Big universe, big data: machine learning and image analysis for astronomy

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Standard

Big universe, big data : machine learning and image analysis for astronomy. / Kremer, Jan; Stensbo-Smidt, Kristoffer; Gieseke, Fabian Cristian; Pedersen, Kim Steenstrup; Igel, Christian.

I: IEEE Intelligent Systems, Bind 32, Nr. 2, 2017, s. 16-22.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Kremer, J, Stensbo-Smidt, K, Gieseke, FC, Pedersen, KS & Igel, C 2017, 'Big universe, big data: machine learning and image analysis for astronomy', IEEE Intelligent Systems, bind 32, nr. 2, s. 16-22. https://doi.org/10.1109/MIS.2017.40

APA

Kremer, J., Stensbo-Smidt, K., Gieseke, F. C., Pedersen, K. S., & Igel, C. (2017). Big universe, big data: machine learning and image analysis for astronomy. IEEE Intelligent Systems, 32(2), 16-22. https://doi.org/10.1109/MIS.2017.40

Vancouver

Kremer J, Stensbo-Smidt K, Gieseke FC, Pedersen KS, Igel C. Big universe, big data: machine learning and image analysis for astronomy. IEEE Intelligent Systems. 2017;32(2):16-22. https://doi.org/10.1109/MIS.2017.40

Author

Kremer, Jan ; Stensbo-Smidt, Kristoffer ; Gieseke, Fabian Cristian ; Pedersen, Kim Steenstrup ; Igel, Christian. / Big universe, big data : machine learning and image analysis for astronomy. I: IEEE Intelligent Systems. 2017 ; Bind 32, Nr. 2. s. 16-22.

Bibtex

@article{907c2b13eaae4267a9a2ae071544664f,
title = "Big universe, big data: machine learning and image analysis for astronomy",
abstract = "Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on large aperture telescopes has led to ever more ambitious surveys of the sky. Data volumes of entire surveys a decade ago can now be acquired in a single night and real-time analysis is often desired. Thus, modern astronomy requires big data know-how, in particular it demands highly efficient machine learning and image analysis algorithms. But scalability is not the only challenge: Astronomy applications touch several current machine learning research questions, such as learning from biased data and dealing with label and measurement noise. We argue that this makes astronomy a great domain for computer science research, as it pushes the boundaries of data analysis. In the following, we will present this exciting application area for data scientists. We will focus on exemplary results, discuss main challenges, and highlight some recent methodological advancements in machine learning and image analysis triggered by astronomical applications.",
keywords = "Faculty of Science, Big Data, Astronomy, Machine Learning, Computer Vision",
author = "Jan Kremer and Kristoffer Stensbo-Smidt and Gieseke, {Fabian Cristian} and Pedersen, {Kim Steenstrup} and Christian Igel",
year = "2017",
doi = "10.1109/MIS.2017.40",
language = "English",
volume = "32",
pages = "16--22",
journal = "I E E E Intelligent Systems",
issn = "1541-1672",
publisher = "Institute of Electrical and Electronics Engineers",
number = "2",

}

RIS

TY - JOUR

T1 - Big universe, big data

T2 - machine learning and image analysis for astronomy

AU - Kremer, Jan

AU - Stensbo-Smidt, Kristoffer

AU - Gieseke, Fabian Cristian

AU - Pedersen, Kim Steenstrup

AU - Igel, Christian

PY - 2017

Y1 - 2017

N2 - Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on large aperture telescopes has led to ever more ambitious surveys of the sky. Data volumes of entire surveys a decade ago can now be acquired in a single night and real-time analysis is often desired. Thus, modern astronomy requires big data know-how, in particular it demands highly efficient machine learning and image analysis algorithms. But scalability is not the only challenge: Astronomy applications touch several current machine learning research questions, such as learning from biased data and dealing with label and measurement noise. We argue that this makes astronomy a great domain for computer science research, as it pushes the boundaries of data analysis. In the following, we will present this exciting application area for data scientists. We will focus on exemplary results, discuss main challenges, and highlight some recent methodological advancements in machine learning and image analysis triggered by astronomical applications.

AB - Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on large aperture telescopes has led to ever more ambitious surveys of the sky. Data volumes of entire surveys a decade ago can now be acquired in a single night and real-time analysis is often desired. Thus, modern astronomy requires big data know-how, in particular it demands highly efficient machine learning and image analysis algorithms. But scalability is not the only challenge: Astronomy applications touch several current machine learning research questions, such as learning from biased data and dealing with label and measurement noise. We argue that this makes astronomy a great domain for computer science research, as it pushes the boundaries of data analysis. In the following, we will present this exciting application area for data scientists. We will focus on exemplary results, discuss main challenges, and highlight some recent methodological advancements in machine learning and image analysis triggered by astronomical applications.

KW - Faculty of Science

KW - Big Data

KW - Astronomy

KW - Machine Learning

KW - Computer Vision

U2 - 10.1109/MIS.2017.40

DO - 10.1109/MIS.2017.40

M3 - Journal article

VL - 32

SP - 16

EP - 22

JO - I E E E Intelligent Systems

JF - I E E E Intelligent Systems

SN - 1541-1672

IS - 2

ER -

ID: 167219728