Open Access
Issue
A&A
Volume 669, January 2023
Article Number A152
Number of page(s) 18
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202244481
Published online 27 January 2023
  1. Baes, M., & Camps, P. 2015, Astron. Comput., 12, 33 [NASA ADS] [CrossRef] [Google Scholar]
  2. Baes, M., Fritz, J., Gadotti, D. A., et al. 2010, A&A, 518, A39 [Google Scholar]
  3. Behrens, C., Pallottini, A., Ferrara, A., Gallerani, S., & Vallini, L. 2018, MNRAS, 477, 552 [NASA ADS] [CrossRef] [Google Scholar]
  4. Boulais, A., et al. 2021, A&A, 647, A105 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  5. Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000 [NASA ADS] [CrossRef] [Google Scholar]
  6. Camps, P., & Baes, M. 2015, Astron. Comput., 9, 20 [Google Scholar]
  7. Camps, P., Kapoor, A. U., Trcka, A., et al. 2022, MNRAS, 512, 2728 [NASA ADS] [CrossRef] [Google Scholar]
  8. Collins, J. D., Hart, G. C., Haselman, T. K., & Kennedy, B. 1974, AIAA J., 12, 185 [NASA ADS] [CrossRef] [Google Scholar]
  9. Crain, R. A., Schaye, J., Bower, R. G., et al. 2015, MNRAS, 450, 1937 [NASA ADS] [CrossRef] [Google Scholar]
  10. De Geyter, G., Baes, M., De Looze, I., et al. 2015, MNRAS, 451, 1728 [NASA ADS] [CrossRef] [Google Scholar]
  11. De Looze, I., Baes, M., Bendo, G. J., et al. 2012a, MNRAS, 427, 2797 [NASA ADS] [CrossRef] [Google Scholar]
  12. De Looze, I., Baes, M., Fritz, J., & Verstappen, J. 2012b, MNRAS, 419, 895 [NASA ADS] [CrossRef] [Google Scholar]
  13. Deeley, S., Drinkwater, M. J., Sweet, S. M., et al. 2021, MNRAS, 508, 895 [NASA ADS] [CrossRef] [Google Scholar]
  14. Di Mascia, F., Gallerani, S., Ferrara, A., et al. 2021, MNRAS, 506, 3946 [NASA ADS] [CrossRef] [Google Scholar]
  15. Dudzeviciute, U., Smail, I., Swinbank, A. M., et al. 2020, MNRAS, 494, 3828 [NASA ADS] [CrossRef] [Google Scholar]
  16. Font, A. S., McCarthy, I. G., Poole-Mckenzie, R., et al. 2020, MNRAS, 498, 1765 [NASA ADS] [CrossRef] [Google Scholar]
  17. Genel, S., Vogelsberger, M., Springel, V., et al. 2014, MNRAS, 445, 175 [Google Scholar]
  18. Gómez-Rubio, V. 2021, Bayesian Inference with INLA (Boca Raton, FL: Chapman & Hall/CRC Press) [Google Scholar]
  19. Gong, W., Reich, B. J., & Chang, H. H. 2021, Environ. Res. Commun., 3, 101002 [NASA ADS] [CrossRef] [Google Scholar]
  20. González-Gaitán, S., de Souza, R. S., Krone-Martins, A., et al. 2019, MNRAS, 482, 3880 [CrossRef] [Google Scholar]
  21. Grand, R. J. J., Gómez, F. A., Marinacci, F., et al. 2017, MNRAS, 467, 179 [NASA ADS] [Google Scholar]
  22. Groves, B., Dopita, M. A., Sutherland, R. S., et al. 2008, ApJS, 176, 438 [NASA ADS] [CrossRef] [Google Scholar]
  23. Hotelling, H. 1933, J. Educ. Psychol., 24, 417 [CrossRef] [Google Scholar]
  24. Jaffé, R., Nunes, S., Filipe Dos Santos, J., et al. 2021, Environ. Res. Lett., 16, 084034 [CrossRef] [Google Scholar]
  25. Jolliffe, I. T., & Cadima, J. 2016, Philos. Trans. Roy. Soc. London A, 374, 20150202 [NASA ADS] [Google Scholar]
  26. Jones, A. P., Köhler, M., Ysard, N., Bocchio, M., & Verstraete, L. 2017, A&A, 602, A46 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  27. Kapoor, A. U., Camps, R., Baes, M., et al. 2021, MNRAS, 506, 5703 [NASA ADS] [CrossRef] [Google Scholar]
  28. Krone-Martins, A., & Moitinho, A. 2014, A&A, 561, A57 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  29. Lee, D., & Seung, H. 1999, Nature, 401, 788 [NASA ADS] [CrossRef] [Google Scholar]
  30. Logan, C. H. A., & Fotopoulou, S. 2020, A&A, 633, A154 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  31. Noebauer, U. M., & Sim, S. A. 2019, Living Rev. Comput. Astrophys., 5, 1 [CrossRef] [Google Scholar]
  32. Pearson, K. 1901, London Edinburgh Dublin Philos. Mag. J. Sci., 2, 559 [CrossRef] [Google Scholar]
  33. Pillepich, A., Springel, V., Nelson, D., et al. 2018, MNRAS, 473, 4077 [Google Scholar]
  34. Ren, B., et al. 2018, ApJ, 852, 104 [NASA ADS] [CrossRef] [Google Scholar]
  35. Rino-Silvestre, J., González-Gaitán, S., Stalevski, M., et al. 2022, Neural Comput & Applic (2022), https://doi.org/1S.1S07/sSS521-S22-S8S71-x [Google Scholar]
  36. Rowland, B. W., Rushton, S. P., Shirley, M. D. F., Brown, M. A., & Budge, G. E. 2021, Sci. Rep., 11, 21953 [NASA ADS] [CrossRef] [Google Scholar]
  37. Rue, H., Martino, S., & Chopin, N. 2009, J. Roy. Stat. Soci. B (Stat. Methodol.), 71, 319 [CrossRef] [Google Scholar]
  38. Rue, H., Riebler, A., Sørbye, S. H., et al. 2017, Annu. Rev. Stat. Applic., 4, 395 [NASA ADS] [CrossRef] [Google Scholar]
  39. Schaye, J., Crain, R. A., Bower, R. G., et al. 2015, MNRAS, 446, 521 [Google Scholar]
  40. Springel, V. 2010, MNRAS, 401, 791 [Google Scholar]
  41. Stalevski, M., Ricci, C., Ueda, Y., et al. 2016, MNRAS, 458, 2288 [Google Scholar]
  42. Stalevski, M., Asmus, D., & Tristram, K. R. W. 2017, MNRAS, 472, 3854 [NASA ADS] [CrossRef] [Google Scholar]
  43. Stalevski, M., Tristram, K. R. W., & Asmus, D. 2019, MNRAS, 484, 3334 [NASA ADS] [CrossRef] [Google Scholar]
  44. Steinacker, J., Baes, M., & Gordon, K. D. 2013, ARA&A, 51, 63 [CrossRef] [Google Scholar]
  45. Tandon, R., & Sra, S. 2010, Sparse nonnegative matrix approximation: new formulations and algorithms, Tech. Rep. 193, Max Planck Institute for Biological Cybernetics, Tübingen, Germany [Google Scholar]
  46. Van Niekerk, J., Bakka, H., Rue, H., & Schenk, O. 2021, J. Stat. Softw., 100, 1 [CrossRef] [Google Scholar]
  47. Vijayan, A. P., Wilkins, S. M., Lovell, C. C., et al. 2022, MNRAS, 511, 4999 [NASA ADS] [CrossRef] [Google Scholar]
  48. Vogelsberger, M., Genel, S., Sijacki, D., et al. 2013, MNRAS, 436, 3031 [Google Scholar]
  49. Vogelsberger, M., Genel, S., Springel, V., et al. 2014a, Nature, 509, 177 [Google Scholar]
  50. Vogelsberger, M., Genel, S., Springel, V., et al. 2014b, MNRAS, 444, 1518 [Google Scholar]
  51. Weinberger, R., Springel, V., Hernquist, L., et al. 2017, MNRAS, 465, 3291 [Google Scholar]
  52. Whitney, A., Ferreira, L., Conselice, C. J., & Duncan, K. 2021, ApJ, 919, 139 [NASA ADS] [CrossRef] [Google Scholar]
  53. Zanella, A., Pallottini, A., Ferrara, A., et al. 2021, MNRAS, 500, 118 [Google Scholar]

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