Related papers: FAMA: An automatic code for stellar parameter and …
Owing to the remarkable photometric precision of space observatories like Kepler, stellar and planetary systems beyond our own are now being characterized en masse for the first time. These characterizations are pivotal for endeavors such…
From a volume limited sample of 45,542 galaxies and 6,000 groups with $z \leq 0.213$ we use an adapted minimal spanning tree algorithm to identify and classify large scale structures within the Galaxy and Mass Assembly (GAMA) survey. Using…
Planetary nebulae are the products of the evolution of low and intermediate mass stars. The chemical property studies of these objects give important information about the elemental abundances as He, O, Ne, Ar, S and their modifications…
Aims. This paper introduces LRPayne, a novel algorithm designed for the efficient determination of stellar parameters and chemical abundances from low-resolution optical spectra, with a primary focus on data from large-scale galactic…
Spectra of late-type stars are usually analyzed with static model atmospheres in local thermodynamic equilibrium (LTE) and a homogeneous plane-parallel or spherically symmetric geometry. The energy balance requires particular attention, as…
By observing bright and compact astronomical sources while also taking data with the 183 GHz Water Vapour Radiometers, ALMA will be able to measure the `empirical' relationship between fluctuations in the phase of the astronomical signal…
Stellar physical and dynamical properties are essential knowledge to understanding the structure, formation, and evolution of our Galaxy. We produced an all-sky uniformly derived catalog of stellar astrophysical parameters (APs; age, mass,…
Context: Accurate fundamental parameters of stars are essential for the asteroseismic analysis of data from the NASA Kepler mission. Aims: We aim at determining accurate atmospheric parameters and the abundance pattern for a sample of 82…
We carried out a homogeneous abundance study for various elements, including $\alpha$-elements, iron peak elements and $n$-capture elements for 35 metal-poor stars with a wide metallicity range ($-3.0\lesssim$[Fe/H]$\lesssim-0.5$).…
Studies of Galactic chemical and dynamical evolution in the solar neighborhood depend on the availability of precise atmospheric parameters (Teff, [Fe/H] and log g) for solar-type stars. Many large-scale spectroscopic surveys operate at low…
Stellar spectra encode key information on the physical properties and chemical compositions of stars. Accurate stellar parameter determination is essential for addressing major questions such as galaxy and stellar evolution. Large-scale…
The source of hot gas in elliptical galaxies is thought to be due to stellar mass loss, with contributions from supernova events and possibly from infall from a surrounding environment. This picture predicts supersolar values for the…
Stars of a common origin are thought to have similar, if not nearly identical, chemistry. Chemical tagging seeks to exploit this fact to identify Milky Way subpopulations through their unique chemical fingerprints. In this work, we compare…
We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in…
The environment plays a key role in galaxy evolution, yet it remains unclear how detailed molecular gas properties and their connection to star formation and stellar content are influenced by both large-scale and local environments. Here we…
Large-scale spectroscopic surveys have collectively observed millions of stars across the Milky Way, but each derives stellar labels using independent pipelines with distinct modelling assumptions, introducing systematic offsets that…
We present an open-source Thermochemical Equilibrium Abundances (TEA) code that calculates the abundances of gaseous molecular species. The code is based on the methodology of White et al. (1958) and Eriksson (1971). It applies Gibbs…
As a typical data-driven method, deep learning becomes a natural choice for analysing astronomical data nowadays. In this study, we built a deep convolutional neural network to estimate basic stellar parameters $T\rm{_{eff}}$, log g,…
We analyze the X-ray spectra of 19 main sequence stars observed by Chandra using its LETGS configuration. Emission measure (EM) distributions are computed based on emission line measurements, an analysis that also yields evaluations of…
Due to the importance that the star-planet relation has to our understanding of the planet formation process, the precise determination of stellar parameters for the ever increasing number of discovered extra-solar planets is of great…