spectroscopic diversity of SNe Ia in the near-infrared
description
Transcript of spectroscopic diversity of SNe Ia in the near-infrared
spectroscopic diversity of SNe Ia in the near-infrared
Eric Y. HsiaoUniversity of Victoria
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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
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SNe Ia in the near-infraredlow redshift
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SNe Ia in the near-infraredlow redshift
Krisciunas et al. 2004 ApJ, 602, 81Eric Y. Hsiao
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SNe Ia in the near-infraredhigh redshift
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SNe Ia in the near-infraredhigh redshift
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Nobili et al. 2005 A&A, 437, 789
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SNe Ia in the near-infraredk-corrections
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near-infraredlibrary spectra
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near-infraredlibrary spectra
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Hamuy et al. 2002 AJ, 124, 417 (1999ee) Krisciunas et al. 2007 AJ, 133, 58 (2004S) Marion et al. 2003 ApJ, 591, 316 Marion et al. 2009 AJ, submitted
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near-infraredlibrary spectra
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near-infraredlibrary spectra
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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
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Hsiao et al. 2007 ApJ, 663, 1187
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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
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Hsiao et al. 2007 ApJ, 663, 1187
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spectral templatemeasurement of spectral features
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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
Eric Y. Hsiao
Hsiao et al. 2007 ApJ, 663, 1187
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spectral templateweighting functions
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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
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Hsiao et al. 2007 ApJ, 663, 1187
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spectral templatemangling function
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spectral templatebase spectral template
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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
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near-infraredtemporal evolution
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near-infraredtemporal evolution
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near-infraredtemporal evolution
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near-infraredspectral template
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near-infraredspectral template
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near-infraredspectral template
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near-infraredk-corrections
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near-infrareddependence on stretch
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near-infrareddependence on stretch (pre-maximum)
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I-bandlibrary spectra
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I-bandlibrary spectra
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I-bandspectral template
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I-bandk-corrections
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CSP
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I-bandk-corrections
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CSP
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I-bandtemporal evolution
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I-bandstretch template epoch or not?
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I-bandstretch template epoch or not?
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I-bandstretch template epoch or not?
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I-bandstretch template epoch or not?
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I-bandstretch template epoch or not?
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I-bandstretch template epoch or not?
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I-banddependence on stretch
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I-banddependence on stretch (primary maximum)
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I-banddependence on stretch (primary maximum)
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I-banddependence on stretch (primary maximum)
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I-banddependence on stretch (primary maximum)
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1991T
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I-banddependence on stretch (primary maximum)
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2000cx
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I-banddependence on stretch (primary maximum)
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1999aa
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I-banddependence on stretch (secondary maximum)
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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 errorshigh 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
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