Weak lensing cosmology with convolutional neural networks on noisy data

Abstract

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 2D or 3D astronomical data sets, confirmed by recent studies on weak lensing. We show that a CNN is able to yield significantly stricter constraints of (σ8, Ωm) 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.

Cosmology with one galaxy?

“…Another avenue is to use machine learning techniques, e.g. neural networks, to find an approximation to the optimal estimator (Ravanbakhsh et al 2017;Schmelzle et al 2017;Gupta et al 2018;Ribli et al 2019;Fluri et al 2019;Ntampaka et al 2019;Hassan et al 2020;Zorrilla Matilla et al 2020;Villaescusa-Navarro et al 2021a;Lu et al 2021). Recent works have shown that even for fields that are very contaminated by astrophysical effects, it is possible to extract cosmological information from small scales (Villaescusa-Navarro et al 2021b).…”

Revisiting the cosmic distance duality relation with machine learning reconstruction methods: the combination of HII galaxies and ultra-compact radio quasars

“…Artificial Neural Network For the second nonparametric approach, we turn to Artificial Neural Network (ANN) and show the reconstructed function from observational data. With the development of computer hardware in the recent 10 years, machine learning technology has been gradually applied to many research fields in astronomy, and shown excellent potential for solving cosmological problems, such as analyzing gravitational waves [52,53] and constraining cosmological parameters [54][55][56][57][58][59].…”

Machine-Learning Methods for Computational Science and Engineering

“…While ML methods such as ANN have been used for almost 30 years [226], more recent works focus on CNNs, due to their ability to process and analyze images in a relatively computationally efficient way. CNNs have been used to understand the morphology of galaxies [227][228][229], predict photometric redshifts [230,231], detect galaxy clusters [232], identify gravitational lenses [233][234][235][236] and reconstruction of images [237] Video classification is yet another field that keeps improving along with advances in ML. Karpathy et al [238] have used CNNs to classify sports-related videos found on YouTube into their corresponding sports.…”