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A forward model using SVR to estimate stellar parameters from spectra.

Project description


Stellar LAbel Machine (SLAM) is a forward model to estimate stellar labels (e.g., Teff, logg and chemical abundances). It is based on Support Vector Regression (SVR) which is a non-parametric regression method.

For details of SLAM, see Deriving the stellar labels of LAMOST spectra with Stellar LAbel Machine (SLAM).

Related Projects

  1. Exploring the spectral information content in the LAMOST medium-resolution survey (MRS)
  2. Tracing Kinematic and Chemical Properties of Sagittarius Stream by K-Giants, M-Giants, and BHB stars


Bo Zhang (

Home page


  • for the latest stable version:
    • pip install -U astroslam
  • for the latest github version:
    • pip install -U git+git://
  • for Zenodo version


A simple guide to SLAM can be accessed here with token gkvi. If you are interested in SLAM or have any related questions, do not hesitate to contact me.


  • numpy
  • scipy
  • matplotlib
  • astropy
  • scikit-learn
  • joblib
  • pandas
  • emcee

How to cite


       author = {{Zhang}, Bo and {Liu}, Chao and {Deng}, Li-Cai},
        title = "{Deriving the Stellar Labels of LAMOST Spectra with the Stellar LAbel Machine (SLAM)}",
      journal = {\apjs},
     keywords = {Astronomical methods, Astronomy data analysis, Bayesian statistics, Stellar abundances, Chemical abundances, Fundamental parameters of stars, Catalogs, Surveys, Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = 2020,
        month = jan,
       volume = {246},
       number = {1},
          eid = {9},
        pages = {9},
          doi = {10.3847/1538-4365/ab55ef},
archivePrefix = {arXiv},
       eprint = {1908.08677},
 primaryClass = {astro-ph.SR},
       adsurl = {},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}


    author = {Zhang, Bo},
    title = {hypergravity/astroslam: Stellar LAbel Machine},
    doi = {10.5281/zenodo.3461504},
    url = {},
    publisher = {Zenodo},
    year = {2019}

For other formats, please go to

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