AustralieFrV1, Istex, Corpus, bibRecord, 002989

The study of Type Ia supernovae spectral diversity using principal component analysis

Identifieur interne : 002989 ( Istex/Corpus ); précédent : 002988; suivant : 002990

The study of Type Ia supernovae spectral diversity using principal component analysis

Auteurs : Diane Cormier ; Tamara M. Davis

Source :

Monthly Notices of the Royal Astronomical Society [ 0035-8711 ] ; 2011-02-01.

RBID : ISTEX:DFF79945DD11753FB0EF110B2EF7BF0BD8DFC164

English descriptors

KwdEn :
- Absolute magnitude, Average spectrum, Benetti, Blondin, Blondin tonry, Brighter objects, Bullseye, Bullseye plot, Bullseye plots, Clear trend, Continuous catalogue, Continuous data base, Cumulative variance, Data base, Data points, Davis figure, Different colours, Different components, Different luminosities, Different types, Distinct group, Emission peaks, Expansion velocity, Extreme events, Flat data, Flat sample, Francis wills, Good indication, Input spectra, Journal compilation, Large data, Larger data, Light curve, Line shift, Line shifts, Linear relation, Luminosity, Maximum light, Mnras, Negative weight, Negative weights, Nite, Nite input data base, Nite number, Normal group, Normal type, Obvious trends, Original sample, Original spectrum, Peak luminosity, Peculiar type, Phase range, Photometric properties, Physical properties, Positive weight, Principal component, Principal component analysis, Principal component spectra, Principal components, Quasar spectra, Second component, Second weights, Shape data, Shape sample, Small data, Snid data base, Snid program, Solid line, Spectral, Spectral features, Spectral lines, Spectral observations, Spectral templates, Spectral weights, Spectrum, Standard candles, Standard deviation, Standard deviation outlier, Standard deviations, Supernova, Supernova spectra, Template spectra, Thick line, Underluminous, Unusual spectra, Velocity gradient, Velocity gradients, Wide range.
Teeft :
- Absolute magnitude, Average spectrum, Benetti, Blondin, Blondin tonry, Brighter objects, Bullseye, Bullseye plot, Bullseye plots, Clear trend, Continuous catalogue, Continuous data base, Cumulative variance, Data base, Data points, Davis figure, Different colours, Different components, Different luminosities, Different types, Distinct group, Emission peaks, Expansion velocity, Extreme events, Flat data, Flat sample, Francis wills, Good indication, Input spectra, Journal compilation, Large data, Larger data, Light curve, Line shift, Line shifts, Linear relation, Luminosity, Maximum light, Mnras, Negative weight, Negative weights, Nite, Nite input data base, Nite number, Normal group, Normal type, Obvious trends, Original sample, Original spectrum, Peak luminosity, Peculiar type, Phase range, Photometric properties, Physical properties, Positive weight, Principal component, Principal component analysis, Principal component spectra, Principal components, Quasar spectra, Second component, Second weights, Shape data, Shape sample, Small data, Snid data base, Snid program, Solid line, Spectral, Spectral features, Spectral lines, Spectral observations, Spectral templates, Spectral weights, Spectrum, Standard candles, Standard deviation, Standard deviation outlier, Standard deviations, Supernova, Supernova spectra, Template spectra, Thick line, Underluminous, Unusual spectra, Velocity gradient, Velocity gradients, Wide range.

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.

Url:

https://api.istex.fr/document/DFF79945DD11753FB0EF110B2EF7BF0BD8DFC164/fulltext/pdf

DOI: 10.1111/j.1365-2966.2010.17590.x

Links to Exploration step

ISTEX:DFF79945DD11753FB0EF110B2EF7BF0BD8DFC164

Le document en format XML

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<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.</abstract>
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The study of Type Ia supernovae spectral diversity using principal component analysis

The study of Type Ia supernovae spectral diversity using principal component analysis

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