paper

Parametric strong lensing model of the galaxy cluster Abell 2390 from Euclid and MUSE observations

arXiv:2510.17192 · doi:10.1051/0004-6361/202556396

Abstract

We present a new high precision parametric strong lensing total mass reconstruction of the Euclid Early Release Observations (ERO) galaxy cluster Abell 2390, at redshift z = 0.231. We include in this analysis 35 multiple images from 13 background sources, of which 25 are spectroscopically confirmed thanks to observations from the MUSE, spanning a redshift range from z = 0.535 to z = 4.877. After fully reanalysing the MUSE spectroscopy, we combine it with archival spectroscopic catalogues allowing us to select 65 secure cluster members. This sample is further complemented with 114 photometric member galaxies, identified within the Euclid VIS and NISP imaging down to magnitude H = 23. We also measure the stellar velocity dispersions for 22 cluster members, in order to calibrate the Faber-Jackson relation and hence the scaling relations for the subhalo mass components. We test and compare 11 total mass parametrisations of the galaxy cluster with increasing complexity. To do so, we employ the new parametric strong lensing modelling code Gravity.jl. Our best fit total mass parametrisation is characterised by a single large scale halo, 179 subhalo components, and an external shear term. The reference model yields a mean scatter between the model predicted and observed positions of the multiple images of 0.32 arcseconds. We are able to quantify the systematics arising from our modelling choices by taking advantage of all the different explored total mass parametrisations. When our results are compared with those from other lensing studies, we notice an overall agreement in the reconstructed cluster total mass profile in the outermost strong lensing regime: the discrepancy in the innermost region of the cluster (few kiloparsec from the brightest cluster galaxy, where few or no strong lensing features are observed) could possibly be ascribed to the different data and modelling choices.

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