G. Angora — Page 2 | Searcharxiv

arXiv2021

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).

astro-ph.GA↗

arXiv2020

The search for galaxy cluster members with deep learning of panchromatic HST imaging and extensive spectroscopy

The next generation of data-intensive surveys are bound to produce a vast amount of data, which can be dealt with using machine-learning methods to explore possible correlations within the multi-dimensional parameter space. We explore the classification capabilities of convolution neural networks (CNNs) to identify galaxy cluster members (CLMs) by using Hubble Space Telescope (HST) images of 15 galaxy clusters at redshift 0.19<z<0.60, observed as part of the CLASH and Hubble Frontier Field programmes. We used extensive spectroscopic information, based on the CLASH-VLT VIMOS programme combined with MUSE observations, to define the knowledge base. We performed various tests to quantify how well CNNs can identify cluster members on the basis of imaging information only. We investigated the CNN capability to predict source memberships outside the training coverage, by identifying CLMs at the faint end of the magnitude distributions. We find that the CNNs achieve a purity-completeness rate ~90%, demonstrating stable behaviour, along with a remarkable generalisation capability with respect to cluster redshifts. We concluded that if extensive spectroscopic information is available as a training base, the proposed approach is a valid alternative to catalogue-based methods because it has the advantage of avoiding photometric measurements, which are particularly challenging and time-consuming in crowded cluster cores. As a byproduct, we identified 372 photometric CLMs, with mag(F814)<25, to complete the sample of 812 spectroscopic CLMs in four galaxy clusters RX~J2248-4431, MACS~J0416-2403, MACS~J1206-0847 and MACS~J1149+2223. When this technique is applied to the data that are expected to become available from forthcoming surveys, it will be an efficient tool for a variety of studies requiring CLM selection, such as galaxy number densities, luminosity functions, and lensing mass reconstruction.

astro-ph.GA↗