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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/21078

Title: Quantifying substructures in Hubble Frontier Field clusters: comparison with ΛCDM simulations
Authors: Irshad, Mohammed
Prasenjit, Saha
Williams Liliya, L. R.
Liesenborgs, Jori
Sebesta, Kevin
Issue Date: 2016
Citation: Monthly notices of the Royal Astronomical Society, 459 (2), p. 1698-1709
Abstract: The Hubble Frontier Fields (HFF) are six clusters of galaxies, all showing indications of recent mergers, which have recently been observed for lensed images. As such they are the natural laboratories to study the merging history of galaxy clusters. In this work, we explore the 2D power spectrum of the mass distribution PM(k) as a measure of substructure. We compare PM(k) of these clusters (obtained using strong gravitational lensing) to that of ΛCDM simulated clusters of similar mass. To compute lensing PM(k), we produced free-form lensing mass reconstructions of HFF clusters, without any light traces mass (LTM) assumption. The inferred power at small scales tends to be larger if (i) the cluster is at lower redshift, and/or (ii) there are deeper observations and hence more lensed images. In contrast, lens reconstructions assuming LTM show higher power at small scales even with fewer lensed images; it appears the small scale power in the LTM reconstructions is dominated by light information, rather than the lensing data. The average lensing derived PM(k) shows lower power at small scales as compared to that of simulated clusters at redshift zero, both darkmatter only and hydrodynamical. The possible reasons are: (i) the available strong lensing data are limited in their effective spatial resolution on the mass distribution, (ii) HFF clusters have yet to build the small scale power they would have at z ∼ 0, or (iii) simulations are somehow overestimating the small scale power.
Notes: Mohammed, I (reprint author), Fermilab Natl Accelerator Lab, Theoret Astrophys Grp, POB 500, Batavia, IL 60510 USA mohammed@fnal.gov
URI: http://hdl.handle.net/1942/21078
DOI: 10.1093/mnras/stw727
ISI #: 000377471200043
ISSN: 0035-8711
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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