Detecting quasars in large-scale astronomical surveys

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Detecting quasars in large-scale astronomical surveys. / Gieseke, Fabian; Polsterer, Kai Lars; Thom, Andreas; Zinn, Peter; Bomanns, Dominik; Dettmar, Ralf Jürgen; Kramer, Oliver; Vahrenhold, Jan.

Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010. IEEE, 2010. p. 352-357 5708856.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Gieseke, F, Polsterer, KL, Thom, A, Zinn, P, Bomanns, D, Dettmar, RJ, Kramer, O & Vahrenhold, J 2010, Detecting quasars in large-scale astronomical surveys. in Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010., 5708856, IEEE, pp. 352-357, 9th International Conference on Machine Learning and Applications, ICMLA 2010, Washington, DC, United States, 12/12/2010. https://doi.org/10.1109/ICMLA.2010.59

APA

Gieseke, F., Polsterer, K. L., Thom, A., Zinn, P., Bomanns, D., Dettmar, R. J., Kramer, O., & Vahrenhold, J. (2010). Detecting quasars in large-scale astronomical surveys. In Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010 (pp. 352-357). [5708856] IEEE. https://doi.org/10.1109/ICMLA.2010.59

Vancouver

Gieseke F, Polsterer KL, Thom A, Zinn P, Bomanns D, Dettmar RJ et al. Detecting quasars in large-scale astronomical surveys. In Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010. IEEE. 2010. p. 352-357. 5708856 https://doi.org/10.1109/ICMLA.2010.59

Author

Gieseke, Fabian ; Polsterer, Kai Lars ; Thom, Andreas ; Zinn, Peter ; Bomanns, Dominik ; Dettmar, Ralf Jürgen ; Kramer, Oliver ; Vahrenhold, Jan. / Detecting quasars in large-scale astronomical surveys. Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010. IEEE, 2010. pp. 352-357

Bibtex

@inproceedings{01cc1b7b57af4b65a542f0b99ebb6444,
title = "Detecting quasars in large-scale astronomical surveys",
abstract = "We present a classification-based approach to identify quasi-stellar radio sources (quasars) in the Sloan Digital Sky Survey and evaluate its performance on a manually labeled training set. While reasonable results can already be obtained via approaches working only on photometric data, our experiments indicate that simple but problem-specific features extracted from spectroscopic data can significantly improve the classification performance. Since our approach works orthogonal to existing classification schemes used for building the spectroscopic catalogs, our classification results are well suited for a mutual assessment of the approaches' accuracies.",
keywords = "Astronomy, Classification, Feature extraction",
author = "Fabian Gieseke and Polsterer, {Kai Lars} and Andreas Thom and Peter Zinn and Dominik Bomanns and Dettmar, {Ralf J{\"u}rgen} and Oliver Kramer and Jan Vahrenhold",
year = "2010",
doi = "10.1109/ICMLA.2010.59",
language = "English",
isbn = "978-0-7695-4300-0",
pages = "352--357",
booktitle = "Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010",
publisher = "IEEE",
note = "9th International Conference on Machine Learning and Applications, ICMLA 2010 ; Conference date: 12-12-2010 Through 14-12-2010",

}

RIS

TY - GEN

T1 - Detecting quasars in large-scale astronomical surveys

AU - Gieseke, Fabian

AU - Polsterer, Kai Lars

AU - Thom, Andreas

AU - Zinn, Peter

AU - Bomanns, Dominik

AU - Dettmar, Ralf Jürgen

AU - Kramer, Oliver

AU - Vahrenhold, Jan

PY - 2010

Y1 - 2010

N2 - We present a classification-based approach to identify quasi-stellar radio sources (quasars) in the Sloan Digital Sky Survey and evaluate its performance on a manually labeled training set. While reasonable results can already be obtained via approaches working only on photometric data, our experiments indicate that simple but problem-specific features extracted from spectroscopic data can significantly improve the classification performance. Since our approach works orthogonal to existing classification schemes used for building the spectroscopic catalogs, our classification results are well suited for a mutual assessment of the approaches' accuracies.

AB - We present a classification-based approach to identify quasi-stellar radio sources (quasars) in the Sloan Digital Sky Survey and evaluate its performance on a manually labeled training set. While reasonable results can already be obtained via approaches working only on photometric data, our experiments indicate that simple but problem-specific features extracted from spectroscopic data can significantly improve the classification performance. Since our approach works orthogonal to existing classification schemes used for building the spectroscopic catalogs, our classification results are well suited for a mutual assessment of the approaches' accuracies.

KW - Astronomy

KW - Classification

KW - Feature extraction

U2 - 10.1109/ICMLA.2010.59

DO - 10.1109/ICMLA.2010.59

M3 - Article in proceedings

AN - SCOPUS:79952412202

SN - 978-0-7695-4300-0

SP - 352

EP - 357

BT - Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010

PB - IEEE

T2 - 9th International Conference on Machine Learning and Applications, ICMLA 2010

Y2 - 12 December 2010 through 14 December 2010

ER -

ID: 167917653