Detecting unresolved binary stars in Euclid VIS images

T. Kuntzer; F. Courbin

doi:10.1051/0004-6361/201730792

Free Access

Issue		A&A Volume 606, October 2017


Article Number		A119
Number of page(s)		10
Section		Numerical methods and codes
DOI		https://doi.org/10.1051/0004-6361/201730792
Published online		23 October 2017

Abt, H. A., & Levy, S. G. 1976, ApJS, 30, 273 [NASA ADS] [CrossRef] [Google Scholar]
Antilogus, P., Astier, P., Doherty, P., Guyonnet, A., & Regnault, N. 2014, Journal of Instrumentation, 9, C03048 [CrossRef] [Google Scholar]
Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
Bernstein, G. M., & Jarvis, M. 2002, AJ, 123, 583 [NASA ADS] [CrossRef] [Google Scholar]
Bishop, C. M. 1995, Neural Networks for Pattern Recognition (New York, NY, USA: Oxford University Press, Inc.) [Google Scholar]
Breiman, L. 2001, Machine Learning, 45, 5 [CrossRef] [Google Scholar]
Cropper, M., Hoekstra, H., Kitching, T., et al. 2013, MNRAS, 431, 3103 [NASA ADS] [CrossRef] [Google Scholar]
Cropper, M., Pottinger, S., Niemi, S., et al. 2016, VIS: the visible imager for Euclid, 9904, 16 [Google Scholar]
Cummings, J. D., Geisler, D., Villanova, S., & Carraro, G. 2014, AJ, 148, 27 [NASA ADS] [CrossRef] [Google Scholar]
de Bruijne, J. 2012, Astrophys. Space Sci., 341, 31 [NASA ADS] [CrossRef] [Google Scholar]
Deacon, N. R., Magnier, E. A., Best, W. M. J., et al. 2017, MNRAS [Google Scholar]
Desai, S., Mohr, J. J., Bertin, E., Kümmel, M., & Wetzstein, M. 2016, Astron. Comput., 16, 67 [NASA ADS] [CrossRef] [Google Scholar]
Duchêne, G., & Kraus, A. 2013, ARA&A, 51, 269 [NASA ADS] [CrossRef] [Google Scholar]
Duquennoy, A., Mayor, M., & Halbwachs, J.-L. 1991, A&AS, 88, 281 [Google Scholar]
Duvet, L., Gondoin, P., & Saavedra Criado, G. 2010, Euclid Science Requirements Document, Tech. Rep. issue 4, revision 0, ESA, dEM-SA-Dc-00001 [Google Scholar]
Eyer, L., Rimoldini, L., Holl, B., et al. 2015, in Living Together: Planets, Host Stars and Binaries, eds. S. M. Rucinski, G. Torres, & M. Zejda, ASP Conf. Ser., 496, 121 [Google Scholar]
Gaia Collaboration (Prusti, T., et al.) 2016, A&A, 595, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. 2004, ACM Trans. Inf. Syst., 22, 5 [CrossRef] [EDP Sciences] [Google Scholar]
Hirata, C., & Seljak, U. 2003, MNRAS, 343, 459 [NASA ADS] [CrossRef] [Google Scholar]
Hoekstra, H., Wu, Y., & Udalski, A. 2005, ApJ, 626, 1070 [NASA ADS] [CrossRef] [Google Scholar]
Hunter, J. 2007, Comp. Sci. Eng., 9, 90 [NASA ADS] [CrossRef] [Google Scholar]
Jones, E., Oliphant, T., Peterson, P., et al. 2001, SciPy: Open source scientific tools for Python, http://www.scipy.org/ [Google Scholar]
Kaiser, N., Squires, G., & Broadhurst, T. 1995, ApJ, 449, 460 [NASA ADS] [CrossRef] [Google Scholar]
Kleinbaum, D., & Klein, M. 2010, Logistic Regression: A Self-Learning Text (New York: Springer-Verlag) [Google Scholar]
Kuntzer, T., Courbin, F., & Meylan, G. 2016a, A&A, 586, A74 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
Kuntzer, T., Tewes, M., & Courbin, F. 2016b, A&A, 591, A54 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, Euclid Study Report [arXiv:1110.3193] [Google Scholar]
Marcy, G. W., & Benitz, K. J. 1989, ApJ, 344, 441 [NASA ADS] [CrossRef] [Google Scholar]
Massey, R., Hoekstra, H., Kitching, T., et al. 2013, MNRAS, 429, 661 [NASA ADS] [CrossRef] [Google Scholar]
Milone, A. P., Piotto, G., Bedin, L. R., et al. 2012, ApJ, 744, 58 [NASA ADS] [CrossRef] [Google Scholar]
Nardiello, D., Milone, A. P., Piotto, G., et al. 2015, A&A, 573, A70 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
Ngolè Mboula, F., Starck, J.-L., Ronayette, S., Okumura, K., & Amiaux, J. 2015, A&A, 575, A86 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
Nissen, S. 2003, Report, Department of Computer Science University of Copenhagen (DIKU), 31 [Google Scholar]
Paulin-Henriksson, S., Amara, A., Voigt, L., Refregier, A., & Bridle, S. L. 2008, A&A, 484, 67 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
Racca, G. D., Laureijs, R., Stagnaro, L., et al. 2016, The Euclid mission design, Proc. SPIE, 9904, 23 [Google Scholar]
Raghavan, D., McAlister, H. A., Henry, T. J., et al. 2010, ApJSS, 190, 1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
Riddle, R. L., Tokovinin, A., Mason, B. D., et al. 2015, ApJ, 799, 4 [NASA ADS] [CrossRef] [Google Scholar]
Robin, A. C., Reylé, C., Derrière, S., & Picaud, S. 2003, A&A, 409, 523 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
Rodriguez, D. R., Duchêne, G., Tom, H., et al. 2015, MNRAS, 449, 3160 [NASA ADS] [CrossRef] [Google Scholar]
Rowe, B., Jarvis, M., Mandelbaum, R., et al. 2015, Astronomy and Computing, 10, 121 [NASA ADS] [CrossRef] [Google Scholar]
Schneider, P. 2006, Gravitational Lensing: Strong, Weak and Micro, Saas-Fee Advanced Course (Springer), 33, 269 [NASA ADS] [CrossRef] [Google Scholar]
Semboloni, E., Hoekstra, H., Huang, Z., et al. 2013, MNRAS, 432, 2385 [NASA ADS] [CrossRef] [Google Scholar]
Spergel, D., Gehrels, N., Baltay, C., et al. 2015, ArXiv e-prints [arXiv:1503.03757] [Google Scholar]
Terziev, E., Law, N. M., Arcavi, I., et al. 2013, ApJSS, 206, 18 [NASA ADS] [CrossRef] [Google Scholar]
van der Walt, S., Colbert, S., & Varoquaux, G. 2011, Comp. Sci. Eng., 13, 22 [CrossRef] [Google Scholar]
Voigt, L. M., Bridle, S. L., Amara, A., et al. 2012, MNRAS, 421, 1385 [NASA ADS] [CrossRef] [Google Scholar]

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