Speaker
Description
We present a proof of concept for an alternative method of strong gravitational lens finding using a conditional Generative Adversarial Network (cGAN). We use Early Release Observation (ERO) images of the Perseus Cluster from Euclid, covering 0. 57sq.degrees on the sky, and the network is based on the pix2pix architecture with an adapted U-Net generator. We train our model to predict Euclid’s NISP-H band flux ($1.54-2.00µm$) from a combination of the filters NISP-J, NISP-Y and VIS band ($0.55-1.54µm$) in 40,000 cut-outs from the Perseus Cluster which are $20\times20$ arcseconds in size. We test the cGAN on 5,000 cut-outs from the Perseus cluster, 10% of which contain a simulated strong gravitational lens painted into the cut-out based on SIE/Singular Isothermal Ellipsoid and PEMD/Power Law Ellipse Mass Density mass profiles. Candidate gravitational lenses and cut-outs with a gravitational lens painted in were deliberated excluded from the model’s training data set such that gravitational lensing systems remain unknown to the network. We find that the cGAN can accurately predict the NISP-H band flux of the cut-outs from the Perseus cluster. However, the model fails to predict the NISP-H band flux of the cut-outs containing the simulated gravitational lenses, with a larger difference between the prediction of the model and ground truth for lenses with extended arcs and Einstein rings, suggesting that the cGAN can be used as an anomaly detector for an alternative method of lens finding.
