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Eric Y. Hsiao University of Victoria. spectroscopic diversity of SNe Ia in the near-infrared. SNe Ia in the near-infrared why?. less dust extinction low redshift standard candles in JHK bands independent measure of Hubble constant high redshift rest frame I band Hubble diagram
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Eric Y. Hsiao University of Victoria spectroscopic diversity of SNe Ia in the near-infrared
SNe Ia in the near-infraredwhy? less dust extinction • low redshift • standard candles in JHK bands • independent measure of Hubble constant • high redshift • rest frame I band Hubble diagram • independent evidence of dark energy
SNe Ia in the near-infraredlow redshift Krisciunas et al. 2004 ApJ, 602, 81
SNe Ia in the near-infraredhigh redshift Nobili et al. 2005 A&A, 437, 789
near-infraredlibrary spectra • Hamuy et al. 2002 AJ, 124, 417 (1999ee) • Krisciunaset al. 2007 AJ, 133, 58 (2004S) • Marion et al. 2003 ApJ, 591, 316 • Marion et al. 2009 AJ, submitted
spectral templateprocedures • measure the feature strengths of the library spectra • determine the weighted mean feature strength at each epoch and wavelength • adjust the base spectral template to the weighted mean feature strengths Hsiao et al. 2007 ApJ, 663, 1187
spectral templatemeasurement of spectral features • measure the feature strengths of the library spectra • determine the weighted mean feature strength at each epoch and wavelength • adjust the base spectral template to the weighted mean feature strengths Hsiao et al. 2007 ApJ, 663, 1187
spectral templateweighting functions • measure the feature strengths of the library spectra • determine the weighted mean feature strength at each epoch and wavelength • adjust the base spectral template to the weighted mean feature strengths Hsiao et al. 2007 ApJ, 663, 1187
spectral templateprocedures • measure the feature strengths of the library spectra • determine the weighted mean feature strength at each epoch and wavelength • adjust the base spectral template to the weighted mean feature strengths Hsiao et al. 2007 ApJ, 663, 1187
principal component analysis (PCA) • PCA reduces multi-dimensional data into principal components • first component is the vector pointing toward the direction of the highest variance in the multi-dimensional data space • rest of the components are orthogonal vectors ranked by their variances
summary low redshift/JHK band • slow temporal evolution of spectral features k-correction errors small around NIR maximum • k-corrections using 1 SN can cause significant systematic errors high redshift/I band • epochs of the spectral template should be stretch corrected • there is an apparent spectroscopic sequence in the I-band OI and CaII triplet features