Related papers: Identification of metal-poor stars using the artif…

We explore the application of artificial neural networks (ANNs) for the estimation of atmospheric parameters (Teff, logg, and [Fe/H]) for Galactic F- and G-type stars. The ANNs are fed with medium-resolution (~ 1-2 A) non flux-calibrated…

With the dual aims of enlarging the list of extremely metal-poor stars identified in the Galaxy, and boosting the numbers of moderately metal-deficient stars in directions that sample the rotational properties of the thick disk, we have…

In order to find a fast and reliable method for selecting metal poor galaxies (MPGs), especially in large surveys and huge database, an Artificial Neural Network (ANN) method is applied to a sample of star-forming galaxies from the Sloan…

New generation large-aperture telescopes, multi-object spectrographs, and large format detectors are making it possible to acquire very large samples of stellar spectra rapidly. In this context, traditional star-by-star spectroscopic…

Stellar metallicity strongly correlates with the presence of planets and their properties. To check for new correlations between stars and the existence of an orbiting planet, we determine precise stellar parameters for a sample of…

We report the discovery of 34 stars in the Hamburg/ESO Survey for metal-poor stars and the Sloan Digital Sky Survey that have [Fe/H] < -3.0. Their median and minimum abundances are [Fe/H] = -3.1 and -4.1, respectively, while 10 stars have…

We have undertaken a spectroscopic survey of field stars to find metal-poor objects among them. Observations are carried out using OMR spectrograph attached to 2.3m telescope at VBO Kavalur, India. The sample of candidate stars are chosen…

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 chemical abundances of large samples of extremely metal-poor (EMP) stars can be used to investigate metal-free stellar populations, supernovae, and nucleosynthesis as well as the formation and galactic chemical evolution of the Milky…

The estimation of stellar atmospheric parameters for large-scale samples, particularly metal-poor stars, is a cornerstone of Galactic archaeology. In this work, we optimized a photometric filter design tailored to measuring stellar…

Determining the physical parameters of pulsating variable stars such as RR Lyrae is essential for understanding their internal structure, pulsation mechanisms, and evolutionary state. In this study, we present a machine learning framework…

We present a technique which employs artificial neural networks to produce physical parameters for stellar spectra. A neural network is trained on a set of synthetic optical stellar spectra to give physical parameters (e.g. T_eff, log g,…

Very metal-poor (VMP, [Fe/H]<-2.0) stars offer a wealth of information on the nature and evolution of elemental production in the early galaxy and universe. The upcoming China Space Station Telescope (CSST) will provide us with a large…

Accurate determination of stellar atmospheric parameters and elemental abundances is crucial for Galactic archeology via large-scale spectroscopic surveys. In this paper, we estimate stellar atmospheric parameters -- effective temperature…

We present a novel analysis of the metal-poor star sample in the complete Radial Velocity Experiment (RAVE) Data Release 5 catalog with the goal of identifying and characterizing all very metal-poor stars observed by the survey. Using a…

In the search for metal-poor stars, large spectroscopic surveys are an invaluable tool. However, the spectra of metal-poor stars can be difficult to analyse because of the relative lack of available lines, which can also lead to…

Deriving metallicities for solar-like stars follows well-established methods, but for cooler stars such as M dwarfs, the determination is much more complicated due to forests of molecular lines that are present. Several methods have been…

The artificial neural network (ANN) is a well-established mathematical technique for data prediction, based on the identification of correlations and pattern recognition in input training sets. We present the application of ANNs to predict…

Large scale, deep survey missions such as GAIA will collect enormous amounts of data on a significant fraction of the stellar content of our Galaxy. These missions will require a careful optimisation of their observational systems in order…

Representative samples of F-, G-, K-type stars located out of the Solar Neighbourhood has started to be available in spectroscopic surveys. The fraction of metal-poor ([Fe/H]~$\lesssim -0.8$~dex) giants becomes increasingly relevant to far…