Open Access
Issue
A&A
Volume 634, February 2020
Article Number A48
Number of page(s) 24
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/201936345
Published online 05 February 2020
  1. Abadi, M., Barham, P., Chen, J., et al. 2016, in Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16), 16, 265 [Google Scholar]
  2. Autry, R. G., Probst, R. G., Starr, B. M., et al. 2003, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, eds. M. Iye, & A. F. M. Moorwood, 4841, 525 [NASA ADS] [CrossRef] [Google Scholar]
  3. Badrinarayanan, V., Kendall, A., & Cipolla, R. 2015, ArXiv e-prints [arXiv:1511.00561] [Google Scholar]
  4. Badrinarayanan, V., Kendall, A., & Cipolla, R. 2017, IEEE Trans. Pattern Anal. Mach. Intell., 39, 2481 [CrossRef] [Google Scholar]
  5. Bailer-Jones, C. A., Smith, K., Tiede, C., Sordo, R., & Vallenari, A. 2008, MNRAS, 391, 1838 [NASA ADS] [CrossRef] [Google Scholar]
  6. Bektešević, D., Vinković, D., Rasmussen, A., & Ivezić, Ž. 2018, MNRAS, 474, 4837 [NASA ADS] [CrossRef] [Google Scholar]
  7. Bellm, E. C., Kulkarni, S. R., Graham, M. J., et al. 2019, PASP, 131, 018002 [NASA ADS] [CrossRef] [Google Scholar]
  8. Bertin, E. 2009, Mem. Soc. Astron. It., 80, 422 [NASA ADS] [Google Scholar]
  9. Bertin, E. 2013, Astrophysics Source Code Library [record ascl:1301.001] [Google Scholar]
  10. Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  11. Bosch, J., Armstrong, R., Bickerton, S., et al. 2018, PASJ, 70, S5 [NASA ADS] [CrossRef] [Google Scholar]
  12. Boulade, O., Charlot, X., Abbon, P., et al. 2003, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, eds. M. Iye, & A. F. M. Moorwood, 4841, 72 [NASA ADS] [CrossRef] [Google Scholar]
  13. Bouy, H., Bertin, E., Moraux, E., et al. 2013, A&A, 554, A101 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  14. Casali, M., Adamson, A., Alves de Oliveira, C., et al. 2007, A&A, 467, 777 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  15. Cuillandre, J. C., Luppino, G. A., Starr, B. M., & Isani, S. 2000, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, eds. M. Iye, & A. F. Moorwood, 4008, 1010 [NASA ADS] [CrossRef] [Google Scholar]
  16. Dalton, G. B., Caldwell, M., Ward, A. K., et al. 2006, Proc. SPIE, 6269, 62690X [CrossRef] [Google Scholar]
  17. Flaugher, B. L., Abbott, T. M. C., Annis, J., et al. 2010, in Ground-based and Airborne Instrumentation for Astronomy III, Proc. SPIE, 7735, 77350D [CrossRef] [Google Scholar]
  18. Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., & Garcia-Rodriguez, J. 2017, ArXiv e-prints [arXiv:1704.06857] [Google Scholar]
  19. Griffin, M. J., Abergel, A., Abreu, A., et al. 2010, A&A, 518, L3 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  20. Hampshire, II, J. B., & Pearlmutter, B. 1991, Connectionist Models (Elsevier), 159 [CrossRef] [Google Scholar]
  21. Ienaka, N., Kawara, K., Matsuoka, Y., et al. 2013, ApJ, 767, 80 [NASA ADS] [CrossRef] [Google Scholar]
  22. Ives, D. 1998, IEEE Spectrum, 16, 20 [NASA ADS] [Google Scholar]
  23. Kawanomoto, S., Komiyama, Y., & Yagi, M. 2016a, in Subaru Users’ Meeting FY2016 [Google Scholar]
  24. Kawanomoto, Y., Yagi, M., & Kawanomoto, S. 2016b, in Subaru Users’ Meeting FY2016 [Google Scholar]
  25. Kingma, D. P., & Ba, J. 2014, ArXiv e-prints [arXiv:1412.6980] [Google Scholar]
  26. Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2012, in Advances in Neural Information Processing Systems, 1097 [Google Scholar]
  27. Kuijken, K., Bender, R., Cappellaro, E., et al. 2002, The Messenger, 110, 15 [NASA ADS] [Google Scholar]
  28. LeCun, Y., & Bengio, Y. 1995, The Handbook of Brain Theory and Neural Networks (Cambridge: MIT Press), 3361 [Google Scholar]
  29. Long, J., Shelhamer, E., & Darrell, T. 2015, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3431 [Google Scholar]
  30. Long, K. S., Baggett, S. M., & MacKenty, J. W. 2015, Persistence in the WFC3 IR Detector: an Improved Model Incorporating the Effects of Exposure Time, Tech. rep. [Google Scholar]
  31. Lowe, D. G. 1999, ICCV’99: Proceedings of the International Conference on Computer Vision, 1150 [Google Scholar]
  32. Magnier, E. A., & Cuillandre, J.-C. 2004, PASP, 116, 449 [NASA ADS] [CrossRef] [Google Scholar]
  33. Matthews, B. W. 1975, Biochimica et Biophysica Acta (BBA)-Protein Structure, 405, 442 [CrossRef] [Google Scholar]
  34. McCully, C., Crawford, S., Kovacs, G., et al. 2018, https://doi.org/10.5281/zenodo.1482019 [Google Scholar]
  35. Melchior, P., Sheldon, E., Drlica-Wagner, A., et al. 2016, Astron. Comput., 16, 99 [NASA ADS] [CrossRef] [Google Scholar]
  36. Metzger, M. R., Luppino, G. A., & Miyazaki, S. 1995, Bull. Am. Astron. Soc., 27, 1389 [Google Scholar]
  37. Miville-Deschênes, M.-A., Duc, P.-A., Marleau, F., et al. 2016, A&A, 593, A4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  38. Miyazaki, S., Komiyama, Y., Kawanomoto, S., et al. 2018, PASJ, 70, S1 [NASA ADS] [Google Scholar]
  39. Morganson, E., Gruendl, R. A., Menanteau, F., et al. 2018, PASP, 130, 074501 [NASA ADS] [CrossRef] [Google Scholar]
  40. Nir, G., Zackay, B., & Ofek, E. O. 2018, AJ, 156, 229 [NASA ADS] [CrossRef] [Google Scholar]
  41. Ordénovic, C., Surace, C., Torrésani, B., & Llébaria, A. 2008, Stat. Methodol., 5, 373 [NASA ADS] [CrossRef] [Google Scholar]
  42. Pilbratt, G. L., Riedinger, J. R., Passvogel, T., et al. 2010, A&A, 518, L1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  43. Rheault, J. P., Mondrik, N. P., DePoy, D. L., Marshall, J. L., & Suntzeff, N. B. 2014, Spectrophotometric Calibration of the Swope and duPont Telescopes for the Carnegie Supernova Project 2 [Google Scholar]
  44. Richard, M. D., & Lippmann, R. P. 1991, Neural Comput., 3, 461 [CrossRef] [Google Scholar]
  45. Rojas, R. 1996, Neural Comput., 8, 41 [CrossRef] [Google Scholar]
  46. Rubinstein, R. 1999, Methodol. Comput. Appl. Probab., 1, 127 [CrossRef] [Google Scholar]
  47. Ruder, S. 2016, ArXiv e-prints [arXiv:1609.04747] [Google Scholar]
  48. Saerens, M., Latinne, P., & Decaestecker, C. 2002, Neural Comput., 14, 21 [CrossRef] [Google Scholar]
  49. Simonyan, K., & Zisserman, A. 2014, ArXiv e-prints [arXiv:1409.1556] [Google Scholar]
  50. Szegedy, C., Liu, W., Jia, Y., et al. 2015, ArXiv e-prints [arXiv:1409.4842] [Google Scholar]
  51. Valdes, F., Gruendl, R., & DES Project 2014, in Astronomical Data Analysis Software and Systems XXIII, eds. N. Manset, & P. Forshay, ASP Conf. Ser., 485, 379 [NASA ADS] [Google Scholar]
  52. van Dokkum, P. G. 2001, PASP, 113, 1420 [NASA ADS] [CrossRef] [Google Scholar]
  53. Vandame, B. 2002, in Astronomical Data Analysis II, eds. J. L. Starck, & F. D. Murtagh, SPIE Conf. Ser., 4847, 123 [Google Scholar]
  54. Williams, C. K. I. 1998, in Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond, ed. M. I. Jordan (Dordrecht: Springer), 599 [Google Scholar]
  55. Wolfe, T., Armandroff, T., Blouke, M. M., et al. 2000, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, eds. M. M. Blouke, N. Sampat, G. M. Williams, & T. Yeh, 3965, 80 [NASA ADS] [Google Scholar]
  56. Yang, T., Wu, Y., Zhao, J., & Guan, L. 2018, Cognit. Syst. Res., 53, 20 [CrossRef] [Google Scholar]

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