Euclid preparation - XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong-lensing events

Open Access

Table 2

Summary of the criteria we adopted to choose the images included in the different selections of lenses and nonlenses for our experiments.

Selection	Lenses		nonlenses		Total

	Criterion	Number of images	Criterion	Number of images
S1	`n_pix_source` >430	1001	Randomly selected objects with `n_sources = 0`	1000	2001

S2	`n_pix_source` >140	5083	Randomly selected objects with `n_sources = 0`	5000	10 083

S3	`n_pix_source` >70	9709	Randomly selected objects with `n_sources = 0`	10000	19 709

S4	`n_pix_source` >20	19 591	Randomly selected objects with `n_source_im` = 0	20 000	39 591

Notes. While the identification of the lenses is solely based on the variation of a threshold value for the parameter n_pix_source, the identification of the nonlenses is primarily based on the possible presence and visibility of a background source.

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.