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SNLS : Cosmological results from the first year dataset

SNLS : Cosmological results from the first year dataset. Dominique Fouchez, CPPM Marseille On behalf of the SNLS collaboration. Rencontres de Monriond 2006. Plan of the talk. Deep homogenous and complete SNIa observation Detection Photometry : deep and well sample SN lightcurves

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SNLS : Cosmological results from the first year dataset

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  1. SNLS : Cosmological results from the first year dataset Dominique Fouchez, CPPM Marseille On behalf of the SNLS collaboration. Rencontres de Monriond 2006

  2. Plan of the talk • Deep homogenous and complete SNIa observation • Detection • Photometry : deep and well sample SN lightcurves • Spectrocopy -> identification of eachSN • Precise Measurement • Differential photometry • Calibration • Hubble diagram and cosmology • Lightcurve modelling , fitting and luminosity distance • Hubble diagram and cosmological parameter fitting • Full cosmological parameter error estimation

  3. Deep homogenous and complete SNIa observations • Detection • Photometry • Spectroscopy

  4. Photometry CFHT : 3.6 meters at Hawaii Megacam imager of 1 sq degree Observation of the 4 CFHTLS/DEEP 1d°2 fields in 4 colors

  5. Detection • Principle of detection : same field observed every 4 days • Subtraction of each night (stacked images) on a reference image to find individual detections then construction of Lightcurve of candidates - = days

  6. Photometry :deep and well sampled lightcurves • Rolling search method : same field every 4 days = automatic follow up (and down!)

  7. Real Time Detection • Need a spectroscopy a maximum of luminosity : quasi-real time detection • 2 pipelines detect new candidatess in 24h hours time after observation • Scanning-free-automatized detection pipeline has been set up (shapelet-neural net based detection)

  8. Spectroscopy • Spectrocopy : 8m - class • VLT + FORS1 • Gemini + GMOS • Others : KECK + LRIS 250H/year of spectro 250H/year of photo

  9. The SNIa sample : Status March 2006 : 2.5 years of running 440 spectra analysed in quasi real time 231 Ia identified with spectroscopy, ready for cosmology

  10. Digression • In addition to SNIa sample, many variable objects, which include many SN non IA . • This big sample make it possible to study other science topics than cosmological parameters within the SNLS photometric+spectroscopic data They are numerous …

  11. Other SNLS topics

  12. Precise Measurement of SNIa sample Differential photometry Calibration • Only the first year of data has been released and will be presented in the rest of the talk • 91 Sne Ia 10 miss references • 6 only have 1 band 73 usable SNIa events • 2 peculiars

  13. Differential photometry: Method Input images + variable seeing kernel (wrt best) -> Fit galaxy on a stamp Fit position (same in all images) Fit flux on each exposure

  14. Differential Photometry: Results • Photometry close to optimal (only 15% worse than photon statistics) • Lightcurve points : flux with errors (full covariance matrix)

  15. Photometric calibration • Same Differential photometry is used to measure tertiary standard stars in each field • Calibration on observed Landolt stars • 1% zero point dispersion (except z’)

  16. Hubble diagram and cosmology • Lightcurve modelling , fitting and luminosity distance • Hubble diagram and cosmological parameter fitting • Full cosmological parameter error estimation

  17. Lightcurve fitting with SALT method • 2 parameters describe the variability of SNIa lightcurves, but the luminosity at max : s and c (‘stretch and color’)

  18. Distance measurement • The fitted s and c are used to correct for the ‘brighter-slower’ and ‘brighter-bluer’ behaviour • The correction coefficients a,b and M have been fitted together with the cosmology on the whole lightcurve samples. • The distance is then derived from magnitude at max in B restframe and s,c :

  19. Hubble diagram • 44 low z + 71 SNLS Supernovae

  20. SNLS 2.5 year Hubble diagram : Coming soon ...

  21. Cosmological parameter fits BAO: Baryonic Acoustic Oscillation Eisentein 2005

  22. Systematic uncertainties • Possible sources : • Calibration and photometry • Evolution • Malmquist bias • Contamination • Gray dust • Lensing

  23. Calibration • 1% on zero points :

  24. Evolution: Comparison Low/High Z Stretch color • ? No sizeable evolution !

  25. Malmquist bias • ? Selection effect (keep brightest at high z) found on the data at same level as what expected from montecarlo Max of 0.05 deviation at z=1

  26. Summary of systematic uncertainties

  27. Final result Statistical error is still dominant …

  28. Perspectives • With full the statistics 700 SNIa expected (x10 !), the measurement will start to be ‘systematics limited’ • Improvement on main systematics errors is under study : • Calibration : z zero point ,in particular, can be improved but need other calibrators than Landolt -> join calibration campaign with CFHT to calibrate with spectrophoto standards the DEEP fields. • Detailled study of selection effects with huge Montecarlo dataset is underway. • Lightcurve modelling improvements. • …

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