The study of Type Ia supernovae spectral diversity using principal component analysis
Identifieur interne : 002078 ( PascalFrancis/Corpus ); précédent : 002077; suivant : 002079The study of Type Ia supernovae spectral diversity using principal component analysis
Auteurs : Diane Cormier ; Tamara M. DavisSource :
- Monthly Notices of the Royal Astronomical Society [ 0035-8711 ] ; 2011.
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- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
In order to use supernovae (SNe) as cosmological probes, a good understanding of their properties and diversity is necessary. Here we investigate whether principal component analysis (PCA) can be used to improve the calibration of Type Ia SNe. We apply PCA to two different cases: a small data set of supernova spectra taken at maximum light and a larger data set with more spectra taken at various epochs. On the SN Ia luminosity scale, the supernova SN 1991T appears at the upper end and SN 1991bg at the lower end. While 91bg-like SNe seem to form a distinct group, 91T-like SNe show a continuum of properties with normal SNe. The differences are mainly explained by line shifts in the spectra. However, we do not find that PCA is able to distinguish trends or subsets in the supernova data beyond what has already been found using specific spectral features. The main utility of PCA will be as a tool for characterizing large sets of spectra. We show how the information in a data base of supernova spectra can be vastly simplified using PCA. This can be used to make a continuum of spectral templates from a discrete library of spectra, which may be useful in k-corrections and the training of supernova light-curve fitters.
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NO : | PASCAL 11-0058001 INIST |
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ET : | The study of Type Ia supernovae spectral diversity using principal component analysis |
AU : | CORMIER (Diane); DAVIS (Tamara M.) |
AF : | Department of Physics, University of Queensland/QLD 4072/Australie (1 aut., 2 aut.); Service d'Astrophysique, CEA Saclay/91191 Gif-sur-Yvette/France (1 aut.); Dark Cosmology Centre, Niels Bohr Institute, University of Copenhagen/Danemark (2 aut.) |
DT : | Publication en série; Niveau analytique |
SO : | Monthly Notices of the Royal Astronomical Society; ISSN 0035-8711; Coden MNRAA4; Etats-Unis; Da. 2011; Vol. 410; No. 4; Pp. 2137-2148; Bibl. 1/2 p. |
LA : | Anglais |
EA : | In order to use supernovae (SNe) as cosmological probes, a good understanding of their properties and diversity is necessary. Here we investigate whether principal component analysis (PCA) can be used to improve the calibration of Type Ia SNe. We apply PCA to two different cases: a small data set of supernova spectra taken at maximum light and a larger data set with more spectra taken at various epochs. On the SN Ia luminosity scale, the supernova SN 1991T appears at the upper end and SN 1991bg at the lower end. While 91bg-like SNe seem to form a distinct group, 91T-like SNe show a continuum of properties with normal SNe. The differences are mainly explained by line shifts in the spectra. However, we do not find that PCA is able to distinguish trends or subsets in the supernova data beyond what has already been found using specific spectral features. The main utility of PCA will be as a tool for characterizing large sets of spectra. We show how the information in a data base of supernova spectra can be vastly simplified using PCA. This can be used to make a continuum of spectral templates from a discrete library of spectra, which may be useful in k-corrections and the training of supernova light-curve fitters. |
CC : | 001E03 |
FD : | Supernova type I; Analyse composante principale; Luminosité; Continuum; Spectre discret; Correction; Courbe lumière; Analyse donnée; Méthode statistique |
ED : | Type I supernova; Principal component analysis; Luminosity; Continuum; Discrete spectrum; Corrections; Light curves; Data analysis; Statistical method |
SD : | Supernova tipo I; Continuo; Espectro discreto; Método estadístico |
LO : | INIST-2067.354000191847050030 |
ID : | 11-0058001 |
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Pascal:11-0058001Le document en format XML
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<front><div type="abstract" xml:lang="en">In order to use supernovae (SNe) as cosmological probes, a good understanding of their properties and diversity is necessary. Here we investigate whether principal component analysis (PCA) can be used to improve the calibration of Type Ia SNe. We apply PCA to two different cases: a small data set of supernova spectra taken at maximum light and a larger data set with more spectra taken at various epochs. On the SN Ia luminosity scale, the supernova SN 1991T appears at the upper end and SN 1991bg at the lower end. While 91bg-like SNe seem to form a distinct group, 91T-like SNe show a continuum of properties with normal SNe. The differences are mainly explained by line shifts in the spectra. However, we do not find that PCA is able to distinguish trends or subsets in the supernova data beyond what has already been found using specific spectral features. The main utility of PCA will be as a tool for characterizing large sets of spectra. We show how the information in a data base of supernova spectra can be vastly simplified using PCA. This can be used to make a continuum of spectral templates from a discrete library of spectra, which may be useful in k-corrections and the training of supernova light-curve fitters.</div>
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<ET>The study of Type Ia supernovae spectral diversity using principal component analysis</ET>
<AU>CORMIER (Diane); DAVIS (Tamara M.)</AU>
<AF>Department of Physics, University of Queensland/QLD 4072/Australie (1 aut., 2 aut.); Service d'Astrophysique, CEA Saclay/91191 Gif-sur-Yvette/France (1 aut.); Dark Cosmology Centre, Niels Bohr Institute, University of Copenhagen/Danemark (2 aut.)</AF>
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<EA>In order to use supernovae (SNe) as cosmological probes, a good understanding of their properties and diversity is necessary. Here we investigate whether principal component analysis (PCA) can be used to improve the calibration of Type Ia SNe. We apply PCA to two different cases: a small data set of supernova spectra taken at maximum light and a larger data set with more spectra taken at various epochs. On the SN Ia luminosity scale, the supernova SN 1991T appears at the upper end and SN 1991bg at the lower end. While 91bg-like SNe seem to form a distinct group, 91T-like SNe show a continuum of properties with normal SNe. The differences are mainly explained by line shifts in the spectra. However, we do not find that PCA is able to distinguish trends or subsets in the supernova data beyond what has already been found using specific spectral features. The main utility of PCA will be as a tool for characterizing large sets of spectra. We show how the information in a data base of supernova spectra can be vastly simplified using PCA. This can be used to make a continuum of spectral templates from a discrete library of spectra, which may be useful in k-corrections and the training of supernova light-curve fitters.</EA>
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