Robust Machine Learning Applied to Terascale Astronomical Datasets

TL;DR

This paper demonstrates the application of machine learning algorithms, particularly k-nearest neighbors, on terascale astronomical datasets using supercomputing resources to improve classification and measurement accuracy.

Contribution

It introduces a novel use of supercomputing for data mining in astronomy, extending machine learning methods to handle terascale datasets efficiently.

Findings

Improved classifications for over 100 million celestial objects

Enhanced distance measurement techniques

Identification of infrastructure challenges for petascale data

Abstract

We present recent results from the LCDM (Laboratory for Cosmological Data Mining; http://lcdm.astro.uiuc.edu) collaboration between UIUC Astronomy and NCSA to deploy supercomputing cluster resources and machine learning algorithms for the mining of terascale astronomical datasets. This is a novel application in the field of astronomy, because we are using such resources for data mining, and not just performing simulations. Via a modified implementation of the NCSA cyberenvironment Data-to-Knowledge, we are able to provide improved classifications for over 100 million stars and galaxies in the Sloan Digital Sky Survey, improved distance measures, and a full exploitation of the simple but powerful k-nearest neighbor algorithm. A driving principle of this work is that our methods should be extensible from current terascale datasets to upcoming petascale datasets and beyond. We discuss issues encountered to-date, and further issues for the transition to petascale. In particular, disk I/O will become a major limiting factor unless the necessary infrastructure is implemented.