Large cosmological datasets have been probing the properties of our Universe and constraining the parameters of dark matter and dark energy with increasing precision. Deep learning techniques have shown potential to be smarter than — and greatly outperform — human-designed statistics.
References
Ribli, D., Pataki, A. B. & Csabai, I. Nat. Astron. https://doi.org/10.1038/s41550-018-0596-8 (2018).
Kilbinger, M. Rep. Prog. Phys. 78, 086901 (2015).
Kitching, T. D. et al. Mon. Not. R. Astron. Soc. 442, 1326–1349 (2014).
Hildebrandt, H. et al. Mon. Not. R. Astron. Soc. 465, 1454–1498 (2017).
Abbott, T. M. C. et al. Preprint at https://arXiv.org/abs/1708.01530 (2017).
Liu, J. et al. Phys. Rev. D 91, 063507 (2015).
Kacprzak, T. et al. Mon. Not. R. Astron. Soc. 463, 3653–3673 (2016).
Martinet, N. et al. Mon. Not. R. Astron. Soc. 474, 712–730 (2018).
Krizhevsky, A., Sutskever, I. & Hinton, G. E. in Advances in Neural Information Processing Systems (eds Pereira, F. et al.) 1097–1105 (Curran Associates, Red Hook, 2012).
Ravanbakhsh, S. et al. Proc. Mach. Learn. Res. 48, 2407–2416 (2016).
Schmelzle, J. et al. Preprint at https://arXiv.org/abs/arXiv:1707.05167 (2017).
Gupta, A., Zorrilla, J. M., Hsu, D. & Haiman, Z. Phys. Rev. D 97, 103515 (2018).
Marian, L., Smith, R. E., Hilbert, S. & Schneider, P. Mon. Not. R. Astron. Soc. 432, 1338–1350 (2013).
Fluri, J. et al. Preprint at https://arXiv.org/abs/1807.08732 (2018).
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Haiman, Z. Learning from the machine. Nat Astron 3, 18–19 (2019). https://doi.org/10.1038/s41550-018-0623-9
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DOI: https://doi.org/10.1038/s41550-018-0623-9
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