AstroML: data mining and machine learning for Astronomy

AstroML: data mining and machine learning for Astronomy by Jake Vanderplas, Alex Gray, Andrew Connolly and Zeljko Ivezic.

Description:

Python is currently being adopted as the language of choice by many astronomical researchers. A prominent example is in the Large Synoptic Survey Telescope (LSST), a project which will repeatedly observe the southern sky 1000 times over the course of 10 years. The 30,000 GB of raw data created each night will pass through a processing pipeline consisting of C++ and legacy code, stitched together with a python interface. This example underscores the need for astronomers to be well-versed in large-scale statistical analysis techniques in python. We seek to address this need with the AstroML package, which is designed to be a repository for well-tested data mining and machine learning routines, with a focus on applications in astronomy and astrophysics. It will be released in late 2012 with an associated graduate-level textbook, ‘Statistics, Data Mining and Machine Learning in Astronomy’ (Princeton University Press). AstroML leverages many computational tools already available available in the python universe, including numpy, scipy, scikit- learn, pymc, healpy, and others, and adds efficient implementations of several routines more specific to astronomy. A main feature of the package is the extensive set of practical examples of astronomical data analysis, all written in python. In this talk, we will explore the statistical analysis of several interesting astrophysical datasets using python and astroML.

AstroML at Github:

AstroML is a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, and matplotlib, and distributed under the 3-clause 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.

The goal of astroML is to provide a community repository for fast Python implementations of common tools and routines used for statistical data analysis in astronomy and astrophysics, to provide a uniform and easy-to-use interface to freely available astronomical datasets. We hope this package will be useful to researchers and students of astronomy. The astroML project was started in 2012 to accompany the book Statistics, Data Mining, and Machine Learning in Astronomy by Zeljko Ivezic, Andrew Connolly, Jacob VanderPlas, and Alex Gray, to be published in early 2013.

The book, Statistics, Data Mining, and Machine Learning in Astronomy by Zeljko Ivezic, Andrew Connolly, Jacob VanderPlas, and Alex Gray, is not yet listed by Princeton University Press. 🙁

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One Response to “AstroML: data mining and machine learning for Astronomy”

  1. […] Para quem desejar realizer analyses de dados espaciais com Python é uma ótima pedida. Share this:ImprimirLinkedInFacebookEmailRedditDiggStumbleUponTwitterGoogle +1TumblrPinterestCurtir isso:Curtir Carregando… Etiquetado Astronomia, Ferramentas, Python […]