Contents Previous

REFERENCES

  1. Agnello, A. 2017, MNRAS, 471, 2013
  2. Alibert, Y. 2019, arXiv e-prints, arXiv:1901.09719
  3. Anders, F., Chiappini, C., Santiago, B. X., et al. 2018, A&A, 619, A125
  4. Armstrong, D. J., Pollacco, D., & Santerne, A. 2017, MNRAS, 465, 2634
  5. Armstrong, D. J., Kirk, J., Lam, K. W. F., et al. 2016, MNRAS, 456, 2260
  6. Ascasibar, Y., & Sánchez Almeida, J. 2011, MNRAS, 415, 2417
  7. Bailey, S. 2012, PASP, 124, 1015
  8. Balazs, L. G., Garibjanyan, A. T., Mirzoyan, L. V., et al. 1996, A&A, 311, 145
  9. Ball, N. M., & Brunner, R. J. 2010, International Journal of Modern Physics D, 19, 1049
  10. Banerji, M., Lahav, O., Lintott, C. J., et al. 2010, MNRAS, 406, 342
  11. Baron, D., & Poznanski, D. 2017, MNRAS, 465, 4530
  12. Baron, D., Poznanski, D., Watson, D., et al. 2015, MNRAS, 451, 332
  13. Bellm, E. 2014, in The Third Hot-wiring the Transient Universe Workshop, ed. P. R. Wozniak, M. J. Graham, A. A. Mahabal, & R. Seaman, 27–33
  14. Bilicki, M., Hoekstra, H., Brown, M. J. I., et al. 2018, A&A, 616, A69
  15. Blake, C., Collister, A., Bridle, S., & Lahav, O. 2007, MNRAS, 374, 1527
  16. Bloom, J. S., Richards, J. W., Nugent, P. E., et al. 2012, PASP, 124, 1175
  17. Boroson, T. A., & Green, R. F. 1992, ApJS, 80, 109
  18. Breiman, L. 2001, Machine Learning, 45, 5. http://dx.doi.org/10.1023/A:1010933404324
  19. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. 1984, Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software
  20. Brescia, M., Cavuoti, S., D’Abrusco, R., Longo, G., & Mercurio, A. 2013, ApJ, 772, 140
  21. Brescia, M., Cavuoti, S., Longo, G., & De Stefano, V. 2014, A&A, 568, A126
  22. Brescia, M., Cavuoti, S., Paolillo, M., Longo, G., & Puzia, T. 2012, MNRAS, 421, 1155
  23. Carliles, S., Budavári, T., Heinis, S., Priebe, C., & Szalay, A. S. 2010, ApJ, 712, 511
  24. Carrasco Kind, M., & Brunner, R. J. 2014, MNRAS, 438, 3409
  25. Castro, N., Protopapas, P., & Pichara, K. 2018, AJ, 155, 16
  26. Collister, A. A., & Lahav, O. 2004, PASP, 116, 345
  27. Connolly, A. J., Szalay, A. S., Bershady, M. A., Kinney, A. L., & Calzetti, D. 1995, AJ, 110, 1071
  28. D’Abrusco, R., Fabbiano, G., Djorgovski, G., et al. 2012, ApJ, 755, 92
  29. D’Abrusco, R., Longo, G., & Walton, N. A. 2009, MNRAS, 396, 223
  30. Daniel, S. F., Connolly, A., Schneider, J., Vanderplas, J., & Xiong, L. 2011, AJ, 142, 203
  31. Das, P., & Sanders, J. L. 2019, MNRAS, 484, 294
  32. de Souza, R. S., & Ciardi, B. 2015, Astronomy and Computing, 12, 100
  33. de Souza, R. S., Dantas, M. L. L., Costa-Duarte, M. V., et al. 2017, MNRAS, 472, 2808
  34. Delli Veneri, M., Cavuoti, S., Brescia, M., Longo, G., & Riccio, G. 2019, arXiv e-prints, arXiv:1902.02522
  35. Dewdney, P. E., Hall, P. J., Schilizzi, R. T., & Lazio, T. J. L. W. 2009, IEEE Proceedings, 97, 1482
  36. D’Isanto, A., Cavuoti, S., Brescia, M., et al. 2016, MNRAS, 457, 3119
  37. D’Isanto, A., Cavuoti, S., Gieseke, F., & Polsterer, K. L. 2018, A&A, 616, A97
  38. D’Isanto, A., & Polsterer, K. L. 2018, A&A, 609, A111
  39. Djorgovski, S. 1995, ApJ, 438, L29
  40. Djorgovski, S. G., Graham, M. J., Donalek, C., et al. 2016, arXiv e-prints, arXiv:1601.04385
  41. Donalek, C., Arun Kumar, A., Djorgovski, S. G., et al. 2013, arXiv e-prints, arXiv:1310.1976
  42. Eatough, R. P., Molkenthin, N., Kramer, M., et al. 2010, MNRAS, 407, 2443
  43. Ellison, S. L., Teimoorinia, H., Rosario, D. J., & Mendel, J. T. 2016, MNRAS, 458, L34
  44. Fadely, R., Hogg, D. W., & Willman, B. 2012, ApJ, 760, 15
  45. Firth, A. E., Lahav, O., & Somerville, R. S. 2003, MNRAS, 339, 1195
  46. Freund, Y., & Schapire, R. E. 1997, J. Comput. Syst. Sci., 55, 119. http://dx.doi.org/10.1006/jcss.1997.1504
  47. Fustes, D., Manteiga, M., Dafonte, C., et al. 2013, ArXiv e-prints, arXiv:1309.2418
  48. Gaia Collaboration, Prusti, T., de Bruijne, J. H. J., et al. 2016, A&A, 595, A1
  49. Galluccio, L., Michel, O., Bendjoya, P., & Slezak, E. 2008, in American Institute of Physics Conference Series, Vol. 1082, American Institute of Physics Conference Series, ed. C. A. L. Bailer-Jones, 165–171
  50. Garcia-Dias, R., Allende Prieto, C., Sánchez Almeida, J., & Ordovás-Pascual, I. 2018, A&A, 612, A98
  51. Gianniotis, N., Kügler, D., Tino, P., Polsterer, K., & Misra, R. 2015, arXiv e-prints, arXiv:1505.00936
  52. Gianniotis, N., Kügler, S. D., Tiňo, P., & Polsterer, K. L. 2016, ArXiv e-prints, arXiv:1601.05654
  53. Giles, D., & Walkowicz, L. 2019, MNRAS, 484, 834
  54. Hartley, P., Flamary, R., Jackson, N., Tagore, A. S., & Metcalf, R. B. 2017, MNRAS, 471, 3378
  55. Hertzsprung, E. 1909, Astronomische Nachrichten, 179, 373
  56. Hocking, A., Geach, J. E., Davey, N., & Sun, Y. 2015, ArXiv e-prints, arXiv:1507.01589
  57. Hocking, A., Geach, J. E., Sun, Y., & Davey, N. 2018, MNRAS, 473, 1108
  58. Hojnacki, S. M., Kastner, J. H., Micela, G., Feigelson, E. D., & LaLonde, S. M. 2007, ApJ, 659, 585
  59. Huertas-Company, M., Rouan, D., Tasca, L., Soucail, G., & Le Fèvre, O. 2008, A&A, 478, 971
  60. Huertas-Company, M., Primack, J. R., Dekel, A., et al. 2018, ApJ, 858, 114
  61. Hui, J., Aragon, M., Cui, X., & Flegal, J. M. 2018, MNRAS, 475, 4494
  62. Hunter, J. D. 2007, Computing In Science & Engineering, 9, 90
  63. Hyvärinen, A., & Oja, E. 2000, Neural Netw., 13, 411. http://dx.doi.org/10.1016/S0893-6080(00)00026-5
  64. Ishida, E. E. O., Beck, R., González-Gaitán, S., et al. 2019, MNRAS, 483, 2
  65. Ivezić, Ž., Connolly, A., Vanderplas, J., & Gray, A. 2014, Statistics, Data Mining and Machine Learning in Astronomy (Princeton University Press)
  66. Ivezic, Z., Tyson, J. A., Abel, B., et al. 2008, ArXiv, arXiv:0805.2366
  67. Jones, E., Oliphant, T., Peterson, P., et al. 2001–, SciPy: Open source scientific tools for Python, [Online; accessed <today>]. http://www.scipy.org/
  68. Kaiser, N., Burgett, W., Chambers, K., et al. 2010, in Proc. SPIE, Vol. 7733, Ground-based and Airborne Telescopes III, 77330E
  69. Kohonen, T. 1982, Biological Cybernetics, 43, 59. https://doi.org/10.1007/BF00337288
  70. Kovács, A., & Szapudi, I. 2015, MNRAS, 448, 1305
  71. Krakowski, T., Małek, K., Bilicki, M., et al. 2016, A&A, 596, A39
  72. Krone-Martins, A., Ishida, E. E. O., & de Souza, R. S. 2014, MNRAS, 443, L34
  73. Krone-Martins, A., & Moitinho, A. 2014, A&A, 561, A57
  74. Krone-Martins, A., Delchambre, L., Wertz, O., et al. 2018, A&A, 616, L11
  75. Ksoll, V. F., Gouliermis, D. A., Klessen, R. S., et al. 2018, MNRAS, 479, 2389
  76. Kuncheva, L. I., & Whitaker, C. J. 2003, Machine Learning, 51, 181. https://doi.org/10.1023/A:1022859003006
  77. Laurino, O., D’Abrusco, R., Longo, G., & Riccio, G. 2011, MNRAS, 418, 2165
  78. Levi, M., Bebek, C., Beers, T., et al. 2013, ArXiv, arXiv:1308.0847
  79. Liu, F. T., Ting, K. M., & Zhou, Z. H. 2008, IEEE, 413
  80. Lochner, M., McEwen, J. D., Peiris, H. V., Lahav, O., & Winter, M. K. 2016, ArXiv e-prints, arXiv:1603.00882
  81. Ma, R., Angryk, R. A., Riley, P., & Filali Boubrahimi, S. 2018a, ApJS, 236, 14
  82. Ma, Z., Xu, H., Zhu, J., et al. 2018b, arXiv e-prints, arXiv:1812.07190
  83. MacQueen, J. B. 1967, in Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, ed. L. M. L. Cam & J. Neyman, Vol. 1 (University of California Press), 281–297
  84. Mahabal, A., Sheth, K., Gieseke, F., et al. 2017, ArXiv e-prints, arXiv:1709.06257
  85. Mahabal, A., Djorgovski, S. G., Turmon, M., et al. 2008, Astronomische Nachrichten, 329, 288
  86. Mahabal, A., Rebbapragada, U., Walters, R., et al. 2019, PASP, 131, 038002
  87. Małek, K., Solarz, A., Pollo, A., et al. 2013, A&A, 557, A16
  88. Masci, F. J., Hoffman, D. I., Grillmair, C. J., & Cutri, R. M. 2014, AJ, 148, 21
  89. McInnes, L., Healy, J., & Melville, J. 2018, arXiv e-prints, arXiv:1802.03426
  90. Meusinger, H., Brünecke, J., Schalldach, P., & in der Au, A. 2017, A&A, 597, A134
  91. Meusinger, H., Schalldach, P., Scholz, R.-D., et al. 2012, A&A, 541, A77
  92. Miller, A. A. 2015, ApJ, 811, 30
  93. Miller, A. A., Kulkarni, M. K., Cao, Y., et al. 2017, AJ, 153, 73
  94. Möller, A., Ruhlmann-Kleider, V., Leloup, C., et al. 2016, JCAP, 12, 008
  95. Morales-Luis, A. B., Sánchez Almeida, J., Aguerri, J. A. L., & Muñoz-Tuñón, C. 2011, ApJ, 743, 77
  96. Moreno, J., Vogeley, M. S., & Richards, G. 2019, in American Astronomical Society Meeting Abstracts, Vol. 233, American Astronomical Society Meeting Abstracts #233, 431.02
  97. Nakoneczny, S., Bilicki, M., Solarz, A., et al. 2018, arXiv e-prints, arXiv:1812.03084
  98. Naul, B., Bloom, J. S., Pérez, F., & van der Walt, S. 2018, Nature Astronomy, 2, 151
  99. Norris, R. P. 2017a, PASA, 34, e007
  100. —. 2017b, Nature Astronomy, 1, 671
  101. Norris, R. P., Salvato, M., Longo, G., et al. 2019, arXiv e-prints, arXiv:1902.05188
  102. Nun, I., Protopapas, P., Sim, B., & Chen, W. 2016, The Astronomical Journal, 152, 71. http://stacks.iop.org/1538-3881/152/i=3/a=71
  103. Paatero, P., & Tapper, U. 1994, Environmetrics, 5, 111
  104. Parks, D., Prochaska, J. X., Dong, S., & Cai, Z. 2018, MNRAS, 476, 1151
  105. Pashchenko, I. N., Sokolovsky, K. V., & Gavras, P. 2018, MNRAS, 475, 2326
  106. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, Journal of Machine Learning Research, 12, 2825
  107. Pérez, F., & Granger, B. E. 2007, Computing in Science and Engineering, 9, 21. http://ipython.org
  108. Pesenson, M. Z., Pesenson, I. Z., & McCollum, B. 2010, Advances in Astronomy, 2010, 350891
  109. Peth, M. A., Lotz, J. M., Freeman, P. E., et al. 2016, MNRAS, 458, 963
  110. Pichara, K., & Protopapas, P. 2013, ApJ, 777, 83
  111. Pichara, K., Protopapas, P., Kim, D.-W., Marquette, J.-B., & Tisserand, P. 2012, MNRAS, 427, 1284
  112. Plewa, P. M. 2018, MNRAS, 476, 3974
  113. Polsterer, K., Gieseke, F., Igel, C., Doser, B., & Gianniotis, N. 2016, Parallelized rotation and flipping INvariant Kohonen maps (PINK) on GPUs
  114. Protopapas, P., Giammarco, J. M., Faccioli, L., et al. 2006, MNRAS, 369, 677
  115. Qu, M., Shih, F. Y., Jing, J., & Wang, H. 2003, SoPh, 217, 157
  116. Rahmani, S., Teimoorinia, H., & Barmby, P. 2018, MNRAS, 478, 4416
  117. Re Fiorentin, P., Bailer-Jones, C. A. L., Lee, Y. S., et al. 2007, A&A, 467, 1373
  118. Reis, I., Baron, D., & Shahaf, S. 2019, AJ, 157, 16
  119. Reis, I., Poznanski, D., Baron, D., Zasowski, G., & Shahaf, S. 2018a, MNRAS, 476, 2117
  120. Reis, I., Poznanski, D., & Hall, P. B. 2018b, MNRAS, 480, 3889
  121. Richards, J. W., Homrighausen, D., Freeman, P. E., Schafer, C. M., & Poznanski, D. 2012, MNRAS, 419, 1121
  122. Rogers, B., Ferreras, I., Lahav, O., et al. 2007, in Astronomical Society of the Pacific Conference Series, Vol. 371, Statistical Challenges in Modern Astronomy IV, ed. G. J. Babu & E. D. Feigelson, 431
  123. Russell, H. N. 1914, Popular Astronomy, 22, 275
  124. Sánchez Almeida, J., Aguerri, J. A. L., Muñoz-Tuñón, C., & de Vicente, A. 2010, ApJ, 714, 487
  125. Sánchez Almeida, J., & Allende Prieto, C. 2013, ApJ, 763, 50
  126. Schawinski, K., Turp, D., & Zhang, C. 2018, in American Astronomical Society Meeting Abstracts, Vol. 231, American Astronomical Society Meeting Abstracts #231, 309.01
  127. Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. 1999, in Proceedings of the 12th International Conference on Neural Information Processing Systems, NIPS’99 (Cambridge, MA, USA: MIT Press), 582–588. http://dl.acm.org/citation.cfm?id=3009657.3009740
  128. Segal, G., Parkinson, D., Norris, R. P., & Swan, J. 2018, arXiv e-prints, arXiv:1805.10718
  129. Shi, T., & Horvath, S. 2006, Journal of Computational and Graphical Statistics, 15, 118
  130. Simpson, J. D., Cottrell, P. L., & Worley, C. C. 2012, MNRAS, 427, 1153
  131. Singh, H. P., Gulati, R. K., & Gupta, R. 1998, MNRAS, 295, 312
  132. Snider, S., Allende Prieto, C., von Hippel, T., et al. 2001, ApJ, 562, 528
  133. Solarz, A., Bilicki, M., Gromadzki, M., et al. 2017, A&A, 606, A39
  134. Storrie-Lombardi, M. C., Lahav, O., Sodre, L., J., & Storrie-Lombardi, L. J. 1992, MNRAS, 259, 8P
  135. Tagliaferri, R., Longo, G., Andreon, S., et al. 2003, Lecture Notes in Computer Science, 2859, 226
  136. Teimoorinia, H., Bluck, A. F. L., & Ellison, S. L. 2016, MNRAS, 457, 2086
  137. van der Maaten, L., & Hinton, G. 2008, Journal of Machine Learning Research, 9, 2579. http://www.jmlr.org/papers/v9/vandermaaten08a.html
  138. Vanden Berk, D. E., Shen, J., Yip, C.-W., et al. 2006, AJ, 131, 84
  139. Vanderplas, J., & Connolly, A. 2009, AJ, 138, 1365
  140. Vanderplas, J., Connolly, A., Ivezić, Ž., & Gray, A. 2012, in Conference on Intelligent Data Understanding (CIDU), 47 –54
  141. Vanzella, E., Cristiani, S., Fontana, A., et al. 2004, A&A, 423, 761
  142. Vasconcellos, E. C., de Carvalho, R. R., Gal, R. R., et al. 2011, AJ, 141, 189
  143. Vazdekis, A., Sánchez-Blázquez, P., Falcón-Barroso, J., et al. 2010, MNRAS, 404, 1639
  144. Ward, J. H. 1963, Journal of the American Statistical Association, 58, 236. https://www.tandfonline.com/doi/abs/10.1080/01621459.1963.10500845
  145. Weaver, W. B., & Torres-Dodgen, A. V. 1995, ApJ, 446, 300
  146. Wright, D. E., Smartt, S. J., Smith, K. W., et al. 2015, MNRAS, 449, 451
  147. Yang, T., & Li, X. 2015, MNRAS, 452, 158
  148. Yong, S. Y., King, A. L., Webster, R. L., et al. 2018, ArXiv e-prints, arXiv:1806.07090
  149. York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579
  150. Zhang, J.-n., Wu, F.-c., Luo, A.-l., & Zhao, Y.-h. 2006, Chinese Astronomy and Astrophysics, 30, 176
  151. Zucker, S., & Giryes, R. 2018, AJ, 155, 147

Contents Previous