Investigation of stellar magnetic activity using variational autoencoder based on low-resolution spectroscopic survey

Abstract

We apply the variational autoencoder (VAE) to the LAMOST-K2 low-resolution spectra to detect the magnetic activity of the stars in the K2 field. After the training on the spectra of the selected inactive stars, the VAE model can efficiently generate the synthetic reference templates needed by the spectral subtraction procedure, without knowing any stellar parameters. Then we detect the peculiar spectral features, such as chromospheric emissions, strong nebular emissions and lithium absorptions, in our sample. We measure the emissions of the chromospheric activity indicators, H$\alpha$ and Ca II infrared triplet (IRT) lines, to quantify the stellar magnetic activity. The excess emissions of H$\alpha$ and Ca II IRT lines of the active stars are correlated well to the rotational periods and the amplitudes of light curves derived from the K2 photometry. We degrade the LAMOST spectra to simulate the slitless spectra of the China Space Station Telescope (CSST) and apply the VAE to the simulated data. For cool active stars, we reveal a good agreement between the equivalent widths (EWs) of H$\alpha$ line derived from the spectra with two resolutions. The result indicates the ability of identifying the magnetically active stars in the future CSST survey, which will deliver an unprecedented large database of low-resolution spectra as well as simultaneous multi-band photometry of stars.

arXiv comment13 pages, 19 figures, accepted for publication in MNRAS. Table 1 is available on Zenodo at https://doi.org/10.5281/zenodo.6802956 and the code can be found on GitHub at https://github.com/xylib/vae-for-spectroscopic-survey

References in corpus (43)

• The Sloan Digital Sky Survey: Technical Summary
• A new extensive library of PHOENIX stellar atmospheres and synthetic spectra
• Kepler Mission Design, Realized Photometric Performance, and Early Science
• The First Data Release of the Sloan Digital Sky Survey
• The First Data Release (DR1) of the LAMOST general survey
• The Indo-U.S. Library of Coude Feed Stellar Spectra
• LAMOST Experiment for Galactic Understanding and Exploration (LEGUE) The survey science plan
• Improving galaxy morphologies for SDSS with Deep Learning
• Spectroscopic Properties of Cool Stars in the SDSS: An Analysis of Magnetic Activity and a Search for Subdwarfs
• On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data
• Cosmology from the Chinese Space Station Optical Survey (CSS-OS)
• Low-Mass Dwarf Template Spectra from the Sloan Digital Sky Survey
• Generalized investigation of the rotation-activity relation: Favouring rotation period instead of Rossby number
• CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding
• LAMOST observations in the Kepler field. Database of low-resolution spectra
• Activity indicators and stellar parameters of the Kepler targets. An application of the ROTFIT pipeline to LAMOST-Kepler stellar spectra
• K2 Variable Catalogue II: Machine Learning Classification of Variable Stars and Eclipsing Binaries in K2 Fields 0-4
• The Gaia-ESO Survey: Chromospheric Emission, Accretion Properties, and Rotation in $\gamma$ Velorum and Chamaeleon I
• Rotation of Low-Mass Stars in Taurus with K2
• Tracers of Chromospheric Structure I: Observations of Ca II K and H alpha in M Dwarfs
• Multiwavelength optical observations of chromospherically active binary systems. I. Simultaneous H{alpha}, NaI D{1}, D{2}, and HeI D{3} observations
• Dimensionality Reduction of SDSS Spectra with Variational Autoencoders
• LAMOST observations in the Kepler field. II. Database of the low-resolution spectra from the five-year regular survey
• Stellar activity with LAMOST. I. Spot configuration in Pleiades
• The Ca II infrared triplet's performance as an activity indicator compared to Ca II H and K
• Magnetic Activity of F-, G-, and K-type Stars in the LAMOST-Kepler Field
• A slitless spectroscopic survey for Halpha emission-line objects in SMC clusters
• Overview of the LAMOST-$Kepler$ project
• Lithium abundance in atmospheres of F- and G-type supergiants and bright giants
• An autoencoder of stellar spectra and its application in automatically estimating atmospheric parameters
• Enhanced stellar activity for slow antisolar differential rotation?
• Relative Flux Calibration of the LAMOST Spectroscopic Survey of the Galactic Anti-center
• LAMOST Observations in 15 \textit{K}2 Campaigns: I. Low resolution spectra from LAMOST DR6
• Active deep learning method for the discovery of objects of interest in large spectroscopic surveys
• Neural network-based anomaly detection for high-resolution X-ray spectroscopy
• The Lithium Abundances from the Large Sky Area Multi-object Fiber Spectroscopic Telescope Medium-resolution Survey. I. The Method
• Machines Learn to Infer Stellar Parameters Just by Looking at a Large Number of Spectra
• AKARI mid-infrared slitless spectroscopic survey of star-forming galaxies at $z\lesssim 0.5$
• Structured Variational Inference for Simulating Populations of Radio Galaxies
• An unsupervised method for identifying $X$-enriched stars directly from spectra: Li in LAMOST
• A slitless spectroscopic survey for quasars near quasars
• The GALAH survey: Characterization of emission-line stars with spectral modelling using autoencoders
• X-ray Study of Spatial Structures in Tycho's Supernova Remnant Using Unsupervised Deep Learning

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