astroslam 1.2022.1228.1

Description:

astroslam 1.2022.1228.1

SLAM
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.
Author
Bo Zhang ([email protected])
Home page

https://github.com/hypergravity/astroslam
https://pypi.org/project/astroslam/

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

https://zenodo.org/record/3461504



Tutorial
[updated on 2020-12-02]

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.

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.