2023A&A...676A..40A


Query : 2023A&A...676A..40A

2023A&A...676A..40A - Astronomy and Astrophysics, volume 676A40 (2023/8-1)

Searching for strong galaxy-scale lenses in galaxy clusters with deep networks I. Methodology and network performance.

ANGORA G., ROSATI P., MENEGHETTI M., BRESCIA M., MERCURIO A., GRILLO C., BERGAMINI P., ACEBRON A., CAMINHA G., NONINO M., TORTORELLI L., BAZZANINI L. and VANZELLA E.

Abstract (from CDS):

Strong galaxy-scale lenses in galaxy clusters provide a unique tool with which to investigate the inner mass distribution of these clusters and the subhalo density profiles in the low-mass regime, which can be compared with predictions from ΛCDM cosmological simulations. We search for galaxy-galaxy strong-lensing systems in the Hubble Space Telescope (HST) multi-band imaging of galaxy cluster cores by exploring the classification capabilities of deep learning techniques. Convolutional neural networks (CNNs) are trained utilising highly realistic simulations of galaxy-scale strong lenses injected into the HST cluster fields around cluster members (CLMs). To this aim, we take advantage of extensive spectroscopic information available in 16 clusters and accurate knowledge of the deflection fields in half of these from high-precision strong-lensing models. Using observationally based distributions, we sample the magnitudes (down to F814W = 29 AB), redshifts, and sizes of the background galaxy population. By placing these sources within the secondary caustics associated with the cluster galaxies, we build a sample of approximately 3000 strong galaxy-galaxy lenses, which preserve the full complexity of real multi-colour data and produce a wide diversity of strong-lensing configurations. We study two deep learning networks, processing a large sample of image cutouts, in three bands, acquired by HST Advanced Camera for Survey (ACS), and we quantify their classification performance using several standard metrics. We find that both networks achieve a very good trade-off between purity and completeness (85%-95%), as well as a good stability, with fluctuations within 2%-4%. We characterise the limited number of false negatives (FNs) and false positives (FPs) in terms of the physical properties of the background sources (magnitudes, colours, redshifts, and effective radii) and CLMs (Einstein radii and morphology). We also demonstrate the high degree of generalisation of the neural networks by applying our method to HST observations of 12 clusters with previously known galaxy-scale lensing systems.

Abstract Copyright: © The Authors 2023

Journal keyword(s): gravitational lensing: strong - galaxies: clusters: general - galaxies: distances and redshifts - techniques: image processing

Simbad objects: 18

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Number of rows : 18
N Identifier Otype ICRS (J2000)
RA
ICRS (J2000)
DEC
Mag U Mag B Mag V Mag R Mag I Sp type #ref
1850 - 2026
#notes
1 ACO 2744 ClG 00 14 20.03 -30 23 17.8           ~ 1077 0
2 ACO 209 ClG 01 31 52.9 -13 36 53           ~ 326 0
3 ACO 370 ClG 02 39 50.5 -01 35 08           ~ 825 0
4 ACO 383 ClG 02 48 03.3915 -03 31 45.228           ~ 389 0
5 RX J0329.6-0211 ClG 03 29 41.6 -02 11 47           ~ 155 0
6 MCS J0416.1-2403 ClG 04 16 08.380 -24 04 20.80           ~ 425 0
7 ClG J0429-0253 ClG 04 29 36.0 -02 53 09           ~ 131 0
8 ClG J0744+3927 ClG 07 44 52.5 +39 27 30           ~ 189 0
9 MCS J1115.8+0129 ClG 11 15 52.1 +01 29 53           ~ 142 0
10 MCS J1149.5+2223 ClG 11 49 35.8 +22 23 55           ~ 546 0
11 MCS J1206.2-0847 ClG 12 06 12.2 -08 48 02           ~ 286 0
12 ClG J1311-0311 ClG 13 11 01.7 -03 10 38           ~ 120 0
13 ClG J1347-1145 ClG 13 47 30.5 -11 45 07           ~ 577 0
14 ClG J1931-2635 ClG 19 31 49.6 -26 34 33           ~ 181 0
15 ClG J2129-0741 ClG 21 29 26.0 -07 41 28           ~ 189 0
16 ClG J2129+0005 ClG 21 29 40.5 +00 05 47           ~ 308 0
17 ClG 2137-2353 ClG 21 40 12.8 -23 39 27     18.5     ~ 390 0
18 ACO S 1063 ClG 22 48 45.4 -44 31 42           ~ 395 0

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