Related papers: Quantitative spectroscopy of single and multiple O…
We present a state-of-the-art analysis technique able to simultaneously reproduce the entire H and He spectra of OB-type stars in the visual and the near-IR and to derive highly accurate metal abundances (so far C and N). The spectrum…
Upcoming large-scale spectroscopic surveys such as WEAVE and 4MOST will provide thousands of spectra of massive stars, which need to be analysed in an efficient and homogeneous way. Studies on massive stars are usually based on samples of a…
Upcoming large-scale spectroscopic surveys with e.g. WEAVE and 4MOST will provide thousands of spectra of massive stars, which need to be analysed in an efficient and homogeneous way. Usually, studies of massive stars are limited to samples…
(abridged) We develop a tool for the automated spectral classification of OB stars according to their sub-types. We use the regular Random Forest (RF) algorithm, the Probabilistic RF (PRF), and we introduce the KDE-RF method which is a…
The IACOB and OWN surveys are two ambitious complementary observational projects which have made available a large multi-epoch spectroscopic database of optical high resolution spectra of Galactic massive O-type stars. As a first step in…
We describe a self-consistent spectrum analysis technique employing non-LTE line formation, which allows precise atmospheric parameters of massive stars to be derived: 1sigma-uncertainties as low as ~1% in effective temperature and…
Quantitative spectroscopy of luminous BA-type supergiants offers a high potential for modern astrophysics. The degree to which we can rely on quantitative studies of this class of stars as a whole depends on the quality of the analyses for…
In the past years we have made great efforts to reduce the statistical and systematic uncertainties in stellar parameter and chemical abundance determinations of early B-type stars. Both the construction of robust model atoms for non-LTE…
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…
Spectral analysis by means of NLTE model atmospheres has presently arrived at a high level of sophistication. High-resolution spectra of central stars of planetary nebulae can be reproduced in detail from the infrared to the X-ray…
CONTEXT: The spectral analysis of hot, massive stars is a fundamental astrophysical method to obtain their intrinsic properties and their feedback. Quantitative spectroscopy for hot, massive stars requires detailed numerical modeling of the…
A simple, fully connected neural network with a single hidden layer is used to estimate stellar masses for star-forming galaxies. The model is trained on broad-band photometry - from far-ultraviolet to mid-infrared wavelengths - generated…
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…
Spectroscopy of planetary nebulae (PNe) provides the means to investigate s-process enrichments of neutron(n)-capture elements that cannot be detected in asymptotic giant branch (AGB) stars. However, accurate abundance determinations of…
Context: New spectroscopic surveys will increase the number of astronomical objects requiring characterization by over tenfold.. Machine learning tools are required to address this data deluge in a fast and accurate fashion. Most machine…
Innovation in the ground and space-based instruments has taken us into a new age of spectroscopy, in which a large amount of stellar content is becoming available. So, automatic classification of stellar spectra became subjective in recent…
We present the first automated fitting method for the quantitative spectroscopy of O- and early B-type stars with stellar winds. The method combines the non-LTE stellar atmosphere code FASTWIND from Puls et al. (2005) with the genetic…
In this paper, we present a deep learning system approach to estimating luminosity, effective temperature, and surface gravity of O-type stars using the optical region of the stellar spectra. In previous work, we compare a set of machine…
*Context: The optimisation of new multiplex spectrographs (resolution, wavelength range,...), their associated surveys (choice of setup), or their parameterisation pipelines require methods that estimate which wavelengths contain useful…
Deep learning with artificial neural networks is increasingly gaining attention, because of its potential for data-driven astronomy. However, this methodology usually does not provide uncertainties and does not deal with incompleteness and…