Free Access
Issue
A&A
Volume 645, January 2021
Article Number A107
Number of page(s) 30
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/201936561
Published online 22 January 2021
  1. Abbott, T., Abdalla, F., Allam, S., et al. 2018, ApJS, 239, 18 [Google Scholar]
  2. Adelman-McCarthy, J. K., Agüeros, M. A., Allam, S. S., et al. 2006, ApJS, 162, 38 [NASA ADS] [CrossRef] [Google Scholar]
  3. Akhlaghi, M., & Ichikawa, T. 2015, ApJS, 220, 1 [NASA ADS] [CrossRef] [Google Scholar]
  4. Amiaux, J., Scaramella, R., Mellier, Y., et al. 2012, in Space Telescopes and Instrumentation 2012: Optical, Infrared, and Millimeter Wave, Int. Soc. Opt. Photon., 8442, 84420Z [CrossRef] [Google Scholar]
  5. Astropy Collaboration (Robitaille, T. P., et al.) 2013, A&A, 558, A33 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  6. Astropy Collaboration (Price-Whelan, A. M., et al.) 2018, AJ, 156, 123 [Google Scholar]
  7. Baillard, A., Bertin, E., De Lapparent, V., et al. 2011, A&A, 532, A74 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  8. Beard, S., MacGillivray, H., & Thanisch, P. 1990, MNRAS, 247, 311 [Google Scholar]
  9. Beckwith, S. V., Stiavelli, M., Koekemoer, A. M., et al. 2006, AJ, 132, 1729 [NASA ADS] [CrossRef] [Google Scholar]
  10. Bertin, E. 2006, Automatic Astrometric and Photometric Calibration with SCAMP (San Francisco: Astronomical Society of the Pacific) [Google Scholar]
  11. Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  12. Beucher, S. 1982, ICASSP ’82. IEEE International Conference on Acoustics, Speech, and Signal Processing, 7 [Google Scholar]
  13. Borlaff, A., Trujillo, I., Román, J., et al. 2019, A&A, 621, A133 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  14. Boucaud, A., Heneka, C., Ishida, E. E., et al. 2020, MNRAS, 491, 2481 [NASA ADS] [CrossRef] [Google Scholar]
  15. Bouwens, R. J., Illingworth, G. D., Franx, M., & Ford, H. 2008, ApJ, 686, 230 [NASA ADS] [CrossRef] [Google Scholar]
  16. Bouwens, R., Illingworth, G., Franx, M., et al. 2009, ApJ, 705, 936 [NASA ADS] [CrossRef] [Google Scholar]
  17. Carlinet, E., & Géraud, T. 2014, IEEE Trans. Image Process., 23, 3885 [CrossRef] [Google Scholar]
  18. Fliri, J., & Trujillo, I. 2016, MNRAS, 456, 1359 [NASA ADS] [CrossRef] [Google Scholar]
  19. GNU Astronomy Utilities 2019, NoiseChisel Optimization, https://www.gnu.org/software/gnuastro/manual/html_node/NoiseChisel-optimization.html [Google Scholar]
  20. Goodman, A. A., Rosolowsky, E. W., Borkin, M. A., et al. 2009, Nature, 457, 63 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  21. Houlahan, P., & Scalo, J. 1992, ApJ, 393, 172 [NASA ADS] [CrossRef] [Google Scholar]
  22. Huang, S., Leauthaud, A., Murata, R., et al. 2018, PASP, 70, S6 [Google Scholar]
  23. Iodice, E., Capaccioli, M., Grado, A., et al. 2016, ApJ, 820, 42 [NASA ADS] [CrossRef] [Google Scholar]
  24. Iodice, E., Spavone, M., Capaccioli, M., et al. 2019, A&A, 623, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  25. Ivezić, Z., Tyson, J., Abel, B., et al. 2019, ApJ, 873, 111 [NASA ADS] [CrossRef] [Google Scholar]
  26. Jones, D. R., Schonlau, M., & Welch, W. J. 1998, J. Global Optim., 13, 455 [CrossRef] [Google Scholar]
  27. Levine, M. D., & Nazif, A. 1981, An Experimental Rule-based System for Testing Low Level Segmentation Strategies (McGill University) [Google Scholar]
  28. Masias, M., Freixenet, J., Lladó, X., & Peracaula, M. 2012, MNRAS, 422, 1674 [NASA ADS] [CrossRef] [Google Scholar]
  29. Melchior, P., Moolekamp, F., Jerdee, M., et al. 2018, Astron. Comput., 24, 129 [CrossRef] [Google Scholar]
  30. Moschini, U., Meijster, A., & Wilkinson, M. H. F. 2018, IEEE Trans. Pattern Anal. Mach. Intell., 40, 513 [CrossRef] [Google Scholar]
  31. Oesch, P., Bouwens, R. J., Carollo, C. M., et al. 2009, ApJ, 709, L21 [NASA ADS] [CrossRef] [Google Scholar]
  32. Ouzounis, G. K., & Wilkinson, M. H. 2010, IEEE Trans. Pattern Anal. Mach. Intell., 33, 224 [CrossRef] [Google Scholar]
  33. Pal, N. R., & Pal, S. K. 1993, Pattern Recognit., 26, 1277 [CrossRef] [Google Scholar]
  34. Pratt, N. 1977, Vistas Astron., 21, 1 [CrossRef] [Google Scholar]
  35. Prole, D. J., Davies, J. I., Keenan, O. C., & Davies, L. J. 2018, MNRAS, 478, 667 [NASA ADS] [CrossRef] [Google Scholar]
  36. Reiman, D. M., & Göhre, B. E. 2019, MNRAS, 485, 2617 [CrossRef] [Google Scholar]
  37. Rix, H.-W., Barden, M., Beckwith, S. V., et al. 2004, ApJS, 152, 163 [NASA ADS] [CrossRef] [Google Scholar]
  38. Robitaille, T., Beaumont, C., McDonald, B., & Rosolowsky, E. 2013, Astrodendro, A Python Package to Compute Dendrograms of Astronomical Data, http://www.dendrograms.org [Google Scholar]
  39. Robotham, A., Davies, L., Driver, S., et al. 2018, MNRAS, 476, 3137 [NASA ADS] [CrossRef] [Google Scholar]
  40. Roerdink, J. B. T. M., & Meijster, A. 2000, Fundam. Inf., 41, 187 [CrossRef] [Google Scholar]
  41. Román, J., & Trujillo, I. 2017a, MNRAS, 468, 703 [NASA ADS] [CrossRef] [Google Scholar]
  42. Román, J., & Trujillo, I. 2017b, MNRAS, 468, 4039 [NASA ADS] [CrossRef] [Google Scholar]
  43. Román, J., & Trujillo, I. 2018, Res. Notes Am. Astron. Soc., 2, 144 [NASA ADS] [CrossRef] [Google Scholar]
  44. Román, J., Trujillo, I., & Montes, M. 2020, A&A, 644, A42 [CrossRef] [EDP Sciences] [Google Scholar]
  45. Rosolowsky, E., Pineda, J., Kauffmann, J., & Goodman, A. 2008, ApJ, 679, 1338 [NASA ADS] [CrossRef] [Google Scholar]
  46. Salembier, P., & Wilkinson, M. H. F. 2009, IEEE Signal Process. Mag., 26, 136 [CrossRef] [Google Scholar]
  47. Salembier, P., Oliveras, A., & Garrido, L. 1998, IEEE Trans. Image Process., 7, 555 [CrossRef] [Google Scholar]
  48. Sersic, J. L. 1968, Atlas de Galaxias Australes (Cordoba, Argentina: Observatorio Astronomico) [Google Scholar]
  49. Simet, M., & Mandelbaum, R. 2015, MNRAS, 449, 1259 [NASA ADS] [CrossRef] [Google Scholar]
  50. Teeninga, P., Moschini, U., Trager, S. C., & Wilkinson, M. H. F. 2013, 11th International Conference “Pattern Recognition and Image Analysis: New Information Technologies” (PRIA-11-2013), IPSI RAS, 746 [Google Scholar]
  51. Teeninga, P., Moschini, U., Trager, S. C., & Wilkinson, M. H. F. 2016, Mathematical Morphology – Theory and Applications, 1, 100 [Google Scholar]
  52. The GPyOpt authors 1968, GPyOpt: A Bayesian Optimization Framework in Python, http://github.com/SheffieldML/GPyOpt [Google Scholar]
  53. Van Dokkum, P. G., Abraham, R., Merritt, A., et al. 2015, ApJ, 798, L45 [NASA ADS] [CrossRef] [Google Scholar]
  54. Venhola, A. 2019, PhD Thesis, University of Groningen [Google Scholar]
  55. Venhola, A., Peletier, R., Laurikainen, E., et al. 2017, A&A, 608, A142 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  56. Venhola, A., Peletier, R., Laurikainen, E., et al. 2018, A&A, 620, A165 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  57. Venhola, A., Peletier, R., Laurikainen, E., et al. 2019, A&A, 625, A143 [EDP Sciences] [Google Scholar]
  58. Wilkinson, M. H. F. 1998, in Digital Image Analysis of Microbes, eds. M. H. F. Wilkinson, & F. Schut (Chichester, UK: John Wiley and Sons, Ltd), 135 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.