Euclid: Searches for strong gravitational lenses using convolutional neural nets in Early Release Observations of the Perseus field

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Table 1

Performance metrics for the various models with their p_THRESH values, based on the common test set of 12 086 stamps.

Model	Section	p_THRESH	TPR	FPR	N_FP	N_TP	N_FN	N_TN	N_FP/N_TP
`Zoobot1`	3.1	0.50	89.71	4.55	547	61	7	11 471	8.97
`Zoobot2`	3.1	0.50	61.72	5.48	659	42	26	11 359	15.69
`E1`	3.1	0.50	41.84	10.09	1213	29	39	10 805	41.83
`E2`	3.1	0.50	45.42	9.09	1093	24	44	10 925	45.54
`Naberrie`	3.2	0.90	26.47	23.64	2841	18	50	9177	157.83
`MRC-95`	3.2	0.95	80.24	65.14	7828	60	8	4190	130.47
`MRC-99`	3.2	0.95	48.53	20.51	2465	33	35	9553	74.70
`MRC-995`	3.2	0.95	60.29	38.74	4656	41	27	7362	113.56
`MRC-3`	3.2	0.95	48.53	20.51	2465	33	35	9553	74.70
`Denselens`	3.5	0.23	50.00	4.64	558	34	34	11 460	16.41
`U-Denselens`	3.5	0.18	39.70	2.75	331	27	41	11 687	12.26
`NG`	3.3	0.63	63.00	31.00	3651	43	25	8367	84.91
`ResNet-18`	3.3	0.99	48.53	22.36	2703	33	35	9315	81.91
`4-layer CNN`	3.4	0.60	70.59	30.09	3616	48	20	8402	75.33
`LensCLR`	3.6	0.50	94.00	21.00	2480	62	6	9515	40.00
`CBEN-0.3`	3.8	0.30	66.18	22.42	2694	45	23	9324	59.87
`CBEN-0.0001`	3.8	0.0001	39.71	4.85	584	27	41	11 434	21.63
`VGG`	3.7	0.30	20.60	21.29	2559	14	54	9459	182.79
`GNet`	3.7	0.30	22.06	21.98	2641	15	53	9377	176.07
`ResNext`	3.7	0.30	29.41	22.70	2728	20	48	9290	136.40

Notes. True positive rate TPR and false positive rate FPR are given as percentages; total numbers of false positives (N_FP), true positives (N_TP), false negatives (N_FN), true negatives (N_TN), and the contamination rate (N_FP/N_TP) are also shown. For a detailed description of each network, we refer the reader to the respective section listed in column 2.

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