相关论文: Applied Machine-Learning Models to Identify Spectr…

Low-mass stars and brown dwarfs -- spectral types (SpTs) M0 and later -- play a significant role in studying stellar and substellar processes and demographics, reaching down to planetary-mass objects. Currently, the classification of these…

M dwarfs are the most abundant stars in the Galaxy and serve as key targets for stellar and exoplanetary studies. It is particularly challenging to determine their metallicities because their spectra are complex. For this reason, several…

The study of machine learning (ML) techniques for the autonomous classification of astrophysical sources is of great interest, and we explore its applications in the context of a multifrequency data-frame. We test the use of supervised ML…

We present the optical spectra of 338 nearby M dwarfs, and compute their spectral types, effective temperatures ($T_{\mathrm{eff}}$), and radii. Our spectra have been obtained using several optical spectrometers with spectral resolutions…

In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…

With tens to hundreds of spectra of white dwarfs being taken each night from multi-object spectroscopic surveys, automated spectral classification is essential as part of efficient data processing. In this study, we design a neural network…

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…

Context. Being the most numerous and oldest stars in the galaxy, M dwarfs are objects of great interest for exoplanet searches. The presence of molecules in their atmosphere complicates our understanding of their atmospheric properties. But…

An extensive sample of M, L and T dwarfs identified in the Sloan Digital Sky Survey (SDSS) has been compiled. The sample of 718 dwarfs includes 677 new objects (629 M dwarfs, 48 L dwarfs) together with 41 that have been previously…

We present the first rates of flares from M dwarf stars in both red optical and near infrared (NIR) filters. We have studied ~50,000 M dwarfs from the SDSS Stripe 82 area, and 1,321 M dwarfs from the 2MASS Calibration Scan Point Source…

M dwarfs are the most common type of star in the Galaxy, and because of their small size are favored targets for searches of Earth-sized transiting exoplanets. Current and upcoming all-sky spectroscopic surveys, such as the Large Sky Area…

M dwarfs are the dominating type of stars in the solar neighbourhood. They serve as excellent tracers for the study of the distribution and properties of the nearby interstellar dust. In this work, we aim to obtain high accuracy reddening…

We define an enhanced spectral classification scheme for M dwarf stars, and use it to derive spectral classification of 104 northern stars with proper motions larger than 0.5"/yr which we discovered in a survey of high proper motion stars…

Using a machine learning (ML) method, we mine DB white dwarfs (DBWDs) from the Sloan Digital Sky Survey (SDSS) Data Release (DR) 12 and DR14. The ML method consists of two parts: feature extraction and classification. The least absolute…

Based on a carefully constructed sample of dwarf stars, a new optical-near infrared photometric calibration to estimate the metallicity of late-type K and early-to-mid-type M dwarfs is presented. The calibration sample has two parts; the…

We present a spectroscopic catalog of the 1,564 brightest (J<9) M dwarf candidates in the northern sky, as selected from the SUPERBLINK proper motion catalog. Observations confirm 1,408 of the candidates to be late-K and M dwarfs with…

Context. M-type subdwarfs are metal-poor low-mass stars and probe for the old populations in our Galaxy. Accurate knowledge of their atmospheric parameters and especially the composition is essential for understanding the chemical history…

We describe a method for accurately determining M dwarf metallicities with spectral synthesis based on abundance analyses of visual binary stars. We obtained high resolution, high signal-to-noise spectra of each component of five visual…

We design a convolutional neural network (CNN) incorporating channel attention and spatial attention mechanisms to predict atmospheric parameters of hot subdwarfs. The experimental dataset comprises spectra at nine distinct signal-to-noise…

We present metallicities, radial velocities and near-infrared spectral types for 447 M dwarfs determined from moderate resolution (R~2000) near-infrared (NIR) spectra obtained with IRTF/SpeX. These M dwarfs are targets of the MEarth Survey,…