Built on

D. Ribli, B. Á. Pataki, J. M. Zorrilla Matilla, D. Hsu, Z. Haiman, and I. Csabai, MNRAS 490
Original
1902
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B. Kim, J. Seo, S. Jeon, J. Koo, J. Choe, and T. Jeon, arXiv e-prints , arXiv:1902.04893 (2019), arXiv:1902.04893 [cs.LG]
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1902
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T. Lu and Z. Haiman, MNRAS 490
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1905
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J. Fluri, T. Kacprzak, A. Lucchi, A. Refregier, A. Amara, T. Hofmann, and A. Schneider, Phys. Rev. D 100
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1906
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W. R. Coulton, J. Liu, I. G. McCarthy, and K. Osato, MNRAS (2020), 10.1093/mnras/staa1098 , arXiv:1910.04171 [astro-ph.CO]
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J. Schmalzing, M. Kerscher, and T. Buchert, in Dark Matter in the Universe , edited by S. Bonometto, J. R. Primack, and A. Provenzale (1996) p. 281, arXiv:astro-ph/9508154 [astro-ph]
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A. Refregier, A. Amara, T. D. Kitching, et al. , arXiv e-prints (2010), arXiv:1001.0061 [astro-ph.IM]
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V. Nair and G. E. Hinton, in Proceedings of the 27th international conference on machine learning (ICML-10) (2010) pp. 807–814
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K. Simonyan, A. Vedaldi, and A. Zisserman, arXiv e-prints , arXiv:1312.6034 (2013), arXiv:1312.6034 [cs.CV]
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Later among the works it cites.
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Later among the works it cites.
J. Fluri, T. Kacprzak, A. Refregier, A. Amara, A. Lucchi, and T. Hofmann, Phys. Rev. D 98
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Later among the works it cites.
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2020

Interpreting deep learning models for weak lensing

Matilla, José Manuel Zorrilla, Sharma, Manasi, Hsu, Daniel et al.

Understand

Deep Neural Networks (DNNs) are powerful algorithms that have been proven capable of extracting non-Gaussian information from weak lensing (WL) data sets.

Understanding which features in the data determine the output of these nested, non-linear algorithms is an important but challenging task.
We analyze a DNN that has been found in previous work to accurately recover cosmological parameters in simulated maps of the WL convergence ($\kappa$).
We derive constraints on the cosmological parameter pair $(\Omega_m,\sigma_8)$ from a combination of three commonly used WL statistics (power spectrum, lensing peaks, and Minkowski functionals), using ray-traced simulated $\kappa$ maps.

Built on

D. Ribli, B. Á. Pataki, J. M. Zorrilla Matilla, D. Hsu, Z. Haiman, and I. Csabai, MNRAS 490
Original
1902
Earlier work this paper cites.
B. Kim, J. Seo, S. Jeon, J. Koo, J. Choe, and T. Jeon, arXiv e-prints , arXiv:1902.04893 (2019), arXiv:1902.04893 [cs.LG]
Original
1902
Earlier work this paper cites.
T. Lu and Z. Haiman, MNRAS 490
Original
1905
Earlier work this paper cites.
J. Fluri, T. Kacprzak, A. Lucchi, A. Refregier, A. Amara, T. Hofmann, and A. Schneider, Phys. Rev. D 100
Original
1906
Earlier work this paper cites.
W. R. Coulton, J. Liu, I. G. McCarthy, and K. Osato, MNRAS (2020), 10.1093/mnras/staa1098 , arXiv:1910.04171 [astro-ph.CO]
Original
1910
Earlier work this paper cites.
H. J. Kelley, Ars Journal 30
1960
Earlier work this paper cites.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Nature 323
1986
Earlier work this paper cites.
K. R. Mecke, T. Buchert, and H. Wagner, A&A 288
1994
Earlier work this paper cites.
J. Schmalzing, M. Kerscher, and T. Buchert, in Dark Matter in the Universe , edited by S. Bonometto, J. R. Primack, and A. Provenzale (1996) p. 281, arXiv:astro-ph/9508154 [astro-ph]
1996
Earlier work this paper cites.
M. Bartelmann and P. Schneider, Phys. Rep. 340
2001
Earlier work this paper cites.
D. Zürcher, J. Fluri, R. Sgier, T. Kacprzak, and A. Refregier, arXiv e-prints , arXiv:2006.12506 (2020), arXiv:2006.12506 [astro-ph.CO]
Original
2006
Earlier work this paper cites.
M. Asgari, C.-A. Lin, B. Joachimi, et al. , arXiv e-prints , arXiv:2007.15633 (2020), arXiv:2007.15633 [astro-ph.CO]
Original
2007
Earlier work this paper cites.
J. Hartlap, P. Simon, and P. Schneider, A&A 464
2007
Earlier work this paper cites.
LSST Science Collaboration, P. A. Abell, J. Allison, et al. , arXiv e-prints (2009), arXiv:0912.0201 [astro-ph.IM]
Original
2009
Earlier work this paper cites.
A. Refregier, A. Amara, T. D. Kitching, et al. , arXiv e-prints (2010), arXiv:1001.0061 [astro-ph.IM]
Original
2010
Earlier work this paper cites.
J. M. Kratochvil, Z. Haiman, and M. May, Phys. Rev. D 81
Original
2010
Earlier work this paper cites.
J. P. Dietrich and J. Hartlap, MNRAS 402
Original
2010
Earlier work this paper cites.
V. Nair and G. E. Hinton, in Proceedings of the 27th international conference on machine learning (ICML-10) (2010) pp. 807–814
2010
Earlier work this paper cites.
D. Munshi, L. van Waerbeke, J. Smidt, and P. Coles, MNRAS 419
Original
2012
Earlier work this paper cites.
K. Simonyan, A. Vedaldi, and A. Zisserman, arXiv e-prints , arXiv:1312.6034 (2013), arXiv:1312.6034 [cs.CV]
Original
2013
Earlier work this paper cites.

Similar

M. D. Zeiler and R. Fergus, arXiv e-prints , arXiv:1311.2901 (2013), arXiv:1311.2901 [cs.CV]
Original
2013
Cited alongside, same era.
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller, arXiv e-prints , arXiv:1412.6806 (2014), arXiv:1412.6806 [cs.LG]
Original
2014
Cited alongside, same era.
M. Kilbinger, Reports on Progress in Physics 78
Original
2015
Cited alongside, same era.
D. Spergel, N. Gehrels, C. Baltay, et al. , arXiv e-prints (2015), arXiv:1503.03757 [astro-ph.IM]
Original
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Cited alongside, same era.
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek, PLoS ONE 10
2015
Cited alongside, same era.

Then

E. Paillas, C. D. P. Lagos, N. Padilla, et al. , MNRAS 470
Original
2017
Later among the works it cites.
A. Gupta, J. M. Z. Matilla, D. Hsu, and Z. Haiman, Phys. Rev. D 97
Original
2018
Later among the works it cites.
J. Fluri, T. Kacprzak, A. Refregier, A. Amara, A. Lucchi, and T. Hofmann, Phys. Rev. D 98
Original
2018
Later among the works it cites.
R. Mandelbaum, H. Miyatake, T. Hamana, et al. , PASJ 70
Original
2018
Later among the works it cites.
N. Martinet, P. Schneider, H. Hildebrandt, et al. , MNRAS 474
Original
2018

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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