STRONG-LENSING ANALYSIS OF THE POWERFUL LENSING CLUSTER MACS J2135.2-0102 (z = 0.33)

Zitrin, Adi; Broadhurst, Tom

doi:10.3847/0004-637x/833/1/25

Cited by 13 publications

(9 citation statements)

References 55 publications

(117 reference statements)

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“…Follow-up could examine more closely the role of environment in strong lensing systems, but this consideration is outside the scope of this paper. G204703 is part of a cluster, and the arc is partially the result of cluster strong lensing (Zitrin & Broadhurst 2016;Dessauges-Zavadsky et al 2017;Zitrin 2017;Newman et al 2018). One of the GalaxyZoo identifications, G593852, is in the HLA, but the arc is faint in the Hubble single filter image (Figure 20).…”

Section: Hubble and Future Surveysmentioning

confidence: 99%

Galaxy and Mass Assembly: A Comparison between Galaxy–Galaxy Lens Searches in KiDS/GAMA

Knabel

Steele

Holwerda

et al. 2020

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Strong gravitational lenses are a rare and instructive type of astronomical object. Identification has long relied on serendipity, but different strategies-such as mixed spectroscopy of multiple galaxies along the line of sight, machine-learning algorithms, and citizen science-have been employed to identify these objects as new imaging surveys become available. We report on the comparison between spectroscopic, machine-learning, and citizenscience identification of galaxy-galaxy lens candidates from independently constructed lens catalogs in the common survey area of the equatorial fields of the Galaxy and Mass Assembly survey. In these, we have the opportunity to compare high completeness spectroscopic identifications against high-fidelity imaging from the Kilo Degree Survey used for both machine-learning and citizen-science lens searches. We find that the three methodsspectroscopy, machine learning, and citizen science-identify 47, 47, and 13 candidates, respectively, in the 180 square degrees surveyed. These identifications barely overlap, with only two identified by both citizen science and machine learning. We have traced this discrepancy to inherent differences in the selection functions of each of the three methods, either within their parent samples (i.e., citizen science focuses on low redshift) or inherent to the method (i.e., machine learning is limited by its training sample and prefers well-separated features, while spectroscopy requires sufficient flux from lensed features to lie within the fiber). These differences manifest as separate samples in estimated Einstein radius, lens stellar mass, and lens redshift. The combined sample implies a lens candidate sky density of ∼0.59 deg −2 and can inform the construction of a training set spanning a wider massredshift space. A combined approach and refinement of automated searches would result in a more complete sample of galaxy-galaxy lens candidates for future surveys.

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Section: Hubble and Future Surveysmentioning

confidence: 99%

Galaxy and Mass Assembly: A Comparison between Galaxy–Galaxy Lens Searches in KiDS/GAMA

Knabel

Steele

Holwerda

et al. 2020

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“…Deep HFF observations have revealed ∼ 100 − 150 multiple images per cluster. To generate mass models of the clusters, HFF has taken advantage of a range of existing lens inversion codes; about eight in all: Lenstool (Jullo & Kneib 2009;Johnson et al 2014;Jauzac et al 2015), SWUnited (Bradač et al 2009;Strait et al 2018), WSLAP+ (Sendra et al 2014;Diego et al 2016), Grale (Sebesta et al 2019), Glafic (Kawamata et al 2016), LensPerfect (Coe et al 2010), LTM (Zitrin & Broadhurst 2016), PIEMDeNFW (Zitrin et al 2013) etc. The ongoing Hubble Space Telescope's Beyond Ultra-deep Frontier Fields And Legacy Observations (BUFFALO; PI: C. Steinhardt) project will also utilize several of these lens inversion techniques.…”

Section: Introductionmentioning

confidence: 99%

Free-form grale lens inversion of galaxy clusters with up to 1000 multiple images

Ghosh

Williams

Liesenborgs

2020

Monthly Notices of the Royal Astronomical Society

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In the near future, ultra deep observations of galaxy clusters with HST or JWST will uncover 300 − 1000 lensed multiple images, increasing the current count per cluster by up to an order of magnitude. This will further refine our view of clusters, leading to a more accurate and precise mapping of the total and dark matter distribution in clusters, and enabling a better understanding of background galaxy population and their luminosity functions. However, to effectively use that many images as input to lens inversion will require a re-evaluation of, and possibly upgrades to the existing methods. In this paper we scrutinize the performance of the free-form lens inversion method Grale in the regime of 150 − 1000 input images, using synthetic massive galaxy clusters. Our results show that with an increasing number of input images, Grale produces improved reconstructed mass distributions, with the fraction of the lens plane recovered at better than 10% accuracy increasing from 40 − 50% for ∼150 images to 65% for ∼ 1000 images. The reconstructed time delays imply a more precise measurement of H 0 , with 1% bias. While the fidelity of the reconstruction improves with the increasing number of multiple images used as model constraints, ∼ 150 to ∼1000, the lens plane rms deteriorates from ∼ 0.11 to ∼ 0.28 . Since lens plane rms is not necessarily the best indicator of the quality of the mass reconstructions, looking for an alternative indicator is warranted.

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“…Strong gravitational Lensing (SL, hereafter) is nowadays at the very heart of important issues in modern cosmology, allowing for instance to measure directly the total projected mass distribution (baryonic and dark) of galaxy cluster cores (e.g. Richard et al 2010;Zitrin & Broadhurst 2016;Monna et al 2017;Mahler et al 2017), to image very high redshift sources otherwise too faint to be detected without the grav-E-mail: ana.acebron@lam.fr itational magnification Richard et al 2008;Coe et al 2013;Atek et al 2015) and to constrain the geometry of the Universe D'Aloisio & Natarajan 2011;Magaña et al 2015;Caminha et al 2016) which is one of the long-standing challenges of modern cosmology.…”

Section: Introductionmentioning

confidence: 99%

Hubble Frontier Fields: systematic errors in strong lensing models of galaxy clusters – implications for cosmography

Acebrón

Jullo

Limousin

et al. 2017

Monthly Notices of the Royal Astronomical Society

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Strong gravitational lensing by galaxy clusters is a fundamental tool to study dark matter and constrain the geometry of the Universe. Recently, the HST Frontier Fields program has allowed a significant improvement of mass and magnification measurements but lensing models still have a residual RMS between 0.2" and few arcseconds, not yet completely understood. Systematic errors have to be better understood and treated in order to use strong lensing clusters as reliable cosmological probes. We have analysed two simulated Hubble Frontier Fields-like clusters from the Hubble Frontier Fields Comparison Challenge, Ares and Hera. We use several estimators (relative bias on magnification, density profiles, ellipticity and orientation) to quantify the goodness of our reconstructions by comparing our multiple models, optimized with the parametric software Lenstool, with the input models. We have quantified the impact of systematic errors arising, first, from the choice of different density profiles and configurations and, secondly, from the availability of constraints (spectroscopic or photometric redshifts, redshift ranges of the background sources) in the parametric modelling of strong lensing galaxy clusters and therefore on the retrieval of cosmological parameters. We find that substructures in the outskirts have a significant impact on the position of the multiple images, yielding tighter cosmological contours. The need for wide-field imaging around massive clusters is thus reinforced. We show that competitive cosmological constraints can be obtained also with complex multimodal clusters and that photometric redshifts improve the constraints on cosmological parameters when considering a narrow range of (spectroscopic) redshifts for the sources.

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STRONG-LENSING ANALYSIS OF THE POWERFUL LENSING CLUSTER MACS J2135.2-0102 (z = 0.33)

Cited by 13 publications

References 55 publications

Galaxy and Mass Assembly: A Comparison between Galaxy–Galaxy Lens Searches in KiDS/GAMA

Galaxy and Mass Assembly: A Comparison between Galaxy–Galaxy Lens Searches in KiDS/GAMA

Free-form grale lens inversion of galaxy clusters with up to 1000 multiple images

Hubble Frontier Fields: systematic errors in strong lensing models of galaxy clusters – implications for cosmography

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