Stellar parameters are estimated through spectra and are crucial in studying both stellar evolution and the history of the galaxy. To extract features from the spectra efficiently, we present ESNet (encoder selection network for spectra), a novel architecture that incorporates three essential modules: a feature encoder (FE), feature selection (FS), and feature mapping (FM). FE is responsible for extracting advanced spectral features through encoding. The role of FS, on the other hand, is to acquire compressed features by reducing the spectral dimension and eliminating redundant information. FM comes into play by fusing the advanced and compressed features, establishing a nonlinear mapping between spectra and stellar parameters. The stellar spectra used for training and testing are obtained through crossing LAMOST and SDSS. The experimental results demonstrate that for low signal-to-noise spectra (0–10), ESNet achieves excellent performance on the test set, with mean absolute error (MAE) values of 82 K for Teff (effective temperature), 0.20 dex for logg (logarithm of the gravity), and 0.10 dex for [Fe/H] (metallicity). The results indeed indicate that ESNet has an excellent ability to extract spectral features. Furthermore, this paper validates the consistency between ESNet predictions and the SDSS catalog. The experimental results prove that the model can be employed for the evaluation of stellar parameters.

ESNet: Estimating Stellar Parameters from LAMOST Low-Resolution Stellar Spectra

A spectral analysis pipeline of LAMOST (Large sky Area Multi-Object fiber Spectroscopic Telescope), which produces
archived spectral type data, is introduced. By studying observational and theoretical stellar spectra, spectral features
within medium resolution are discussed, those lines and bands with high sensitivity to stellar atmospheric parameters,
viz. effective temperature (Teff), surface gravity (logg) and metallicity ([Fe/H]), were selected. According to the
research, selected features were put into different objective algorithms to extract parameters. The application of three
algorithms to SDSS/SEGUE spectra, namely radial basis function neural network (RBFN), back propagation neural
network (BPN) and non-parameter regression (NPR), shows intrinsic statistical consistency. Based on the above research,
a stellar atmospheric parameter pipeline for LAMOST is designed.

Automated stellar spectral analysis software for survey spectra

Handling, processing and archiving the huge amount of data produced by the new generation of experiments and instruments in Astronomy and Astrophysics are among the more exciting challenges to address in designing the future data management infrastructures and computing services. We investigated the feasibility of a data management and computation infrastructure, available world-wide, with the aim of merging the FAIR (Findable, Accessible, Interoperable, Reusable) data management provided by IVOA standards with the efficiency and reliability of a cloud approach. Our work involved the Canadian Advanced Network for Astronomy Research (CANFAR) infrastructure and the European EGI federated cloud (EFC). We designed and deployed a pilot data management and computation infrastructure that provides IVOA-compliant VOSpace storage resources and wide access to interoperable federated clouds. In this paper, we detail the main user requirements covered, the technical choices and the implemented solutions and we describe the resulting Hybrid cloud Worldwide infrastructure, its benefits and limitations.

Cloud access to interoperable IVOA-compliant VOSpace storage

In addition to optical photometry of unprecedented quality, the Sloan Digital Sky Survey (SDSS) is also producing a massive spectroscopic database. They discuss determination of stellar parameters, such as effective temperature, gravity and metallicity from SDSS spectra, describe correlations between kinematics and metallicity, and study their variation as a function of the position in the Galaxy. They show that stellar parameter estimates by Beers et al. show a good correlation with the position of a star in the g-r vs. u-g color-color diagram, thereby demonstrating their robustness as well as a potential for photometric parameter estimation methods. Using Beers et al. parameters, they find that the metallicity distribution of the Milky Way stars at a few kpc from the galactic plane is bimodal with a local minimum at [Z/Z{sub {circle_dot}}] {approx} -1.3. The median metallicity for the low-metallicity [Z/Z{sub {circle_dot}}] -1.3 sample. they also find that the low-metallicity sample has {approx} 2.5 times larger velocity dispersion and that it does not rotate (at the {approx} 10 km/s level), while the rotational velocity of the high-metallicity samplemore » decreases smoothly with the height above the galactic plane.« less

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SDSS spectroscopic survey of stars

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SDSS spectroscopic survey of stars