Related papers: Spectral Classification; Old and Contemporary
In this article we give an overview of the developments in the field of spectral classification and its continued importance in the fields of stellar and galactic evolution. The extension of MK system to cool stars as well as refined…
Spectral classification is the division of stars into classes based on their spectral characteristics. Different classification systems have existed since the 19th century but the term is used nowadays mostly to refer to the Morgan-Keenan…
Classification is valuable and necessary in spectral analysis, especially for data-driven mining. Along with the rapid development of spectral surveys, a variety of classification techniques have been successfully applied to astronomical…
In this work, we select the high signal-to-noise ratio spectra of stars from the LAMOST data andmap theirMK classes to the spectral features. The equivalentwidths of the prominent spectral lines, playing the similar role as the multi-color…
We develop a straightforward and quantitative two-step method for spectroscopically classifying galaxies from the low signal-to-noise (S/N) optical spectra typical of galaxy redshift surveys. First, using \chi^2-fitting of characteristic…
Stellar spectroscopic classification has been successfully automated by a number of groups. Automated classification and parameterization work best when applied to a homogeneous data set, and thus these techniques primarily have been…
With the availability of multi-object spectrometers and the designing \& running of some large scale sky surveys, we are obtaining massive spectra. Therefore, it becomes more and more important to deal with the massive spectral data…
Context: The technique of disentangling has been applied to numerous high-precision studies of spectroscopic binaries and multiple stars. Although, its possibilities have not yet been fully understood and exploited. Aims: Theoretical…
With the advent of large spectroscopic surveys, automated stellar parameter determination has become commonplace. Nevertheless, spectral classification still offers a quick and useful alternative for obtaining parameter estimates for large…
Spectroscopy is one of the most important tools that an astronomer has for studying the universe. This chapter begins by discussing the basics, including the different types of optical spectrographs, with extension to the ultraviolet and…
Analyses of stellar spectra often begin with the determination of a number of parameters that define a model atmosphere. This work presents a prototype for an automated spectral classification system that uses a 15 nm-wide region around…
The aim of this article is to demonstrate the useful role that can be played by spectropolarimetric observations of young and evolved emission line stars that analyse the linearly polarized component in their spectra. At the time of…
Equations arising in General Relativity are usually too complicated to be solved analytically and one has to rely on numerical methods to solve sets of coupled partial differential equations. Among the possible choices, this paper focuses…
These notes offer a unified introduction to spectral methods for the study of complex systems. They are intended as an operative manual rather than a theorem-proof textbook: the emphasis is on tools, identities, and perspectives that can be…
Along the life of the IUE project, a large archive with spectral data has been generated, requiring automated classification methods to be analyzed in an objective form. Previous automated classification methods used with IUE spectra were…
The fast classification of new variable stars is an important step in making them available for further research. Selection of science targets from large databases is much more efficient if they have been classified first. Defining the…
In recent years, deep learning techniques revolutionized the way remote sensing data are processed. Classification of hyperspectral data is no exception to the rule, but has intrinsic specificities which make application of deep learning…
Current and future large astronomical surveys will yield multiparameter databases on millions or even billions of objects. The scientific exploitation of these will require powerful, robust, and automated classification tools tailored to…
Spectral methods have emerged as a simple yet surprisingly effective approach for extracting information from massive, noisy and incomplete data. In a nutshell, spectral methods refer to a collection of algorithms built upon the eigenvalues…
In the last two decades about a dozen methods were invented which derive, from a series of composite spectra over the orbit, the spectra of individual components in binary and multiple systems. Reconstructed spectra can then be analyzed…