Weak lensing cosmology with convolutional neural networks on noisy data. (arXiv:1902.03663v1 [astro-ph.CO])

<a href="http://arxiv.org/find/astro-ph/1/au:+Ribli_D/0/1/0/all/0/1">Dezső Ribli</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Pataki_B/0/1/0/all/0/1">Bálint Ármin Pataki</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Matilla_J/0/1/0/all/0/1">José Manuel Zorrilla Matilla</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Hsu_D/0/1/0/all/0/1">Daniel Hsu</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Haiman_Z/0/1/0/all/0/1">Zoltán Haiman</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Csabai_I/0/1/0/all/0/1">István Csabai</a>

Weak gravitational lensing is one of the most promising cosmological probes

of the late universe. Several large ongoing (DES, KiDS, HSC) and planned (LSST,

EUCLID, WFIRST) astronomical surveys attempt to collect even deeper and larger

scale data on weak lensing. Due to gravitational collapse, the distribution of

dark matter is non-Gaussian on small scales. However, observations are

typically evaluated through the two-point correlation function of galaxy shear,

which does not capture non-Gaussian features of the lensing maps. Previous

studies attempted to extract non-Gaussian information from weak lensing

observations through several higher-order statistics such as the three-point

correlation function, peak counts or Minkowski-functionals. Deep convolutional

neural networks (CNN) emerged in the field of computer vision with tremendous

success, and they offer a new and very promising framework to extract

information from 2 or 3-dimensional astronomical data sets, confirmed by recent

studies on weak lensing. We show that a CNN is able to yield significantly

stricter constraints of ($sigma_8, Omega_m$) cosmological parameters than the

power spectrum using convergence maps generated by full N-body simulations and

ray-tracing, at angular scales and shape noise levels relevant for future

observations. In a scenario mimicking LSST or Euclid, the CNN yields 2.4-2.8

times smaller credible contours than the power spectrum, and 3.5-4.2 times

smaller at noise levels corresponding to a deep space survey such as WFIRST. We

also show that at shape noise levels achievable in future space surveys the CNN

yields 1.4-2.1 times smaller contours than peak counts, a higher-order

statistic capable of extracting non-Gaussian information from weak lensing

maps.

Weak gravitational lensing is one of the most promising cosmological probes

of the late universe. Several large ongoing (DES, KiDS, HSC) and planned (LSST,

EUCLID, WFIRST) astronomical surveys attempt to collect even deeper and larger

scale data on weak lensing. Due to gravitational collapse, the distribution of

dark matter is non-Gaussian on small scales. However, observations are

typically evaluated through the two-point correlation function of galaxy shear,

which does not capture non-Gaussian features of the lensing maps. Previous

studies attempted to extract non-Gaussian information from weak lensing

observations through several higher-order statistics such as the three-point

correlation function, peak counts or Minkowski-functionals. Deep convolutional

neural networks (CNN) emerged in the field of computer vision with tremendous

success, and they offer a new and very promising framework to extract

information from 2 or 3-dimensional astronomical data sets, confirmed by recent

studies on weak lensing. We show that a CNN is able to yield significantly

stricter constraints of ($sigma_8, Omega_m$) cosmological parameters than the

power spectrum using convergence maps generated by full N-body simulations and

ray-tracing, at angular scales and shape noise levels relevant for future

observations. In a scenario mimicking LSST or Euclid, the CNN yields 2.4-2.8

times smaller credible contours than the power spectrum, and 3.5-4.2 times

smaller at noise levels corresponding to a deep space survey such as WFIRST. We

also show that at shape noise levels achievable in future space surveys the CNN

yields 1.4-2.1 times smaller contours than peak counts, a higher-order

statistic capable of extracting non-Gaussian information from weak lensing

maps.

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