Related papers: Automatic stellar spectra parameterisation in the …
The Gaia Data Release 3 (DR3), published in June 2022, delivers a diverse set of astrometric, photometric, and spectroscopic measurements for more than a billion stars. The wealth and complexity of the data makes traditional approaches for…
The GAIA Galactic survey satellite will obtain photometry in 15 filters of over 10^9 stars in our Galaxy across a very wide range of stellar types. No other planned survey will provide so much photometric information on so many stars. I…
Countless low-surface brightness objects - including spiral galaxies, dwarf galaxies, and noise patterns - have been detected in recent large surveys. Classically, astronomers visually inspect those detections to distinguish between real…
Over the last two decades, machine learning models have been widely applied and have proven effective in classifying variable stars, particularly with the adoption of deep learning architectures such as convolutional neural networks,…
The new CARMENES instrument comprises two high-resolution and high-stability spectrographs that are used to search for habitable planets around M dwarfs in the visible and near-infrared regime via the Doppler technique. Characterising our…
We present techniques for the estimation of stellar atmospheric parameters (Teff,logg,[Fe/H]) for stars from the SDSS/SEGUE survey. The atmospheric parameters are derived from the observed medium-resolution (R=2000) stellar spectra using…
We apply four statistical learning methods to a sample of $7941$ galaxies ($z<0.06$) from the Galaxy and Mass Assembly (GAMA) survey to test the feasibility of using automated algorithms to classify galaxies. Using $10$ features measured…
The currently operating space missions, as well as those that will be launched in the near future, (will) deliver high-quality data for millions of stellar objects. Since the majority of stellar astrophysical applications still (at least…
We present some results of an on-going project aimed at studying a sample of Galactic HII regions ionized by a single massive star to test the predictions of modern generation stellar atmosphere codes in the H Lyman continuum. The…
In the first two papers of this series (Rhea et al. 2020; Rhea et al. 2021), we demonstrated the dynamism of machine learning applied to optical spectral analysis by using neural networks to extract kinematic parameters and emission-line…
The numerous extragalactic and cosmological motivations of the DENIS and 2MASS near infrared surveys are outlined. The performance of the DENIS survey is estimated from 50 deg^2 of high galactic latitude data (20 deg < |b| < 60 deg). Simple…
Space-based projects are providing a wealth of high-quality asteroseismic data, including frequencies for a large number of stars showing solar-like oscillations. These data open the prospect for precise determinations of key stellar…
In this work we analyze the spectroscopic properties of a large number of H ii regions, \sim2600, located in 38 galaxies. The sample of galaxies has been assembled from the face-on spirals in the PINGS survey and a sample described in…
Large spectroscopic surveys aim to consistently compute stellar parameters of very diverse stars while minimizing systematic errors. We explore the use of stellar clusters as benchmarks to verify the precision of spectroscopic parameters in…
With tens to hundreds of spectra of white dwarfs being taken each night from multi-object spectroscopic surveys, automated spectral classification is essential as part of efficient data processing. In this study, we design a neural network…
Aims: Integral Field Spectroscopy (IFS) is a powerful approach for the study of nearby galaxies since it enables a detailed analysis of their resolved physical properties. Here we present the sample of nearby galaxies selected to exploit…
In this work we explore the possibility of applying machine learning methods designed for one-dimensional problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly…
The DENIS survey is currently imaging the entire southern sky in the I, J, and K wavebands. The current star/galaxy separation algorithm is presented and the galaxy counts are nearly perfectly Euclidean. 95% complete and reliable galaxy…
In this paper, we apply a new statistical analysis technique, Mean Field approach to Bayesian Independent Component Analysis (MF-ICA), on galaxy spectral analysis. This algorithm can compress the stellar spectral library into a few…
A persistent challenge in astronomical machine learning is a systematic bias where predictions compress the dynamic range of true values-high values are consistently predicted too low while low values are predicted too high. Understanding…