Finding rare objects and building pure samples: Probabilistic quasar classification from low-resolution Gaia spectra

Monthly Notices of the Royal Astronomical Society

Volumn 391, Issue 4, 2008, Pages 1838-1853

  Bailer Jones, C A L ^a   Smith, K W ^a   Tiede, C ^a   Sordo, R ^b   Vallenari, A ^b  

^a MAX PLANCK INSTITUT FÜR ASTRONOMIE   (Germany)

^b INAF OSSERVATORIO ASTRONOMICO DI PADOVA   (Italy)

Author keywords

Methods: data analysis; Methods: statistical; Quasars: general; Surveys

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONTAMINATION; DATA HANDLING; ECONOMIC AND SOCIAL EFFECTS; GALAXIES; GEOMETRICAL OPTICS; POPULATION STATISTICS;

DISCRETE SOURCES; GAIA SPECTRA; LOWER RESOLUTION; METHODS. DATA ANALYSIS; METHODS:STATISTICAL; PROBABILISTIC METHODS; PROBABILISTICS; QUASARS:GENERAL; RELATIVE FREQUENCIES; TRAINING DATA;

SURVEYS;

EID: 57649112237     PISSN: 00358711     EISSN: 13652966     Source Type: Journal    
DOI: 10.1111/j.1365-2966.2008.13983.x     Document Type: Article

References (28)

1. Bailer-Jones C. A. L., 2005, in Turon C., O'Flaherty K. S., Perryman M. A. C., eds, The Three-dimensional Universe with Gaia, SP-576. ESA Publications Division, Noordwijk, p. 393
2. Bailer-Jones C. A. L., Smith K. S., 2008a, Gaia DPAC Technical Note, GAIA-C8-TN-MPIA-CBJ-036 (http://www.mpia.de/GAIA/)
3. Bailer-Jones C. A. L., Smith K. S., 2008b, Gaia DPAC Technical Note, GAIA-C8-TN-MPIA-CBJ-037 (http://www.mpia.de/GAIA/)
4. Ball N. M., Brunner R. J., Myers A. D., Tcheng D., 2006, ApJ, 650, 497
5. Brown A. G. A., 2006, Gaia DPAC Technical Note, GAIA-CA-TN-LEI-AB-005
6. Burges C. J. C., 1998, Data Min. Knowl. Discov., 2, 121
7. Chang C.-C., Lin C.-J., 2001, LIBSVM: A Library for Support Vector Machines, Technical Note (http://www.csie.ntu.edu.tw/∼cjlin/libsvm)
8. Claeskens J.-F., Smette A., Vandenbulcke L., Surdej S., 2006, MNRAS, 367, 879
9. Cortes C., Vapnik V., 1995, Mach. Learn., 20, 273
10. Elting C., Bailer-Jones C. A. L., 2007, Gaia DPAC Technical Note, GAIA-C8-TN-MPIA-CE-001 (http://www.mpia.de/GAIA/)
11. Gao D., Zhang Y.-X., Zhao Y.-H., 2008, MNRAS, 386, 1417
12. Gustafsson B., Edvardsson B., Eriksson K., Jrgensen U. G., Nordlund A., Plez B., 2008, A&A, 486, 951
13. Hastie T., Tibshirani R., Friedman J., 2001, The Elements of Statistical Learning. Springer-Verlag, Berlin
14. Lejeune Th., Cuisinier F., Buser R., 1997, A&ASS, 125, 229
15. Luri X., Babusiaux C., Masana E., 2005, in Turon C., O'Flaherty K. S., Perryman M. A. C., eds, The Three-Dimensional Universe with Gaia, SP-576. ESA Publications Division, Noordwijk, p. 357
16. O'Mullane W., Lammers U., Bailer-Jones C. A. L. et al., 2007, in Shaw R. A., Hill F., Bell D. J., eds, ASP Conf. Ser. Vol. 376, Astronomical Data Analysis Software and Systems 16. Astron. Soc. Pac., San Francisco, p. 99
17. Platt J., 2000, in Smola A. J., Bartlett P., Schölkopf B., Schuurmans D., eds, Advances in Large Margin Classifiers. MIT Press, Cambridge, MA, p. 61 (http://citeseer.ist.psu.edu/platt99probabilistic.html)
18. Richards G. T. et al., 2002, AJ, 123, 2945
19. Richards G. T. et al., 2004, ApJS, 155, 257
20. Shin H., Cho S., 2003, Proc. Korean Data Mining Conference, How to Deal with Large Dataset, Class Imbalance and Binary Output in SVM based Response Model, p. 93
21. Sordo R., Vallenari A., 2008, Gaia DPAC Technical Note, GAIA-C8-DA-OAPD-RS-002
22. Suchkov A. A., Hanisch R. J., Margon B., 2005, AJ, 130, 2439
23. Tsalmantza P. et al., 2007, A&A, 470, 761
24. Tsalmantza P. et al., 2008, A&A, submitted
25. Vanden Berk D. E. et al., 2005, ApJ, 129, 2047
26. Visa S., Ralescu A., 2005, Proc. 16th MAICS, Issues in Mining Imbalanced Data Set-A Review Paper
27. Weiss G. M., 2004, Mining with Rarity: a Unifying Framework, SIGKDD Explorations, 6, 7
28. Wu T.-F., Lin C.-J., Weng R. C., 2004, J. Mach. Learn. Res., 5, 975