Stellar Spectral Classification: I. Application of Component Analysis

Deeming, Terence J.

doi:10.1093/mnras/127.6.493

Cited by 54 publications

(30 citation statements)

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“…PCA has been extensively used in astronomy as a multivariale analysis tool starting from the work of Deeming (1964) on the classification of stellar spectra. Its use for the study of molecular maps of the ISM is more recent, starting with the work of Ungerechts & Thaddeus (1987).…”

Section: Comparison With Other Workmentioning

confidence: 99%

Dissecting the molecular structure of the Orion B cloud: insight from principal component analysis

Gratier

Bron

Gérin

et al. 2017

A&A

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Context. The combination of wideband receivers and spectrometers currently available in (sub-)millimeter observatories deliver widefield hyperspectral imaging of the interstellar medium. Tens of spectral lines can be observed over degree wide fields in about 50 h. This wealth of data calls for restating the physical questions about the interstellar medium in statistical terms. Aims. We aim to gain information on the physical structure of the interstellar medium from a statistical analysis of many lines from different species over a large field of view, without requiring detailed radiative transfer or astrochemical modeling. Methods. We coupled a non-linear rescaling of the data with one of the simplest multivariate analysis methods, namely the principal component analysis, to decompose the observed signal into components that we interpret first qualitatively and then quantitatively based on our deep knowledge of the observed region and of the astrochemistry at play. Results. We identify three principal components, linear compositions of line brightness temperatures, that are correlated at various levels with the column density, the volume density and the UV radiation field. Conclusions. When sampling a sufficiently diverse mixture of physical parameters, it is possible to decompose the molecular emission in order to gain physical insight on the observed interstellar medium. This opens a new avenue for future studies of the interstellar medium.

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Section: Comparison With Other Workmentioning

confidence: 99%

Dissecting the molecular structure of the Orion B cloud: insight from principal component analysis

Gratier

Bron

Gérin

et al. 2017

A&A

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“…2, we briefly describe the actual content of this database 1 . The PCA has been used for stellar spectral classification since Deeming (1964). It has been in use since, and more recently for the purpose of inverting stellar fundamental parameters from the analysis of spectra of various resolutions.…”

Section: Introductionmentioning

confidence: 99%

Inversion of stellar fundamental parameters from ESPaDOnS and Narval high-resolution spectra

Paletou

Böhm

Watson

et al. 2014

A&A

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The general context of this study is the inversion of stellar fundamental parameters from high-resolution Echelle spectra. We aim at developing a fast and reliable tool for the post-processing of spectra produced by ESPaDOnS and Narval spectropolarimeters. Our inversion tool relies on principal component analysis. It allows reducing dimensionality and defining a specific metric for the search of nearest neighbours between an observed spectrum and a set of observed spectra taken from the Elodie stellar library. Effective temperature, surface gravity, total metallicity, and projected rotational velocity are derived. Various tests presented in this study that were based solely on information coming from a spectral band centred on the Mg  b-triplet and had spectra from FGK stars are very promising.

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“…The advantage of this modification is that the use of a large basis of input spectra ensures a closer match to the true underlying stellar population. Recently, Hao & Strauss (2002) applied principal component analysis (PCA; Deeming 1964) to several hundred pure absorption-line galaxies and used the first few eigenspectra as the templates. This makes the size of the template library much smaller because the most prominent features from the sample concentrate into the first few eigenspectra.…”

Section: Introductionmentioning

confidence: 99%

Empirical Modeling of the Stellar Spectrum of Galaxies

Li¹,

Wang²,

Zhou³

et al. 2005

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An empirical method of modeling the stellar spectrum of galaxies is proposed, based on two successive applications of principal component analysis (PCA). PCA is first applied to the newly available stellar library STELIB, supplemented by the J, H, and K s magnitudes taken mainly from the Two Micron All Sky Survey. Next, the resulting eigenspectra are used to fit the observed spectra of a sample of 1016 galaxies selected from the Sloan Digital Sky Survey Data Release 1 (SDSS DR1). PCA is again applied to the fitted spectra to construct the eigenspectra of galaxies with zero velocity dispersion. The first nine galactic eigenspectra so obtained are then used to model the stellar spectrum of the galaxies in SDSS DR1 and synchronously to estimate the stellar velocity dispersion, the spectral type, the near-infrared spectral energy distribution, and the average reddening. Extensive tests show that the spectra of different types of galaxies can be modeled quite accurately using these eigenspectra. The method can yield stellar velocity dispersion with accuracies better than 10% for the spectra of typical signalto-noise ratios in SDSS DR1.

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Stellar Spectral Classification: I. Application of Component Analysis

Cited by 54 publications

References 0 publications

Dissecting the molecular structure of the Orion B cloud: insight from principal component analysis

Dissecting the molecular structure of the Orion B cloud: insight from principal component analysis

Inversion of stellar fundamental parameters from ESPaDOnS and Narval high-resolution spectra

Empirical Modeling of the Stellar Spectrum of Galaxies

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