Playing CHESS with stars. I. Search for similar stars in large spectroscopic data sets

Abstract

Context: Massive amounts of spectroscopic data obtained by stellar surveys are feeding an ongoing revolution in our knowledge of stellar and Galactic astrophysics. Analysing these data sets to extract the best possible astrophysical parameters on short time scales represents a considerable challenge. Aims: The differential analysis method is known to return the most precise results in the spectroscopic analyses of F-, G-, and K-type stars. However, it can only be applied to stars with similar parameters. Our goal is to present a procedure that significantly simplifies the identification of spectra from stars with similar atmospheric parameters within extensive spectral datasets. This approach allows for the quick application of differential analyses in these samples, thus enhancing the precision of the results. Methods: We used projection maps created by the t-SNE dimensionality reduction algorithm applied directly to the spectra using pixels as dimensions. For testing the method, we used more than 7300 high-resolution UVES spectra of about 3000 stars in the field-of-view towards open and globular clusters. As reference, we used 1244 spectra of 274 stars with well-determined and high-quality atmospheric parameters. Results: We calibrated a spectral similarity metric that can identify stars in a t-SNE projection map with parameters that differ by $\pm$ 200 K, $\pm$ 0.3 dex, and $\pm$ 0.2 dex in effective temperatures, surface gravities, and metallicities, respectively. We achieved completeness between 74-98 % and typical purity between 39-54 % in this selection. With this, we will drastically facilitate the detection of stars with similar spectra for a successful differential analysis. We used this method to evaluate the accuracy and precision of four atmospheric parameter catalogues, identifying the regions of the parameter space where spectral analysis methods needs improvement.

References in corpus (66)

• Gaia Data Release 3: Summary of the content and survey properties
• The SIMBAD astronomical database
• The Apache Point Observatory Galactic Evolution Experiment (APOGEE)
• The K2 Mission: Characterization and Early results
• The VizieR database of Astronomical Catalogues
• The GALAH Survey: Scientific Motivation
• ASPCAP: The Apogee Stellar Parameter and Chemical Abundances Pipeline
• Determining stellar atmospheric parameters and chemical abundances of FGK stars with iSpec
• Abundances, Stellar Parameters, and Spectra From the SDSS-III/APOGEE Survey
• The Cannon: A data-driven approach to stellar label determination
• Improving the open cluster census. II. An all-sky cluster catalogue with Gaia DR3
• Modern stellar spectroscopy caveats
• Stellar Diameters and Temperatures III. Main Sequence A, F, G, & K Stars: Additional high-precision measurements and empirical relations
• Photo-astrometric distances, extinctions, and astrophysical parameters for Gaia EDR3 stars brighter than G=18.5
• StarHorse: A Bayesian tool for determining stellar masses, ages, distances, and extinctions for field stars
• The Payne: self-consistent ab initio fitting of stellar spectra
• Gaia-ESO Survey: The analysis of high-resolution UVES spectra of FGK-type stars
• Gaia Data Release 3: Astrophysical parameters inference system (Apsis) I -- methods and content overview
• The wide-field, multiplexed, spectroscopic facility WEAVE: Survey design, overview, and simulated implementation
• Abundance Estimates for 16 Elements in 6 Million Stars from LAMOST DR5 Low-Resolution Spectra
• The Solar Twin Planet Search. I. Fundamental parameters of the stellar sample
• Improving the open cluster census. III. Using cluster masses, radii, and dynamics to create a cleaned open cluster catalogue
• The Gaia-ESO Public Spectroscopic Survey: Implementation, data products, open cluster survey, science, and legacy
• Atomic data for the Gaia-ESO Survey
• Cross-Correlation and Maximum Likelihood Analysis: A New Approach to Combine Cross-Correlation Functions
• The Sixth Data Release of the Radial Velocity Experiment (RAVE) -- I: Survey Description, Spectra and Radial Velocities
• APOGEE Data Releases 13 and 14: Stellar Parameter and Abundance Comparisons With Independent Analyses
• Automated derivation of stellar atmospheric parameters and chemical abundances: the MATISSE algorithm
• Parameters of 220 million stars from Gaia BP/RP spectra
• Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra
• The Gaia-ESO Public Spectroscopic Survey: Motivation, implementation, GIRAFFE data processing, analysis, and final data products
• An Application of Deep Neural Networks in the Analysis of Stellar Spectra
• Stellar Chemical Abundances: In Pursuit of the Highest Achievable Precision
• High-precision stellar abundances of the elements - methods and applications
• Gaia FGK Benchmark stars: Opening the black box of stellar element abundance determination
• A Comparison of Stellar Elemental Abundance Techniques and Measurements
• The Galah Survey: Classification and diagnostics with t-SNE reduction of spectral information
• APOKASC-3: The Third Joint Spectroscopic and Asteroseismic catalog for Evolved Stars in the Kepler Fields
• Assessment of [Fe/H] determinations for FGK stars in spectroscopic surveys
• The Gaia-ESO Survey: calibration strategy
• Benchmark stars for Gaia: fundamental properties of the Population II star HD140283 from interferometric, spectroscopic and photometric data
• SteParSyn: A Bayesian code to infer stellar atmospheric parameters using spectral synthesis
• Comparative Modelling of the Spectra of Cool Giants
• The Gaia-ESO Survey: homogenisation of stellar parameters and elemental abundances
• FAMA: An automatic code for stellar parameter and abundance determination
• Very metal-poor stars observed by the RAVE survey
• The Gaia-ESO Survey: Target selection of open cluster stars
• Comparative analysis of atmospheric parameters from high-resolution spectroscopic sky surveys: APOGEE, GALAH, Gaia-ESO
• Cycle-StarNet: Bridging the gap between theory and data by leveraging large datasets
• The RAdial Velocity Experiment (RAVE): Parameterisation of RAVE spectra based on convolutional neural networks
• The complex stellar system M 22: confirming abundance variations with high precision differential measurements
• The stellar parameters and elemental abundances from low-resolution spectra I: 1.2 million giants from LAMOST DR8
• SUPPNet: Neural network for stellar spectrum normalisation
• ATHOS: On-the-fly stellar parameter determination of FGK stars based on flux ratios from optical spectra
• Titans metal-poor reference stars. I. Accurate effective temperatures and surface gravities for dwarfs and subgiants from 3D non-LTE H$\alpha$ profiles and Gaia parallaxes
• Estimating Stellar Parameters from LAMOST Low-resolution Spectra
• The Gaia-ESO Survey: Preparing the ground for 4MOST & WEAVE galactic surveys. Chemical evolution of lithium with machine learning
• The Gaia-ESO Survey: Spectroscopic-asteroseismic analysis of K2 stars in Gaia-ESO
• Titans metal-poor reference stars II. Red giants and CEMP stars
• Sr and Ba abundance determinations: comparing machine-learning with star-by-star analyses -- High-resolution re-analysis of suspected LAMOST barium stars
• LAMA: LAMOST Medium-Resolution Spectral Analysis Pipeline
• Inferring stellar parameters and their uncertainties from high-resolution spectroscopy using invertible neural networks
• The Gaia-ESO Survey DR5.1 and Gaia DR3 GSP-Spec: a comparative analysis
• Determination of stellar parameters for Ariel targets: a comparison analysis between different spectroscopic methods
• Beryllium abundances in turn-off stars of globular clusters with the CUBES spectrograph
• Validating Stellar Abundance Measurements from Multi-Resolution Spectroscopy