Machine learning algorithms in astronomy

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

We analyze the current state and challenges of machine learning techniques in astronomy, in order to keep up to date with the exponentially increasing amount of observational data and future instrumentation of at least a few orders of magnitude higher than from current instruments. We present the latest cutting-edge methods and new algorithms for extracting knowledge from large and complex astronomical data sets, as a versatile and valuable tool in a new era of data-driven science. As telescopes and detectors become more powerful, the volume of available data will enter the petabyte regime, in need for new algorithms aimed at better modeling of sky brightness, requiring more computing and providing more promising data analysis for billions of sky objects. We emphasize the most important current trends and future directions in machine learning currently adapted to astronomy, pushing forward the frontiers of knowledge through better data collection, manipulation, analysis and visualizing. We also evaluate the emergent techniques and latest approaches for various types and sizes of data sets and show the vast potential and versatility of machine learning algorithms in front of the new challenge of the Fourth Paradigm.
Original languageEnglish
Title of host publicationAstronomical Data Analysis Software and Systems XXV
Subtitle of host publicationproceedings of a conference held at Rydges World Square, Sydney, NSW, Australia, 25-29 October 2015
EditorsNuria P. F. Lorente, Keith Shortridge, Randall Wayth
Place of PublicationSan Francisco, CA
PublisherAstronomical Society of the Pacific
Pages245-248
Number of pages4
ISBN (Electronic)9781583819098
ISBN (Print)9781583819081
Publication statusPublished - Dec 2017
EventAstronomical Data Analysis Software and Systems XXV - Sydney, Australia
Duration: 25 Oct 201529 Oct 2015
Conference number: 25

Publication series

NameAstronomical Society of the Pacific conference series
PublisherAstronomical Society of the Pacific
Volume512
ISSN (Print)1080-7926

Conference

ConferenceAstronomical Data Analysis Software and Systems XXV
Abbreviated titleADASS
CountryAustralia
CitySydney
Period25/10/1529/10/15

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  • Cite this

    Howard, E. M. (2017). Machine learning algorithms in astronomy. In N. P. F. Lorente, K. Shortridge, & R. Wayth (Eds.), Astronomical Data Analysis Software and Systems XXV: proceedings of a conference held at Rydges World Square, Sydney, NSW, Australia, 25-29 October 2015 (pp. 245-248). (Astronomical Society of the Pacific conference series; Vol. 512). San Francisco, CA: Astronomical Society of the Pacific.