相关论文: The RAdial Velocity Experiment (RAVE): Parameteris…

The RAdial Velocity Experiment (RAVE) is a medium resolution R~7500 spectroscopic survey of the Milky Way which already obtained over half a million stellar spectra. They present a randomly selected magnitude-limited sample, so it is…

We present part 2 of the 6th and final Data Release (DR6 or FDR) of the Radial Velocity Experiment (RAVE), a magnitude-limited (9<I<12) spectroscopic survey of Galactic stars randomly selected in the southern hemisphere. The RAVE…

Very metal-poor (VMP, [Fe/H]<-2.0) stars offer a wealth of information on the nature and evolution of elemental production in the early galaxy and universe. The upcoming China Space Station Telescope (CSST) will provide us with a large…

The Radial Velocity Experiment (RAVE) is a magnitude-limited (9<I<12) spectroscopic survey of Galactic stars randomly selected in the southern hemisphere. The RAVE medium-resolution spectra (R~7500) cover the Ca-triplet region (8410-8795A).…

We present the stellar atmospheric parameters (effective temperature, surface gravity, overall metallicity), radial velocities, individual abundances and distances determined for 425 561 stars, which constitute the fourth public data…

The RAdial Velocity Experiment (RAVE) is a spectroscopic survey of the Milky Way. We use the subsample of spectra with spectroscopically determined values of stellar parameters to determine the distances to these stars. The list currently…

We present the second data release of the Radial Velocity Experiment (RAVE), an ambitious spectroscopic survey to measure radial velocities (RVs) and stellar atmosphere parameters of up to one million stars using the 6dF multi-object…

Convolutional neural networks (CNN) have been successfully employed to tackle several remote sensing tasks such as image classification and show better performance than previous techniques. For the radar imaging community, a natural…

We present the first data release of the Radial Velocity Experiment (RAVE), an ambitious spectroscopic survey to measure radial velocities and stellar atmosphere parameters (temperature, metallicity, surface gravity) of up to one million…

In June 2022, Gaia DR3 has provided the astronomy community with about one million spectra from the Radial Velocity Spectrometer (RVS) covering the CaII triplet region. However, one-third of the published spectra have 15<S/N<25 per pixel…

Convolutional neural networks (CNNs) are widely used for image recognition and text analysis, and have been suggested for application on one-dimensional data as a way to reduce the need for pre-processing steps. Pre-processing is an…

In recent years the amount of publicly available astronomical data has increased exponentially, with a remarkable example being large scale multiepoch photometric surveys. This wealth of data poses challenges to the classical methodologies…

We aim to prepare the machine-learning ground for the next generation of spectroscopic surveys, such as 4MOST and WEAVE. Our goal is to show that convolutional neural networks can predict accurate stellar labels from relevant spectral…

We present chemical elemental abundances for $36,561$ stars observed by the RAdial Velocity Experiment (RAVE), an ambitious spectroscopic survey of our Galaxy at Galactic latitudes $|$b$|>25^{\circ}$ and with magnitudes in the range…

The orbits, atmospheric parameters, chemical abundances, and ages of individual stars in the Milky Way provide the most comprehensive illustration of galaxy formation available. The Tycho-Gaia Astrometric Solution (TGAS) will deliver…

We design a convolutional neural network (CNN) incorporating channel attention and spatial attention mechanisms to predict atmospheric parameters of hot subdwarfs. The experimental dataset comprises spectra at nine distinct signal-to-noise…

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…

Aims. This paper introduces LRPayne, a novel algorithm designed for the efficient determination of stellar parameters and chemical abundances from low-resolution optical spectra, with a primary focus on data from large-scale galactic…

Autonomous Raman instruments on Mars rovers, deep-sea landers, and field robots must interpret raw spectra distorted by fluorescence baselines, peak shifts, and limited ground-truth labels. Using curated subsets of the RRUFF database, we…

The new data release (DR5) of the RAdial Velocity Experiment (RAVE) includes radial velocities of 520,781 spectra of 457,588 individual stars, of which 215,590 individual stars are released in the Tycho-Gaia astrometric solution (TGAS) in…