On machine-learned classification of variable stars with sparse and noisy time-series data

Astrophysical Journal

Volumn 733, Issue 1, 2011, Pages

  Richards, Joseph W ^a   Starr, Dan L ^a   Butler, Nathaniel R ^a   Bloom, Joshua S ^a   Brewer, John M ^b   Crellin Quick, Arien ^a   Higgins, Justin ^a   Kennedy, Rachel ^a   Rischard, Maxime ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b YALE UNIVERSITY   (United States)

Author keywords

methods: data analysis; methods: statistical; stars: variables: general; techniques: photometric

Indexed keywords

EID: 79956307645     PISSN: 0004637X     EISSN: 15384357     Source Type: Journal    
DOI: 10.1088/0004-637X/733/1/10     Document Type: Article

References (74)

1. Bailey, S., Aragon, C., Romano, R., Thomas, R. C., Weaver, B. A., & Wong, D. 2007, ApJ, 665, 1246
2. Ball, N. M., Brunner, R. J., Myers, A. D., & Tcheng, D. 2006, ApJ, 650, 497
3. Barning, F. J. M. 1963, Bull. Astron. Inst. Netherlands, 17, 22
4. Belokurov, V., Evans, N. W., & Du, Y. L. 2003, MNRAS, 341, 1373
5. Belokurov, V., Evans, N. W., & Le Du, Y. 2004, MNRAS, 352, 233
6. Blockeel, H., Schietgat, L., Struyf, J., Deroski, S., & Clare, A. 2006, Knowledge Discovery in Databases: PKDD 2006 (Berlin: Springer-Verlag)
7. Blomme, J. et al. 2010, ApJ, 713, L204
8. Breiman, L. 1996, Mach. Learn., 24, 123
9. Breiman, L. 2001, Mach. Learn., 45, 5
10. Breiman, L., Friedman, J., Olshen, R., & Stone, C. 1984, Classification and Regression Trees (Boca Raton, FL: Chapman & Hall/CRC)
11. Brett, D. R., West, R. G., & Wheatley, P. J. 2004, MNRAS, 353, 369
12. Briquet, M., Hubrig, S., De Cat, P., Aerts, C., North, P., & Schöller, M. 2007, A&A, 466, 269
13. Burman, P. 1989, Biometrika, 76, 503
14. Butler, N. R. & Bloom, J. S. 2011, AJ, 141, 93
15. Cesa-Bianchi, N., Gentile, C., & Zaniboni, L. 2006, J. Mach. Learn. Res., 7, 31
16. Cheeseman, P. & Stutz, J. 1996, Advances in Knowledge Discovery and Data Mining (Menlo Park, CA: American Association for Artificial Intelligence)
17. Clark, J. S., Ritchie, B. W., & Negueruela, I. 2010, A&A, 514, A87
18. Covey, K. R. et al. 2007, AJ, 134, 2398
19. Debosscher, J., Sarro, L. M., Aerts, C., Cuypers, J., Vandenbussche, B., Garrido, R., & Solano, E. 2007, A&A, 475, 1159
20. Derekas, A. et al. 2009, MNRAS, 394, 995
21. Doering, R. L., Meixner, M., Holfeltz, S. T., Krist, J. E., Ardila, D. R., Kamp, I., Clampin, M. C., & Lubow, S. H. 2007, AJ, 133, 2122
22. Eads, D. R., Williams, S. J., Theiler, J., Porter, R., Harvey, N. R., Perkins, S. J., Brumby, S. P., & David, N. A. 2004, Proc. SPIE, 200, 79
23. Eyer, L. 2005, The Three-Dimensional Universe with Gaia, ed. C. Turon, K. S. O'Flaherty, & M. A. C. Perryman (Noordwijk: ESA), 513, ESA-SP 576
24. Eyer, L. 2006, Astrophysics of Variable Stars (ASP Conf. Ser. 349), ed. C. Aerts & C. Sterken (San Francisco, CA: ASP), 15
25. Eyer, L. & Blake, C. 2005, MNRAS, 358, 30
26. Eyer, L. & Mowlavi, N. 2008, J. Phys. Conf. Ser., 118, 012010
27. Eyer, L. et al. 2008, Classification and Discovery in Large Astronomical Surveys (AIP Conf. Proc. 1082), ed. C. A. L. Bailer-Jones (Melville, NY: AIP), 257
28. Flores, A., Koenigsberger, G., Cardona, O., & de la Cruz, L. 2007, AJ, 133, 2859
29. Freund, Y. & Schapire, R. 1996, Machine Learning: Proc. Thirteenth Int. Conf. on Machine Learning, 148
30. Friedman, J. 1996, Technical Report, Department of Statistics, Stanford Univ.
31. Friedman, J. H. 2001, Ann. Stat., 29, 1189
32. Gregory, P. C. 2005, Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with "Mathematica" Support (Cambridge: Cambridge Univ. Press)
33. Grinin, V. P., Rostopchina, A. N., Barsunova, O. Y., & Demidova, T. V. 2010, Astrophysics, 53, 367
34. Hastie, T. & Tibshirani, R. 1998, Ann. Stat., 26, 451
35. Hastie, T., Tibshirani, R., & Friedman, J. 2009, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.; New York: Springer)
36. Horne, J. H. & Baliunas, S. L. 1986, ApJ, 302, 757
37. Ivezić,. et al. 2007, AJ, 134, 973
38. Kessler, R. et al. 2010, PASP, 122, 1415
39. Knerr, S. et al. 1990, Optim. Meth. Softw., 1, 23
40. Koen, C. & Eyer, L. 2002, MNRAS, 331, 45
41. Lomb, N. R. 1976, Ap&SS, 39, 447
42. LSST Science Collaborations et al. 2009, arXiv:0912.0201
43. Mahabal, A. et al. 2008, Astron. Nachr., 329, 288
44. Marchenko, S. V., Antokhin, I. I., Bertrand, J., Lamontagne, R., Moffat, A. F. J., Piceno, A., & Matthews, J. M. 1994, AJ, 108, 678
45. Millan-Gabet, R. & Monnier, J. D. 2002, ApJ, 580, L167
46. Moffat, A. F. J. et al. 2008, ApJ, 679, L45
47. O'Keefe, P. J., Gowanlock, M. G., McConnell, S. M., & Patton, D. 2009, Astronomical Data Analysis Software and Systems XVIII (ASP Conf. Ser. 411), ed. D. A. Bohlender, D. Durand, & P. Dowler (San Francisco, CA: ASP), 318
48. Paunzen, E. & Reegen, P. 2008, Commun. Asteroseismol., 153, 49
49. Perryman, M. et al. 1997, A&A, 323, L49
50. Press, W., Vetterling, W., Teukolsky, S., & Flannery, B. 2001, Numerical Recipes in C++: The Art of Scientific Computing (New York: Cambridge Univ. Press)
51. Protopapas, P., Giammarco, J. M., Faccioli, L., Struble, M. F., Dave, R., & Alcock, C. 2006, MNRAS, 369, 677
52. Quinlan, J. 1996, Proc. Thirteenth National Conf. on Artificial Intelligence (American Association for Artificial Intelligence), 725
53. Rebbapragada, U., Protopapas, P., Brodley, C. E., & Alcock, C. 2009, Astronomical Data Analysis Software and Systems XVIII (ASP Conf. Ser. 411), ed. D. A. Bohlender, D. Durand, & P. Dowler (San Francisco, CA: ASP), 264
54. Rodríguez, E., López-González, M. J., & López de Coca, P. 2000, A&AS, 144, 469
55. Sarro, L. M., Debosscher, J., López, M., & Aerts, C. 2009, A&A, 494, 739
56. Scargle, J. D. 1982, ApJ, 263, 835
57. Sesar, B. et al. 2007, AJ, 134, 2236
58. Shin, M., Sekora, M., & Byun, Y. 2009, MNRAS, 400, 1897
59. Silla, C. & Freitas, A. 2011, Data Mining Knowl. Discovery, 22, 31
60. Stankov, A. & Handler, G. 2005, ApJS, 158, 193
61. Stetson, P. B. 1996, PASP, 108, 851
62. Suchkov, A. A., Hanisch, R. J., & Margon, B. 2005, AJ, 130, 2439
63. Tsoumakas, G. & Katakis, I. 2007, Int. J. Data Warehousing Mining, 3, 1
64. Udalski, A., Soszynski, I., Szymanski, M., Kubiak, M., Pietrzynski, G., Wozniak, P., & Zebrun, K. 1999
65. Vapnik, V. 2000, The Nature of Statistical Learning Theory (New York: Springer)
66. Vens, C., Struyf, J., Schietgat, L., Deroski, S., & Blockeel, H. 2008, Mach. Learn., 73, 185
67. Walkowicz, L. M. et al. 2009, arXiv:0902.3981
68. Wasserman, L. 2006, All of Nonparametric Statistics (New York: Springer)
69. Whitelock, P. A., Feast, M. W., & van Leeuwen, F. 2008, MNRAS, 386, 313
70. Willemsen, P. G. & Eyer, L. 2007, arXiv:0712.2898
71. Woźniak, P. R., Williams, S. J., Vestrand, W. T., & Gupta, V. 2004, AJ, 128, 2965
72. Wu, T., Lin, C., & Weng, R. 2004, J. Mach. Learn. Res., 5, 975
73. Yankov, D., Keogh, E., & Rebbapragada, U. 2008, Knowl. Inf. Syst., 17, 241
74. Zechmeister, M. & Kürster, M. 2009, A&A, 496, 577