SubDLe: Identification of substructures in cosmological simulations with deep learning - An image segmentation approach to substructure finding

Open Access

Table 1

Main properties of the clusters analyzed in this study.

ID	M_vir (10¹⁵ M_⊙)	R_vir (Mpc)	N_gal	Phase
CL-1	0.74	2.4	192	train and val
CL-2	0.73	2.4	231	test
CL-3	2.3	3.4	571	test
CL-4	0.92	2.5	236	test
CL-5	0.24	1.6	75	test
CL-6	2.1	3.3	510	test
CL-7	0.18	1.5	40	test
CL-8	1.7	3.1	505	test
CL-9	1.9	3.2	604	test
CL-10	2.4	3.5	602	test
CL-11	2.3	3.5	535	test
CL-12	2.2	3.4	543	test
CL-13	2.3	3.2	542	test

Notes. For each cluster, listed above are the virial mass, the virial radius, the number of resolved galaxies (with at least 100 star particles), and the phase of the analysis in which it was used (i.e., training, referred to as “train”, validation, referred to as “val”, or test).

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