New high-precision strong lensing modeling of Abell 2744. Preparing for JWST observations

We present a new strong lensing (SL) model of the Hubble Frontier Fields galaxy cluster Abell 2744, at z=0.3072, by exploiting archival Hubble Space Telescope (HST) multi-band imaging and Multi Unit Spectroscopic Explorer (MUSE) follow-up spectroscopy. The lens model considers 90 spectroscopically confirmed multiple images (from 30 background sources), which represents the largest secure sample for this cluster field prior to the recently acquired James Webb Space Telescope observations. The inclusion of the sub-structures within several extended sources as model constraints allows us to accurately characterize the inner total mass distribution of the cluster and the position of the cluster critical lines. We include the lensing contribution of 225 cluster members, 202 of which are spectroscopically confirmed. We also measure the internal velocity dispersion of 85 cluster galaxies to independently estimate the role of the subhalo mass component in the lens model. We investigate the effect of the cluster environment on the total mass reconstruction of the cluster core with two different mass parameterizations. We consider the mass contribution from three external clumps, either based on previous weak-lensing studies, or extended HST imaging of luminous members around the cluster core. In the latter case, the observed positions of the multiple images are better reproduced, with a remarkable accuracy of 0.37", a factor of $\sim2$ smaller than previous lens models. We develop and make publicly available a Strong Lensing Online Tool (SLOT) to exploit the predictive power and the full statistical information of this and future models, through a simple graphical interface. We plan to apply our high-precision SL model to the first analysis of the GLASS-JWST-ERS program, specifically to measure the intrinsic physical properties of high-$z$ galaxies from robust magnification maps.Comment: 14 pages, 9 figures, 4 table

The Kormendy relation of early-type galaxies as a function of wavelength in Abell S1063, MACS J0416.1-2403 and MACS J1149.5+2223

The wavelength dependence of the Kormendy relation (KR) is well characterised at low-redshift, but poorly studied at intermediate redshifts. The KR provides information on the evolution of the population of early-type galaxies (ETGs), therefore, by studying it, we may shed light on the assembly processes of these objects and their size evolution. Since studies at different redshifts are generally conducted in different rest-frame wavebands, investigating whether there is a wavelength dependence of the KR is fundamental to interpret the conclusions we might draw from it. We analyse the KRs of the three Hubble Frontier Fields clusters, Abell S1063 (z = 0.348), MACS J0416.1-2403 (z = 0.396), and MACS J1149.5+2223 (z = 0.542), as a function of wavelength. This is the first time the KR of ETGs has been explored consistently in such a large range of wavelength at intermediate redshifts. We exploit very deep HST photometry, ranging from the observed B-band to the H-band, and VLT/MUSE integral field spectroscopy. We improve the structural parameters estimation we performed in a previous work (Tortorelli et al. 2018) by means of a newly developed Python package called morphofit (Tortorelli&Mercurio 2023). With its use on cluster ETGs, we find that the KR slopes smoothly increase with wavelength from the optical to the near-infrared bands in all three clusters, with the intercepts getting fainter at lower redshifts due to the passivisation of the ETGs population. The slope trend is consistent with previous findings at lower redshifts. The slope increase with wavelength implies that smaller size ETGs are more centrally concentrated than larger size ETGs in the near-infrared with respect to the optical regime. Since different bands probe different stellar populations in galaxies, the slope increase also implies that smaller ETGs have stronger internal gradients with respect to larger ETGs.Comment: Submitted to Astronomy and Astrophysics in the form of letter to the Editor, 5 pages, 1 figure, 1 tabl

Euclid Preparation TBD. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong lensing events

Forthcoming imaging surveys will increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of billions of galaxies will have to be inspected to identify potential candidates. In this context, deep-learning techniques are particularly suitable for finding patterns in large data sets, and convolutional neural networks (CNNs) in particular can efficiently process large volumes of images. We assess and compare the performance of three network architectures in the classification of strong-lensing systems on the basis of their morphological characteristics. In particular, we implemented a classical CNN architecture, an inception network, and a residual network. We trained and tested our networks on different subsamples of a data set of 40 000 mock images whose characteristics were similar to those expected in the wide survey planned with the ESA mission Euclid, gradually including larger fractions of faint lenses. We also evaluated the importance of adding information about the color difference between the lens and source galaxies by repeating the same training on single- and multiband images. Our models find samples of clear lenses with ≳90% precision and completeness. Nevertheless, when lenses with fainter arcs are included in the training set, the performance of the three models deteriorates with accuracy values of ~0.87 to ~0.75, depending on the model. Specifically, the classical CNN and the inception network perform similarly in most of our tests, while the residual network generally produces worse results. Our analysis focuses on the application of CNNs to high-resolution space-like images, such as those that the Euclid telescope will deliver. Moreover, we investigated the optimal training strategy for this specific survey to fully exploit the scientific potential of the upcoming observations. We suggest that training the networks separately on lenses with different morphology might be needed to identify the faint arcs. We also tested the relevance of the color information for the detection of these systems, and we find that it does not yield a significant improvement. The accuracy ranges from ~0.89 to ~0.78 for the different models. The reason might be that the resolution of the Euclid telescope in the infrared bands is lower than that of the images in the visual band

CLASH-VLT: Abell S1063: Cluster assembly history and spectroscopic catalogue

Context. The processes responsible for galaxy evolution in different environments as a function of galaxy mass remain heavily debated. Rich galaxy clusters are ideal laboratories in which to distinguish the role of environmental versus mass quenching because they consist of a full range of galaxies and environments. Aims. Using the CLASH-VLT survey, we assembled an unprecedentedly large sample of 1234 spectroscopically confirmed members in Abell S1063. We found a dynamically complex structure at «zcl»= 0.3457 with a velocity dispersion σv = 1380-32+26 km s-1. We investigated cluster environmental and dynamical effects by analysing the projected phase-space diagram and the orbits as a function of galaxy spectral properties. Methods. We classified cluster galaxies according to the presence and strength of the [OII] emission line, the strength of the Hδ absorption line, and colours. We investigated the relation between the spectral classes of galaxies and their position in the projected phase-space diagram. We separately analysed red and blue galaxy orbits. By correlating the observed positions and velocities with the projected phase-space constructed from simulations, we constrained the accretion redshift of galaxies with different spectral types. Results. Passive galaxies are mainly located in the virialised region, while emission-line galaxies lie beyond r200 and are accreted into the cluster at a later time. Emission-line and post-starburst galaxies show an asymmetric distribution in projected phase-space within r200; emission-line galaxies are prominent at Δv/σ ≲ -1.5 and post-starburst galaxies at Δv/σ ≲ 1.5, suggesting that backsplash galaxies lie at high positive velocities. We find that low-mass passive galaxies are accreted into the cluster before high-mass galaxies. This suggests that we observe as passives only the low-mass galaxies that are accreted early into the cluster as blue galaxies. They had the time to quench their star formation. We also find that red galaxies move on more radial orbits than blue galaxies. This can be explained if infalling galaxies can remain blue by moving on tangential orbits

The probability of galaxy-galaxy strong lensing events in hydrodynamical simulations of galaxy clusters

Meneghetti et al. (2020) recently reported an excess of galaxy-galaxy strong lensing (GGSL) in galaxy clusters compared to expectations from the LCDM cosmological model. Theoretical estimates of the GGSL probability are based on the analysis of numerical hydrodynamical simulations in the LCDM cosmology. We quantify the impact of the numerical resolution and AGN feedback scheme adopted in cosmological simulations on the predicted GGSL probability and determine if varying these simulation properties can alleviate the gap with observations. We repeat the analysis of Meneghetti et al. (2020) on cluster-size halos simulated with different mass and force resolutions and implementing several independent AGN feedback schemes. We find that improving the mass resolution by a factor of ten and twenty-five, while using the same galaxy formation model that includes AGN feedback, does not affect the GGSL probability. We find similar results regarding the choice of gravitational softening. On the contrary, adopting an AGN feedback scheme that is less efficient at suppressing gas cooling and star formation leads to an increase in the GGSL probability by a factor between three and six. However, we notice that such simulations form overly massive subhalos whose contribution to the lensing cross-section would be significant while their Einstein radii are too large to be consistent with the observations. The primary contributors to the observed GGSL cross-sections are subhalos with smaller masses, that are compact enough to become critical for lensing. The population with these required characteristics appears to be absent in simulations.Comment: 13 pages, 11 figures. Submitted for publication on Astronomy and Astrophysic

Euclid Preparation TBD. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong lensing events

Forthcoming imaging surveys will potentially increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of tens of millions of galaxies will have to be inspected to identify potential candidates. In this context, deep learning techniques are particularly suitable for the finding patterns in large data sets, and convolutional neural networks (CNNs) in particular can efficiently process large volumes of images. We assess and compare the performance of three network architectures in the classification of strong lensing systems on the basis of their morphological characteristics. We train and test our models on different subsamples of a data set of forty thousand mock images, having characteristics similar to those expected in the wide survey planned with the ESA mission \Euclid, gradually including larger fractions of faint lenses. We also evaluate the importance of adding information about the colour difference between the lens and source galaxies by repeating the same training on single-band and multi-band images. Our models find samples of clear lenses with $\gtrsim 90\%$ precision and completeness, without significant differences in the performance of the three architectures. Nevertheless, when including lenses with fainter arcs in the training set, the three models' performance deteriorates with accuracy values of $\sim 0.87$ to $\sim 0.75$ depending on the model. Our analysis confirms the potential of the application of CNNs to the identification of galaxy-scale strong lenses. We suggest that specific training with separate classes of lenses might be needed for detecting the faint lenses since the addition of the colour information does not yield a significant improvement in the current analysis, with the accuracy ranging from $\sim 0.89$ to $\sim 0.78$ for the different models

A strong lensing model of the galaxy cluster PSZ1 G311.65-18.48

We present a strong lensing analysis of the galaxy cluster PSZ1 G311.65-18.48 (z=0.443) using multi-band observations with Hubble Space Telescope, complemented with VLT/MUSE spectroscopic data. The MUSE observations provide redshift estimates for the lensed sources and help reducing the mis-identification of the multiple images. Spectroscopic data are also used to measure the inner velocity dispersions of 15 cluster galaxies and calibrate the scaling relations to model the subhalo cluster component. The model is based on 62 multiple images grouped in 17 families belonging to 4 different sources. The majority of them are multiple images of compact stellar knots belonging to a single star-forming galaxy at z=2.3702. This source is strongly lensed by the cluster to form the Sunburst Arc system. To accurately reproduce all the multiple images, we build a parametric mass model, which includes both cluster-scale and galaxy-scale components. The resulting model has a r.m.s. separation between the model-predicted and the observed positions of the multiple images of only 0.14''. We conclude that PSZ1 G311.65-18.48 has a relatively round projected shape and a large Einstein radius (29'' for z_s = 2.3702), which could indicate that the cluster is elongated along the line of sight. The Sunburst Arc source is located at the intersection of a complex network of caustics, which explains why parts of the arc are imaged with unprecedented multiplicity (up to 12 times)

A new high-precision strong lensing model of the galaxy cluster MACS J0416.1-2403: Robust characterization of the cluster mass distribution from VLT/MUSE deep observations

We present a new high-precision parametric strong lensing model of the galaxy cluster MACS J0416.1-2403, at z? =? 0.396, which takes advantage of the MUSE Deep Lensed Field (MDLF), with 17.1 h integration in the northeast region of the cluster, and Hubble Frontier Fields data. We spectroscopically identify 182 multiple images from 48 background sources at 0.9