English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Forthcoming imaging surveys will potentially increase the number of knowngalaxy-scale strong lenses by several orders of magnitude. For this to happen,images of tens of millions of galaxies will have to be inspected to identifypotential candidates. In this context, deep learning techniques areparticularly suitable for the finding patterns in large data sets, andconvolutional neural networks (CNNs) in particular can efficiently processlarge volumes of images. We assess and compare the performance of three networkarchitectures in the classification of strong lensing systems on the basis oftheir morphological characteristics. We train and test our models on differentsubsamples of a data set of forty thousand mock images, having characteristicssimilar to those expected in the wide survey planned with the ESA mission\Euclid, gradually including larger fractions of faint lenses. We also evaluatethe importance of adding information about the colour difference between thelens and source galaxies by repeating the same training on single-band andmulti-band images. Our models find samples of clear lenses with $\gtrsim 90\%$precision and completeness, without significant differences in the performanceof the three architectures. Nevertheless, when including lenses with fainterarcs in the training set, the three models' performance deteriorates withaccuracy values of $\sim 0.87$ to $\sim 0.75$ depending on the model. Ouranalysis confirms the potential of the application of CNNs to theidentification of galaxy-scale strong lenses. We suggest that specific trainingwith separate classes of lenses might be needed for detecting the faint lensessince the addition of the colour information does not yield a significantimprovement in the current analysis, with the accuracy ranging from $\sim 0.89$to $\sim 0.78$ for the different models.

Euclid Preparation TBD. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong lensing events