相关论文: Spectral identification and classification of dust…

Dusty stellar point sources are a significant stage in stellar evolution and contribute to the metal enrichment of galaxies. These objects can be classified using photometric and spectroscopic observations with color-magnitude diagrams…

The Magellanic clouds are uniquely placed to study the stellar contribution to dust emission. Individual stars can be resolved in these systems even in the mid-infrared, and they are close enough to allow detection of infrared excess caused…

The Infrared Spectrograph (IRS) on the {\em Spitzer Space Telescope} observed nearly 800 point sources in the Large Magellanic Cloud (LMC), taking over 1,000 spectra. 197 of these targets were observed as part of the Sage-Spec Spitzer…

The Magellanic Clouds (MCs) are excellent locations to study stellar dust emission and its contribution to galaxy evolution. Through spectral and photometric classification, MCs can serve as a unique environment for studying stellar…

Dusty stellar sources, including young stellar objects (YSOs) and evolved stars such as oxygen- and carbon-rich AGBs (OAGBs, CAGBs), red supergiants (RSGs), and post-AGB stars (PAGBs), play a key role in the chemical enrichment of galaxies.…

Mass loss is a key aspect of stellar evolution, particularly in evolved massive stars, yet episodic mass loss remains poorly understood. To investigate this, we need evolved massive stellar populations across various galactic environments.…

We present the classification of 197 point sources observed with the Infrared Spectrograph in the SAGE-Spec Legacy program on the Spitzer Space Telescope. We introduce a decision-tree method of object classification based on infrared…

Information on the spectral types of stars is of great interest in view of the exploitation of space-based imaging surveys. In this article, we investigate the classification of stars into spectral types using only the shape of their…

Mass loss is a key property to understand stellar evolution and in particular for low-metallicity environments. Our knowledge has improved dramatically over the last decades both for single and binary evolutionary models. However, episodic…

The Infrared Space Observatory observed over 900 objects with the Short Wavelength Spectrometer in full-grating-scan mode (2.4-45.2 micron). We have developed a comprehensive system of spectral classification using these data. Sources are…

Classification of young stellar objects (YSOs) into different evolutionary stages helps us to understand the formation process of new stars and planetary systems. Such classification has traditionally been based on spectral energy…

Accurate measurements of statistical properties, such as the star formation rate and the lifetime of young stellar objects (YSOs) in different stages, is essential for constraining star formation theories. However, it is a difficult task to…

Due to the ever-expanding volume of observed spectroscopic data from surveys such as SDSS and LAMOST, it has become important to apply artificial intelligence (AI) techniques for analysing stellar spectra to solve spectral classification…

(abridged) Mass loss is a key parameter in the evolution of massive stars, with discrepancies between theory and observations and with unknown importance of the episodic mass loss. To address this we need increased numbers of classified…

Aims: We study the optical spectral properties of a sample of stars showing far infrared colours similar to those of well-known planetary nebulae. The large majority of them were unidentified sources or poorly known in the literature at the…

We have produced an atlas of Spitzer Infrared Spectrograph (IRS) spectra of mass-losing, evolved stars in the Large Magellanic Cloud. These stars were selected to have high mass-loss rates and so contribute significantly to the return of…

We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this…

The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning…

The spectral energy distribution (SED) of observed stars in wide-field images is crucial for chromatic point spread function (PSF) modelling methods, which use unresolved stars as integrated spectral samples of the PSF across the field of…

In the coming years, next-generation space-based infrared observatories will significantly increase our samples of rare massive stars, representing a tremendous opportunity to leverage modern statistical tools and methods to test massive…