scholarly journals CLASH: EXTENDING GALAXY STRONG LENSING TO SMALL PHYSICAL SCALES WITH DISTANT SOURCES HIGHLY MAGNIFIED BY GALAXY CLUSTER MEMBERS

2014 ◽  
Vol 786 (1) ◽  
pp. 11 ◽  
Author(s):  
C. Grillo ◽  
R. Gobat ◽  
V. Presotto ◽  
I. Balestra ◽  
A. Mercurio ◽  
...  
2020 ◽  
Vol 639 ◽  
pp. A125
Author(s):  
Alberto Manjón-García ◽  
Jose M. Diego ◽  
Diego Herranz ◽  
Daniel Lam

We performed a free-form strong lensing analysis of the galaxy cluster MACS J1206.2−0847 in order to estimate and constrain its inner dark matter distribution. The free-form method estimates the cluster total mass distribution without using any prior information about the underlying mass. We used 97 multiple lensed images belonging to 27 background sources and derived several models, which are consistent with the data. Among these models, we focus on those that better reproduce the radial images that are closest to the centre of the cluster. These radial images are the best probes of the dark matter distribution in the central region and constrain the mass distribution down to distances ∼7 kpc from the centre. We find that the morphology of the innermost radial arcs is due to the elongated morphology of the dark matter halo. We estimate the stellar mass contribution of the brightest cluster galaxy and subtracted it from the total mass in order to quantify the amount of dark matter in the central region. We fitted the derived dark matter density profile with a gNFW, which is characterised by rs = 167 kpc, ρs = 6.7 × 106 M⊙ kpc−3, and γgNFW = 0.70. These results are consistent with a dynamically relaxed cluster. This inner slope is smaller than the cannonical γ = 1 predicted by standard CDM models. This slope does not favour self-interacting models for which a shallower slope would be expected.


Author(s):  
Kevin Sebesta ◽  
Liliya L R Williams ◽  
Jori Liesenborgs ◽  
Elinor Medezinski ◽  
Nobuhiro Okabe

Abstract Abell 2744, a massive Hubble Frontier Fields merging galaxy cluster with many multiple images in the core has been the subject of many lens inversions using different methods. While most existing studies compare various inversion methods, we focus on a comparison of reconstructions that use different input lensing data. Since the quantity and quality of lensing data is constantly improving, it makes sense to ask if the estimated uncertainties are robust against changes in the data. We address this question using free-form Grale, which takes only image information as input, and nothing pertaining to cluster galaxies. We reconstruct Abell 2744 using two sets of strong lensing data from the Hubble Frontier Fields community. Our first and second reconstructions use 55 and 91 images, respectively, and only 10 of the 91 images have the same positions and redshifts as in the first reconstruction. Comparison of the two mass maps shows that Grale uncertainties are robust against these changes, as well as small modifications in the inversion routine. Additionally, applying the methods used in Sebesta et al. (2016) for MACS J0416, we conclude that, in a statistical sense, light follows mass in Abell 2744, with brighter galaxies clustering stronger with the recovered mass than the fainter ones. We also show that the faintest galaxies are anti-correlated with mass, which is likely the result of light contamination from bright galaxies, and lensing magnification bias acting on galaxies background to the cluster.


2014 ◽  
Vol 443 (2) ◽  
pp. 1549-1554 ◽  
Author(s):  
M. Jauzac ◽  
B. Clément ◽  
M. Limousin ◽  
J. Richard ◽  
E. Jullo ◽  
...  

2010 ◽  
Vol 514 ◽  
pp. A60 ◽  
Author(s):  
M. Schirmer ◽  
S. Suyu ◽  
T. Schrabback ◽  
H. Hildebrandt ◽  
T. Erben ◽  
...  

2014 ◽  
Vol 790 (2) ◽  
pp. L26 ◽  
Author(s):  
Grant R. Tremblay ◽  
Michael D. Gladders ◽  
Stefi A. Baum ◽  
Christopher P. O'Dea ◽  
Matthew B. Bayliss ◽  
...  

2011 ◽  
Vol 729 (1) ◽  
pp. 37 ◽  
Author(s):  
Andrea Morandi ◽  
Kristian Pedersen ◽  
Marceau Limousin

Author(s):  
G. V. Pignataro ◽  
P. Bergamini ◽  
M. Meneghetti ◽  
E. Vanzella ◽  
F. Calura ◽  
...  

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