCS, cross-correlation, ZP, background and PSF matching.  SN candidate detection in g and r bands Stars ? Asteroids ? Poor alignment and PSF matching ? Stars ? Asteroids ? Poor alignment and PSF matching ?

SN survey pipeline  Download overlapping image set  Image registration : WCS, cross-correlation, ZP, background and PSF matching. WCS, cross-correlation, ZP, background and PSF matching.  SN candidate detection in g and r bands Stars ? Asteroids ? Poor alignment and PSF matching ? Stars ? Asteroids ? Poor alignment and PSF matching ?  Visual inspection of candidates

SN survey pipeline  Download overlapping image set  Image registration : WCS, cross-correlation, ZP, background and PSF matching. WCS, cross-correlation, ZP, background and PSF matching.  SN candidate detection in g and r bands Stars ? Asteroids ? Poor alignment and PSF matching ? Stars ? Asteroids ? Poor alignment and PSF matching ?  Visual inspection of candidates  Aperture Photometry of final SN sample

SN survey pipeline

SN candidates

 92 Sq. degrees  47 Candidates  7 candidates with redshift > 0.35  11 hostless candidates (9 expected) Final sample: 29 SN candidates

SN classification Broad-Band SN Colors (Poznanski et al. 2002)

SN classification SN Automatic Bayesian Classifier (SNABC; Poznanski, Maoz, Gal-Yam 2007): u, g, r, i, z magnitudes, Host redshift (prior) ↓ P(Ia) P(Ia) ↓ P(Ia)>0.9 P(Ia)>0.9 ↓ Type Ia Type Ia

SN classification Out of 29 SN candidate: Out of 29 SN candidate: 16 classified as Type Ia After debias : After debias :

Type Ia SN rate Traditional rate in SNu units ( ) Control Time : Luminosity density based on Blanton et al. (2003) SDSS luminosity function.

Type Ia SN rate r magnitude Detection Efficiency

Type Ia SN rate Rates in SNu : Conversion to volumetric rates:

Type Ia SN rate

Type Ia SN Rate Botticella et al. 2008

Type Ia SN Rate

Summary  17 Type Ia SN in 92 Sq. degrees  Rate of  Volumetric rates derived from luminosity normalized rates are sensitive to the luminosity function used.  SDSS-I data can be used for SNe study at low cost.  Full SDSS-I data will consist of ~500 SNe

Type Ia SN rate j(z) = ( × z) 10^8 L Mpc−3. Botticella et al. 2008

SN classification  Classification of artificial SN samples of different types (Ia, Ib/c IIP IIn) with various Ia fractions.  Using P(Ia)>0.9 results in 85% of Ia 4% of IIP 28% IIn 13% Ib/c