TY - JOUR
T1 - Lens models under the microscope
T2 - Comparison of Hubble Frontier Field cluster magnification maps
AU - Priewe, Jett
AU - Williams, Liliya L.R.
AU - Liesenborgs, Jori
AU - Coe, Dan
AU - Rodney, Steven A.
N1 - Publisher Copyright:
© 2016 The Authors.
PY - 2017/2/11
Y1 - 2017/2/11
N2 - Using the power of gravitational lensing magnification by massive galaxy clusters, the Hubble Frontier Fields provide deep views of six patches of the high-redshift Universe. The combination of deep Hubble imaging and exceptional lensing strength has revealed the greatest numbers of multiply-imaged galaxies available to constrain models of cluster mass distributions. However, even with O(100) images per cluster, the uncertainties associated with the reconstructions are not negligible. The goal of this paper is to show the diversity of model magnification predictions.We examine seven and nine mass models of Abell 2744 and MACS J0416, respectively, submitted to the Mikulski Archive for Space Telescopes for public distribution in 2015 September. The dispersion between model predictions increases from 30 per cent at common low magnifications (μ ~ 2) to 70 per cent at rare high magnifications (μ ~ 40). MACS J0416 exhibits smaller dispersions than Abell 2744 for 2 < μ < 10. We show that magnification maps based on different lens inversion techniques typically differ from each other by more than their quoted statistical errors. This suggests that some models underestimate the true uncertainties, which are primarily due to various lensing degeneracies. Though the exact mass sheet degeneracy is broken, its generalized counterpart is not broken at least in Abell 2744. Other local degeneracies are also present in both clusters. Our comparison of models is complementary to the comparison of reconstructions of known synthetic mass distributions. By focusing on observed clusters, we can identify those that are best constrained, and therefore provide the clearest view of the distant Universe.
AB - Using the power of gravitational lensing magnification by massive galaxy clusters, the Hubble Frontier Fields provide deep views of six patches of the high-redshift Universe. The combination of deep Hubble imaging and exceptional lensing strength has revealed the greatest numbers of multiply-imaged galaxies available to constrain models of cluster mass distributions. However, even with O(100) images per cluster, the uncertainties associated with the reconstructions are not negligible. The goal of this paper is to show the diversity of model magnification predictions.We examine seven and nine mass models of Abell 2744 and MACS J0416, respectively, submitted to the Mikulski Archive for Space Telescopes for public distribution in 2015 September. The dispersion between model predictions increases from 30 per cent at common low magnifications (μ ~ 2) to 70 per cent at rare high magnifications (μ ~ 40). MACS J0416 exhibits smaller dispersions than Abell 2744 for 2 < μ < 10. We show that magnification maps based on different lens inversion techniques typically differ from each other by more than their quoted statistical errors. This suggests that some models underestimate the true uncertainties, which are primarily due to various lensing degeneracies. Though the exact mass sheet degeneracy is broken, its generalized counterpart is not broken at least in Abell 2744. Other local degeneracies are also present in both clusters. Our comparison of models is complementary to the comparison of reconstructions of known synthetic mass distributions. By focusing on observed clusters, we can identify those that are best constrained, and therefore provide the clearest view of the distant Universe.
KW - Galaxies: clusters: individual: Abell 2744
KW - Galaxies: clusters: individual: MACS J0416
KW - Gravitational lensing: strong
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U2 - 10.1093/mnras/stw2785
DO - 10.1093/mnras/stw2785
M3 - Article
AN - SCOPUS:85007383969
SN - 0035-8711
VL - 465
SP - 1030
EP - 1045
JO - Monthly Notices of the Royal Astronomical Society
JF - Monthly Notices of the Royal Astronomical Society
IS - 1
ER -