Robust machine learning applied to astronomical data sets. II. Quantifying photometric redshifts for quasars using instance-based learning

Astrophysical Journal

Volumn 663, Issue 2 I, 2007, Pages 774-780

  Ball, Nicholas M ^a   Brunner, Robert J ^a   Myers, Adam D ^a   Strand, Natalie E ^b   Alberts, Stacey L ^a   Tcheng, David ^a   Llorà, Xavier ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b University of Illinois at Urbana Champaign   (United States)

Author keywords

Catalogs; Cosmology: miscellaneous; Methods: data analysis; Quasars: general

Indexed keywords

EID: 34547433063     PISSN: 0004637X     EISSN: 15384357     Source Type: Journal    
DOI: 10.1086/518362     Document Type: Article

References (60)

1. Abazajian, K., et al. 2004, AJ, 128, 502
2. Aha, D. W., Kibler, D., & Albert, M. K. 1991, Machine Learning, 6, 37
3. Babbedge, T. S. R., et al. 2004, MNRAS, 353, 654
4. Ball, N. M., Brunner, R. J., Myers, A. D., & Tcheng, D. 2006, ApJ, 650, 497
5. Ball, N. M., Loveday, J., Fukugita, M., Nakamura, O., Okamura, S., Brinkmann, J., & Brunner, R. J. 2004, MNRAS, 348, 1038
6. Baum, W. A. 1962, in IAU Symp. 15, Problems of Extra-Galactic Research, ed. G. C. McVittie (New York: Macmillan), 390
7. Beckwith, S. V. W., et al. 2006, AJ, 132, 1729
8. Benítez, N. 2000, ApJ, 536, 571
9. Brunner, R. J., Connolly, A. J., Szalay, A. S., & Bershady, M. A. 1997, ApJ, 482, L21
10. Brunner, R. J., Szalay, A. S., & Connolly, A. J. 2000, ApJ, 541, 527
11. Budavári, T., et al. 2001, AJ, 122, 1163
12. Cannon, R., et al. 2006, MNRAS, 372, 425
13. Cardelli, J. A., Clayton, G. C., & Mathis, J. S. 1989, ApJ, 345, 245
14. Coe, D., Benitez, N., Sánchez, S. F., Jee, M., Bouwens, R., & Ford, H. 2006, AJ, 132, 926
15. Collister, A., et al. 2007, MNRAS, 375, 68
16. Collister, A. A., & Lahav, O. 2004, PASP, 116, 345
17. Connolly, A. J., Szalay, A. S., & Brunner, R. J. 1998, ApJ, 499, L125
18. Cover, T. M., & Hart, P. E. 1967, IEEE Trans. Inf. Theory, 13, 21
19. Croom, S. M., Smith, R. J., Boyle, B. J., Shanks, T., Miller, L., Outram, P. J., & Loaring, N. S. 2004, MNRAS, 349, 1397
20. Csabai, I., et al. 2003, AJ, 125, 580
21. Eisenstein, D. J., et al. 2001, AJ, 122, 2267
22. Firth, A. E., Lahav, O., & Somerville, R. S. 2003, MNRAS, 339, 1195
23. Goldberg, D. E. 1989, Genetic Algorithms in Search, Optimization, and Machine Learning (Reading: Addison Wesley)
24. _. 2002, Design of Innovation: Lessons from and for Competent Genetic Algorithms (Boston: Kluwer)
25. Gwyn, S. D. J., & Hartwick, F. D. A. 1996, ApJ, 468, L77
26. Hastie, T., Tibshirani, R., & Friedman, J. 2001, The Elements of Statistical Learning (Springer)
27. Haupt, R. L., & Haupt, S. E. 1998, Practical Genetic Algorithms (New York: Wiley)
28. Hogg, D. W., et al. 1998, AJ, 115, 1418
29. Holland, J. H. 1975, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence (Ann Arbor: Univ. Michigan Press)
30. Koo, D. C. 1985, AJ, 90, 418
31. Lanzetta, K. M., Yahil, A., & Fernandez-Soto, A. 1996, Nature, 381, 759
32. Lawrence, A., et al. 2006, MNRAS, submitted (astro-ph/0604426)
33. Loh, E. D., & Spillar, E. J. 1986, ApJ, 303, 154
34. Martin, D. C., et al. 2005, ApJ, 619, L1
35. Mobasher, B., Rowan-Robinson, M., Georgakakis, A., & Eaton, N. 1996, MNRAS, 282, L7
36. Myers, A. D., Brunner, R. J., Nichol, R. C., Richards, G. T., Schneider, D. P., & Bahcall, N. A. 2007a, ApJ, 658, 85
37. Myers, A. D., Brunner, R. J., Richards, G. T., Nichol, R. C., Schneider, D. P., & Bahcall, N. A. 2007b, ApJ, 658, 99
38. Myers, A. D., et al. 2006, ApJ, 638, 622
39. Padmanabhan, N., et al. 2005, MNRAS, 359, 237
40. Richards, G. T., et al. 2001, AJ, 122, 1151
41. Sawicki, M. J., Lin, H., & Yee, H. K. C. 1997, AJ, 113, 1
42. Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525
43. Skrutskie, M. F., et al. 2006, AJ, 131, 1163
44. Stoughton, C., et al. 2002, AJ, 123, 485
45. Tagliaferri, R., Longo, G., Andreon, S., Capozziello, S., Donalek, C., & Giordanoet, G. 2002, preprint (astro-ph/0203445)
46. Vanden Berk, D. E., et al. 2005, AJ, 129, 2047
47. Varizella, E., et al. 2004, A&A, 423, 761
48. Wadadekar, Y. 2005, PASP, 117, 79
49. Wang, Y., Baheall, N., & Turner, E. L. 1998, AJ, 116, 2081
50. Way, M. J., & Srivastava, A. N. 2006, ApJ, 647, 102
51. Weinstein, M. A., et al. 2004, ApJS, 155, 243 (W04)
52. Welge, M., et al. 2003, Data to Knowledge (Urbana: NCSA), http://alg.ncsa.uiuc.edu/do/tools/d2k.
53. Werner, M. W., et al. 2004, ApJS, 154, 1
54. Williams, R. E., et al. 1996, AJ, 112, 1335
55. _. 2000, AJ, 120, 2735
56. Witten, I. H., & Frank, E. 2000, Data Mining (San Francisco: Morgan Kaufmann)
57. Wolf, C., Wisotzki, L., Borch, A., Dye, S., Kleinheinrich, M., & Meisenheimer, K. 2003, A&A, 408, 499
58. Wolf, C., et al. 2004, A&A, 421, 913
59. Wu, X.-B., Zhang, W., & Zhou, X. 2004, Chinese, J. Astron. Astrophys., 4, 17
60. York, D. G., et al. 2000, AJ, 120, 1579