Related papers: SDSS-IV MaStar: Data-driven Parameter Derivation f…
Using the integral field unit (IFU) data from Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, we collect a sample of 36 star forming galaxies that host galactic-scale outflows in ionized gas phase. The control sample is…
The X-shooter Spectral Library (XSL) is a large empirical stellar library used as a benchmark for the development of stellar population models. The inclusion of $\alpha$-elements abundances is crucial to disentangling the chemical evolution…
Identification of metal-poor stars among field stars is extremely useful for studying the structure and evolution of the Galaxy and of external galaxies. We search for metal-poor stars using the artificial neural network (ANN) and extend…
The goal of this paper is to describe the science verification of Milky Way Mapper (MWM) APOGEE Stellar Parameter and Chemical Abundances Pipeline (ASPCAP) data products published in Data Release 19 (DR19) of the fifth phase of the Sloan…
In this paper, we developed a spectral emulator based on the Mapping Nearby Galaxies at Apache Point Observatory Stellar Library (MaStar) and a grouping optimization strategy to estimate effective temperature (T_eff), surface gravity (log…
We present precise photometric estimates of stellar parameters, including effective temperature, metallicity, luminosity classification, distance, and stellar age, for nearly 26 million stars using the methodology developed in the first…
The detection and subsequent characterisation of exoplanets are intimately linked to the characteristics of their host star. Therefore, it is necessary to study the star in detail in order to understand the formation history and…
Three basic ingredients are required to generate a simple stellar population (SSP) library, i.e., an initial mass function (IMF), a stellar evolution model/isochrones, and an empirical/theoretical stellar spectral library. However, there…
We use the statistical power of the MaNGA integral-field spectroscopic galaxy survey to improve the definition of strong line diagnostic boundaries used to classify gas ionization properties in galaxies. We detect line emission from 3.6…
We present a library of 1654 high-resolution stellar spectra, with a sampling of 0.3 A and covering the wavelength range from 3000 A ~ to 7000 A The library was computed with the latest improvements in stellar atmospheres, incorporating…
A new stellar library developed for stellar population synthesis modeling is presented. The library consist of 985 stars spanning a large range in atmospheric parameters. The spectra were obtained at the 2.5m INT telescope and cover the…
Deriving stellar atmospheric parameters and chemical abundances from stellar spectra is crucial for understanding the evolution of the Milky Way. By performing a fitting with MARCS model atmospheric theoretical synthetic spectra combined…
We present a method of fitting optical spectra of galaxies using a basis set of six vectors obtained from principal component analysis (PCA) of a library of synthetic spectra of 40000 star formation histories (SFHs). Using this library, we…
MEGARA is the optical integral field and multi-object spectrograph at the Gran Telescopio Canarias. We have created MEGASTAR, an empirical library of stellar spectra obtained using MEGARA at high resolution $R=20\,000$ (FWHM), available in…
We present a catalog of fundamental stellar properties for 7,673 evolved stars, including stellar radii and masses, determined from the combination of spectroscopic observations from the Apache Point Observatory Galactic Evolution…
We present here the analysis performed using the pyPipe3D pipeline for the final MaNGA dataset included in the SDSS seventeenth data-release. This dataset comprises more than 10,000 individual datacubes, being the integral field…
Chemical abundances are key tracers of the cycle of baryons driving the evolution of galaxies. Most measurements of interstellar medium (ISM) abundance and metallicity gradients in galaxies are based, however, on model-dependent strong-line…
Spectroscopic surveys require fast and efficient analysis methods to maximize their scientific impact. Here we apply a deep neural network architecture to analyze both SDSS-III APOGEE DR13 and synthetic stellar spectra. When our…
A machine-learning-based method is developed to identify objects with unusual stellar spectra. The method employs an autoencoder, a neural network trained to compress spectral data into a low-dimensional representation and subsequently…
We present a database of 45,000 atmospheric models (which will become 80,000 models by the end of the project) with stellar masses between 9 and 120 M$_{\odot}$, covering the region of the OB main sequence and W-R stars in the H-R diagram.…