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The extension of the singular perturbative approach to the second order is presented in this paper. The general expansion to the second order is derived. The second order expansion is considered as a small correction to the first order expansion. Using this approach it is demonstrated that the second order expansion is reducible to a first order expansion via a re-definition of the first order pertubative fields. Even if in practice the second order correction is small the reducibility of the second order expansion to the first order expansion indicates a degeneracy problem. In general this degeneracy is hard to break. A useful and simple second order approximation is the thin source approximation which offers a direct estimation of the correction. The practical application of the corrections derived in this paper are illustrated by using an elliptical NFW lens model. The second order pertubative expansion provides a noticeable improvement, even for the simplest case of thin source approximation. To conclude it is clear that for accurate modelisation of gravitational lenses using the perturbative method the second order perturbative expansion should be considered. In particular an evaluation of the degeneracy due to the second order term should be performed, for which the thin source approximation is particularly useful.
We construct Greens functions for second order parabolic operators of the form $Pu=partial_t u-{rm div}({bf A} abla u+ boldsymbol{b}u)+ boldsymbol{c} cdot abla u+du$ in $(-infty, infty) times Omega$, where $Omega$ is an open connected set in $mathb
This paper is concerned with higher Holder regularity for viscosity solutions to non-translation invariant second order integro-PDEs, compared to cite{mou2018}. We first obtain $C^{1,alpha}$ regularity estimates for fully nonlinear integro-PDEs. We t
Convolutional Neural Networks (ConvNets) are one of the most promising methods for identifying strong gravitational lens candidates in survey data. We present two ConvNet lens-finders which we have trained with a dataset composed of real galaxies fro
We present a sample of 16 likely strong gravitational lenses identified in the VST Optical Imaging of the CDFS and ES1 fields (VOICE survey) using Convolutional Neural Networks (CNNs). We train two different CNNs on composite images produced by super
We consider a machine learning algorithm to detect and identify strong gravitational lenses on sky images. First, we simulate different artificial but very close to reality images of galaxies, stars and strong lenses, using six different methods, i.e