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: click here.
Bo Zhang (bozhang@nao.cas.cn)
- 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
[updated on 2020年12月02日]
- A new SLAM tutorial can be found here
- 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
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.