AstroML
AstroML: Machine Learning for Astronomy. AstroML is a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, and matplotlib, and distributed under the BSD license. It contains a growing library of statistical and machine learning routines for analyzing astronomical data in python, loaders for several open astronomical datasets, and a large suite of examples of analyzing and visualizing astronomical datasets. This project was started in 2012 by Jake VanderPlas to accompany the book Statistics, Data Mining, and Machine Learning in Astronomy by Zeljko Ivezic, Andrew Connolly, Jacob VanderPlas, and Alex Gray
Keywords for this software
References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
Sorted by year (- Alfredo Mejia-Narvaez, Gustavo Bruzual, Sebastian F. Sanchez, Leticia Carigi, Jorge Barrera-Ballesteros, Mabel Valerdi, Renbin Yan, Niv Drory: CoSHA: Code for Stellar properties Heuristic Assignment - for the MaStar stellar library (2021) arXiv
- Bonasera, Stefano; Bosanac, Natasha: Applying data mining techniques to higher-dimensional Poincaré maps in the circular restricted three-body problem (2021)
- Huang, Da; Geng, Chao-Qiang; Kuan, Hao-Jui: Scalar gravitational wave signals from core collapse in massive scalar-tensor gravity with triple-scalar interactions (2021)
- Higson, Edward; Handley, Will; Hobson, Mike; Lasenby, Anthony: Sampling errors in nested sampling parameter estimation (2018)