Related papers: Stellar Spectral Interpolation using Machine Learn…
Stellar population synthesis is an important method in the galaxy and star-cluster studies. In the stellar population synthesis models, stellar spectral library is necessary for the integrated spectra of the stellar population. Usually, the…
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. Empirical libraries of stellar spectra are used for stellar classification and synthesis of stellar populations. MILES is a medium spectral-resolution library in the optical domain covering a wide range of temperatures, surface…
The growth of sky surveys and the large amount of stellar spectra in the current databases, has generated the necessity of developing new methods to estimate atmospheric parameters, a fundamental task on stellar research. In this work we…
The integrated spectra of stellar systems contain a wealth of information, and its analysis can reveal fundamental parameters such as metallicity, age and star formation history. Widely used methods to analyze these spectra are based on…
Multispectral and hyperspectral images are increasingly popular in different research fields, such as remote sensing, astronomical imaging, or precision agriculture. However, the amount of free data available to perform machine learning…
We present SM-Net, a machine-learning model that learns a continuous spectral manifold from multiple high-resolution stellar libraries. SM-Net generates stellar spectra directly from the fundamental stellar parameters effective temperature…
We present a new stellar atmosphere interpolator which we will use to compute stellar population models based on empirical and/or synthetic spectra. We combined observed and synthetic stellar spectra in order to achieve more or less uniform…
Libraries of stellar spectra, such as ELODIE (Prugniel & Soubiran 2001), CFLIB (Valdes et al. 2004), or MILES (S\'anchez-Bl\'azquez et al. 2006), are used for a variety of applications, and especially in modelling stellar populations (e. g.…
We present tests carried out on optical and infrared stellar spectra to evaluate the accuracy of different types of interpolation. Both model atmospheres and continuum normalized fluxes were interpolated. In the first case we used linear…
We applied machine learning to the entire data history of ESO's High Accuracy Radial Velocity Planet Searcher (HARPS) instrument. Our primary goal was to recover the physical properties of the observed objects, with a secondary emphasis on…
In this work, we present Stellar Spectra Factory (SSF), a tool to generate empirical-based stellar spectra from arbitrary stellar atmospheric parameters. The relative flux-calibrated empirical spectra can be predicted by SSF given arbitrary…
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…
Accurate model stellar fluxes are key for the analysis of observations of individual stars or stellar populations. Model spectra differ from real stellar spectra due to limitations of the input physical data and adopted simplifications, but…
The use of 3D hydrodynamical simulations of stellar surface convection for model atmospheres is computationally expensive. Although these models have been available for quite some time, their use is limited because of the lack of extensive…
Machine learning has been widely applied to clearly defined problems of astronomy and astrophysics. However, deep learning and its conceptual differences to classical machine learning have been largely overlooked in these fields. The broad…
Stellar population synthesis (SPS) models are invaluable to study star clusters and galaxies. They provide means to extract stellar masses, stellar ages, star formation histories, chemical enrichment and dust content of galaxies from their…
Models of stellar spectra are necessary for interpreting light from individual stars, planets, integrated stellar populations, nebulae, and the interstellar medium. We provide a comprehensive and homogeneous collection of synthetic spectra…
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…
Given the widespread availability of grids of models for stellar atmospheres, it is necessary to recover intermediate atmospheric models by means of accurate techniques that go beyond simple linear interpolation and capture the intricacies…