Open Access
Issue |
A&A
Volume 678, October 2023
|
|
---|---|---|
Article Number | A198 | |
Number of page(s) | 14 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202347074 | |
Published online | 25 October 2023 |
- Allers, K. N., Jaffe, D. T., Lacy, J. H., Draine, B. T., & Richter, M. J. 2005, ApJ, 630, 368 [NASA ADS] [CrossRef] [Google Scholar]
- Asensio Ramos, A., & Elitzur, M. 2018, A&A, 616, A131 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Asmussen, S., & Glynn, P. W. 2007, Stochastic Simulation: Algorithms andAnalysis, SMAP, 57 eds. B. Rozovskii, G. Grimmett, D. Dawson, et al. (New York, NY: Springer) [CrossRef] [Google Scholar]
- Behrens, E., Mangum, J. G., Holdship, J., et al. 2022, ApJ, 939, 119 [NASA ADS] [CrossRef] [Google Scholar]
- Bohlin, R. C., Savage, B. D., & Drake, J. F. 1978, ApJ, 224, 132 [Google Scholar]
- Bojanov, B. D., Hakopian, H. A., & Sahakian, A. A. 1993, Spline Functions andMultivariate Interpolations (Dordrecht: Springer Netherlands) [CrossRef] [Google Scholar]
- Brinch, C., & Hogerheijde, M. R. 2010, A&A, 523, A25 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Bron, E., Roueff, E., Gerin, M., et al. 2021, A&A, 645, A28 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Burton, M. G., Hollenbach, D. J., & Tielens, A. G. G. M. 1990, ApJ, 365, 620 [NASA ADS] [CrossRef] [Google Scholar]
- Chen, T., & Guestrin, C. 2016, in Proceedings of the 22nd ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining, 785 [Google Scholar]
- de Mijolla, D., Viti, S., Holdship, J., Manolopoulou, I., & Yates, J. 2019, A&A, 630, A117 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Dullemond, C. P., Juhasz, A., Pohl, A., et al. 2012, Astrophysics Source Code Library [record ascl:1202.015] [Google Scholar]
- Einig, L., Pety, J., Roueff, A., et al. 2023, A&A, 677, A158 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Fasshauer, G. E. 2007, Meshfree Approximation Methods with Matlab (Singapore: World Scientific) [CrossRef] [Google Scholar]
- Ferland, G. J., Chatzikos, M., Guzman, F., et al. 2017, Revista mexicana deastronomía y astrofísica, 53, 385 [Google Scholar]
- Fitzpatrick, E. L., & Massa, D. 2007, ApJ, 663, 320 [Google Scholar]
- Fluke, C. J., & Jacobs, C. 2020, WIREs Data Mining and Knowledge Discovery, 10, e1349 [CrossRef] [Google Scholar]
- Godard, B., Pineau des Forêts, G., Lesaffre, P., et al. 2019, A&A, 622, A100 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Goicoechea, J. R., & Le Bourlot, J. 2007, A&A, 467, 1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Goicoechea, J. R., Pety, J., Cuadrado, S., et al. 2016, Nature, 537, 207 [Google Scholar]
- Graff, P., Feroz, F., Hobson, M. P., & Lasenby, A. 2012, MNRAS, 421, 169 [NASA ADS] [Google Scholar]
- Graff, P., Feroz, F., Hobson, M. P., & Lasenby, A. 2014, MNRAS, 441, 1741 [NASA ADS] [CrossRef] [Google Scholar]
- Grassi, T., Krstic, P., Merlin, E., et al. 2011, A&A, 533, A123 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Grassi, T., Nauman, F., Ramsey, J. P., et al. 2022, A&A, 668, A139 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Gratier, P., Majumdar, L., Ohishi, M., et al. 2016, ApJS, 225, 25 [Google Scholar]
- Haber, S. 1966, Math. Comput., 20, 361 [CrossRef] [Google Scholar]
- He, K., Zhang, X., Ren, S., & Sun, J. 2016, in 2016 IEEE Conference onComputer Vision and Pattern Recognition (CVPR), 770 [CrossRef] [Google Scholar]
- Heays, A. N., Bosman, A. D., & van Dishoeck, E. F. 2017, A&A, 602, A105 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Holdship, J., Viti, S., Jiménez-Serra, I., Makrymallis, A., & Priestley, F. 2017, AJ, 154, 38 [NASA ADS] [CrossRef] [Google Scholar]
- Holdship, J., Jeffrey, N., Makrymallis, A., Viti, S., & Yates, J. 2018, ApJ, 866, 116 [NASA ADS] [CrossRef] [Google Scholar]
- Holdship, J., Viti, S., Haworth, T. J., & Ilee, J. D. 2021, A&A, 653, A76 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Hornik, K., Stinchcombe, M., & White, H. 1989, Neural Netw., 2, 359 [NASA ADS] [CrossRef] [Google Scholar]
- Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. 2017, in 2017 IEEEConference on Computer Vision and Pattern Recognition (CVPR), 2261 [CrossRef] [Google Scholar]
- Huertas-Company, M., Gravet, R., Cabrera-Vives, G., et al. 2015, ApJS, 221, 8 [NASA ADS] [CrossRef] [Google Scholar]
- Indriolo, N., Geballe, T. R., Oka, T., & McCall, B. J. 2007, ApJ, 671, 1736 [NASA ADS] [CrossRef] [Google Scholar]
- Joblin, C., Bron, E., Pinto, C., et al. 2018, A&A, 615, A129 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Jóhannesson, G., Ruiz de Austri, R., Vincent, A. C., et al. 2016, ApJ, 824, 16 [CrossRef] [Google Scholar]
- Keil, M., Viti, S., & Holdship, J. 2022, ApJ, 927, 203 [NASA ADS] [CrossRef] [Google Scholar]
- Kingma, D. P., & Ba, J. 2017, arXiv eprints [arXiv: 1412.6980] [Google Scholar]
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2017, Commun. ACM, 60, 84 [CrossRef] [Google Scholar]
- Le Petit, F., Roueff, E., & Herbst, E. 2004, A&A, 417, 993 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Le Petit, F., Nehmé, C., Le Bourlot, J., & Roueff, E. 2006, ApJS, 164, 506 [NASA ADS] [CrossRef] [Google Scholar]
- Lemaire, J. L., Field, D., Maillard, J. P., et al. 1999, A&A, 349, 253 [NASA ADS] [Google Scholar]
- Leshno, M., Lin, V. Y., Pinkus, A., & Schocken, S. 1993, Neural Netw., 6, 861 [CrossRef] [Google Scholar]
- Maffucci, D. M., Wenger, T. V., Le Gal, R., & Herbst, E. 2018, ApJ, 868, 41 [NASA ADS] [CrossRef] [Google Scholar]
- Makrymallis, A., & Viti, S. 2014, ApJ, 794, 45 [NASA ADS] [CrossRef] [Google Scholar]
- Marconi, A., Testi, L., Natta, A., & Walmsley, C. M. 1998, A&A, 330, 696 [NASA ADS] [Google Scholar]
- Mathis, J. S., Rumpl, W., & Nordsieck, K. H. 1977, ApJ, 217, 425 [Google Scholar]
- Mathis, J. S., Mezger, P. G., & Panagia, N. 1983, A&A, 128, 212 [NASA ADS] [Google Scholar]
- McCulloch, W. S., & Pitts, W. 1943, Bull. Math. Biophys., 5, 115 [CrossRef] [Google Scholar]
- McElroy, D., Walsh, C., Markwick, A. J., et al. 2013, A&A, 550, A36 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- McKay, M. D., Beckman, R. J., & Conover, W. J. 1979, Technometrics, 21, 239 [Google Scholar]
- Motulsky, H. J., & Brown, R. E. 2006, BMC Bioinformatics, 7, 123 [CrossRef] [Google Scholar]
- Nwankpa, C. E., Gachagan, A., & Marshall, S. 2021, 2nd InternationalConference on Computational Sciences and Technology (Jamshoro, Pakistan) [Google Scholar]
- Ostertagová, E. 2012, Procedia Eng., 48, 500 [CrossRef] [Google Scholar]
- Paszke, A., Gross, S., Chintala, S., et al. 2017, NeurIPS Autodiff Workshop [Google Scholar]
- Peek, J. E. G., & Burkhart, B. 2019, ApJ, 882, L12 [Google Scholar]
- Pety, J., Guzmán, V. V., Orkisz, J. H., et al. 2017, A&A, 599, A98 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Pinte, C., Ménard, F., Duchêne, G., et al. 2022, Astrophysics Source Code Library [record ascl:2207.023] [Google Scholar]
- Ramambason, L., Lebouteiller, V., Bik, A., et al. 2022, A&A, 667, A35 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Rasmussen, C. E., & Williams, C. K. I. 2006, Gaussian Processes for MachineLearning, Adaptive Computation and Machine Learning (Cambridge, Mass: MIT Press) [Google Scholar]
- Robert, C. P., & Casella, G. 2004, Monte Carlo Statistical Methods, Springer Texts in Statistics (New York, NY: Springer New York) [CrossRef] [Google Scholar]
- Röllig, M., & Ossenkopf-Okada, V. 2022, A&A, 664, A67 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Rousseeuw, P. J., & Leroy, A. M. 1987, Robust Regression and Outlier Detection, Wiley Series in Probability and Mathematical Statistics (New York: Wiley) [CrossRef] [Google Scholar]
- Rumelhart, D. E., Hinton, G. E., & Williams, R. J. 1986, Nature, 323, 533 [Google Scholar]
- Shalev-Shwartz, S., & Ben-David, S. 2014, Understanding Machine Learning:From Theory to Algorithms, 1st edn. (Cambridge University Press) [CrossRef] [Google Scholar]
- Shallue, C. J., & Vanderburg, A. 2018, AJ, 155, 94 [NASA ADS] [CrossRef] [Google Scholar]
- Sheffer, Y., & Wolfire, M. G. 2013, ApJ, 774, L14 [NASA ADS] [CrossRef] [Google Scholar]
- Sheffer, Y., Wolfire, M. G., Hollenbach, D. J., Kaufman, M. J., & Cordier, M. 2011, ApJ, 741, 45 [NASA ADS] [CrossRef] [Google Scholar]
- Smirnov-Pinchukov, G. V., Molyarova, T., Semenov, D. A., et al. 2022, A&A, 666, L8 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Sternberg, A., Le Petit, F., Roueff, E., & Le Bourlot, J. 2014, ApJ, 790, 10 [CrossRef] [Google Scholar]
- Sutherland, R., Dopita, M., Binette, L., & Groves, B. 2018, Astrophysics Source Code Library [record ascl:1807.005] [Google Scholar]
- Tieleman, T., & Hinton, G. 2012, Neural Netw. Mach. Learn. 4, 26 [Google Scholar]
- van der Tak, F. F. S., Black, J. H., Schöier, F. L., Jansen, D. J., & van Dishoeck, E. F. 2007, A&A, 468, 627 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Wakelam, V., Herbst, E., Loison, J. C., et al. 2012, ApJS, 199, 21 [Google Scholar]
- Wu, R., Bron, E., Onaka, T., et al. 2018, A&A, 618, A53 [NASA ADS] [CrossRef] [EDP Sciences] [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.