New approaches to photometric redshift prediction via gaussian process regression in the sloan digital SKY survey

Astrophysical Journal

Volumn 706, Issue 1, 2009, Pages 623-636

  Way, M J ^a,c,d   Foster, L V ^b   Gazis, P R ^c   Srivastava, A N ^c  

^a NASA GODDARD INSTITUTE FOR SPACE STUDIES   (United States)

^b SAN JOSE STATE UNIVERSITY   (United States)

^c NASA AMES RESEARCH CENTER   (United States)

^d MS 269 4 ^*  (Sweden)

Author keywords

Galaxies: Distances and redshifts; Methods: Statistical

Indexed keywords

EID: 70549107063     PISSN: 0004637X     EISSN: 15384357     Source Type: Journal    
DOI: 10.1088/0004-637X/706/1/623     Document Type: Article

References (42)

1. Abazajian, K. et al. 2003, AJ, 126, 2081
2. Abazajian, K. et al. 2004, AJ, 128, 502
3. Abazajian, K. et al. 2005, AJ, 129, 1755
4. Adelman-McCarthy, J. K. et al. 2007, ApJS, 172, 634
5. Ball, N. M., Brunner, R. J., Myers, A. D., Strand, N. E., Alberts, S. L., & Tcheng, D. 2008, ApJ, 683, 12
6. Bernstein, G. & Huterer, D. 2009, arXiv:0909.2782v1
7. Blanton, M. R. et al. 2005, AJ, 129, 2562
8. Carliles, S. et al. 2008, Astronomical Data Analysis Software and Systems (ASP Conf. Ser. 394), ed. R. W. Argyle, P. S. Bunclark, & J. R. Lewis (San Francisco, CA: ASP), 521
9. Collister, A. A. & Lahav, O. 2004, PASP, 116, 345
10. Csabai, I. et al. 2003, AJ, 125, 580
11. D'Abrusco, R., Staiano, A., Giuseppe, L., Brescia, M., Paolillo, M., De Filippis, E., & Tagliaferri, R. 2007, ApJ, 663, 752
12. Efron, B. & Tibshirani, R. J. 1993, An Introduction to the Bootstrap (New York: Chapman & Hall)
13. Eisenstein, et al. 2001, AJ, 122, 2267
14. Fine, S. & Scheinberg, K. 2001, J. Mach. Learn. Res., 2, 243
15. Foster, L. et al. 2009, J. Mach. Learn. Res., 10, 857
16. Giavalisco, M. et al. 2004, ApJ, 600, L93
17. Golub, G. H. & Van Loan, C. F. 1996, Matrix Computations, 3rd ed. (Baltimore, MD, USA: Johns Hopkins Univ. Press)
18. Ivezic, Z. et al. 2008, arXiv:0805.2366v1
19. Kaczmarczik, M. C., Richards, G. T., Mehta, S. S., & Schlegel, D. J. 2009, AJ, 138, 19
20. Kaiser, N. et al. 2002, Proc. SPIE, 4836, 154
21. Kurtz, M. J. et al. 2007, AJ, 134, 1360
22. Le Fèvre, O. et al. 2004, A&A, 417, 839
23. Martin, D. C. et al. 2005, ApJ, 619, L1
24. Neal, R. M. 1996, Bayesian Learning for Neural Networks (New York: Springer)
25. Padmanabhan, N. et al. 2005, MNRAS, 359, 327
26. Petrosian, V. 1976, ApJ, 209, L1
27. Poggio, T. & Girosi, F. 1990, Proc. IEEE, 78, 1481
28. Rasmussen, C. E. & Williams, C. K. I. 2006, Gaussian Processes for Machine Learning (Cambridge MA: MIT Press)
29. Seeger, M., Williams, C., & Lawrence, N. D. 2003, Proc. 9th Int. Workshop on Artificial Intelligence and Statistics, Fast Forward Selection to Speed Up Sparse Gaussian Process Regression, ed. C. M. Bishop & B. J. Frey (San Francisco, CA: Morgan Kaufmann)
30. Sérsic, J. L. 1968, Atlas de Galaxias Australes (Cordoba, Argentina: Observatorio Astronomico)
31. Skrutskie, M. F. et al. 2006, AJ, 131, 1163
32. Stabenau, H. F., Connolly, A., & Bhuvnesh, J. 2008, MNRAS, 387, 1215
33. Stoughton, C. et al. 2002, AJ, 123, 485
34. Strauss, M. A. et al. 2002, AJ, 124, 1810
35. Suchkov, A. A., Hanisch, R. J., & Margon, B. 2005, AJ, 130, 2439
36. Wadadekar, Y. 2005, PASP, 117, 79
37. Wang, D., Zhang, Y., Liu, C., & Zhao, Y. 2008, ChJAA, 8, 119
38. Wang, T., Huang, J., & Gu, Q. 2009, Res. Astron. Astrophys., 9, 390
39. Way, M. J. & Srivastava, A. N. 2006, ApJ, 647, 102
40. Wahba, G. 1990, Spline Models for Observational Data (Philadelphia, PA: SIAM)
41. Wray, J. J. & Gunn, J. E. 2008, ApJ, 678, 144
42. York, D. G. et al. 2000, AJ, 120, 1579