Skip to main content

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

Project description

SLAM DOI

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

Author

Bo Zhang (bozhang@nao.cas.cn)

Home page

Install

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

Tutorial

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.

Requirements

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

How to cite

Paper:

@ARTICLE{2020ApJS..246....9Z,
       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 = {https://ui.adsabs.harvard.edu/abs/2020ApJS..246....9Z},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Code:

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

For other formats, please go to https://search.datacite.org/works/10.5281/zenodo.3461504.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for astroslam, version 1.2020.1104.0
Filename, size File type Python version Upload date Hashes
Filename, size astroslam-1.2020.1104.0-py3-none-any.whl (117.0 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size astroslam-1.2020.1104.0.tar.gz (481.4 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page