Related papers: Stellar Spectral Interpolation using Machine Learn…
A trade-off between speed and information controls our understanding of astronomical objects. Fast-to-acquire photometric observations provide global properties, while costly and time-consuming spectroscopic measurements enable a better…
Stellar clusters are regularly used to study the evolution of their host galaxy. Except for a few nearby galaxies, these studies rely on the interpretation of integrated cluster properties, especially integrated photometry observed using…
We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that…
We present model spectra of stellar populations with variable chemical composition. We derived the [alpha/Fe] abundance ratio of the stars of the most important libraries (ELODIE, CFLIB and MILES) using full spectrum fitting and we…
Stellar atmosphere modelling predicts the luminosity and temperature of a star, together with parameters such as the effective gravity and the metallicity, by reproducing the observed spectral energy distribution. Most observational data…
In this work, we select the high signal-to-noise ratio spectra of stars from the LAMOST data andmap theirMK classes to the spectral features. The equivalentwidths of the prominent spectral lines, playing the similar role as the multi-color…
An empirical method of modeling the stellar spectrum of galaxies is proposed, based on two successive applications of Principal Component Analysis (PCA). PCA is first applied to the newly available stellar library STELIB, supplemented by…
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…
Context. Clumping is a common property of stellar winds and is being incorporated to a solution of the radiative transfer equation coupled with kinetic equilibrium equations. However, in static hot model atmospheres, clumping and its…
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.…
We present a library of high-resolution (R $\equiv$ $\lambda$/$\Delta$$\lambda$ $\sim$ 45,000) and high signal-to-noise ratio (S/N $\geq$ 200) near-infrared spectra for stars of a wide range of spectral types and luminosity classes. The…
In the context of clustering, we assume a generative model where each cluster is the result of sampling points in the neighborhood of an embedded smooth surface; the sample may be contaminated with outliers, which are modeled as points…
This paper investigates the problem of prediction of stellar parameters, based on the star's electromagnetic spectrum. The knowledge of these parameters permits to infer on the evolutionary state of the star. From a statistical point of…
Different stellar populations may be identified through differences in chemical, kinematic, and chronological properties, suggesting the interplay of various physical mechanisms that led to their origin and subsequent evolution. As such,…
We present simple stellar population models based on the empirical X-shooter Spectral Library (XSL) from NUV to NIR wavelengths. The unmatched characteristics of relatively high resolution and extended wavelength coverage ($350-2480$ nm,…
Traditional spectral energy distribution (SED)-fitting methods for stellar mass estimation face persistent challenges including systematic biases and computational constraints. We present a controlled comparison of machine learning (ML) and…
Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar…
In this study, we develop an artificial neural network to estimate the infrared (IR) luminosity and star formation rates (SFR) of galaxies. Our network is trained using 'true' IR luminosity values derived from modeling the IR spectral…
The path towards robust near-infrared extensions of stellar population models involves the confrontation between empirical and synthetic stellar spectral libraries across the wavelength ranges of photospheric emission. [...] With its…
Spherical radial-basis-based kernel interpolation abounds in image sciences including geophysical image reconstruction, climate trends description and image rendering due to its excellent spatial localization property and perfect…