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Time delay lens modelling challenge

delete2021-02-22
delete30
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OA
AI
X
Xuheng Ding *
T
Tommaso Treu
S
Simon Birrer
G
Geoff C.-F. Chen
J
Jonathan Coles
P
Philipp Denzel
M
Matteo Frigo
A
A. Galan
P
Philip J. Marshall
M
Martin Millon
A
Anupreeta More
A
Anowar J. Shajib
D
Dominique Sluse
H
Hyungsuk Tak
D
D. Xu
B
Bonvin, V
H
Hum Chand
F
F. Courbin
G
Giulia Despali
C
C. D. Fassnacht
D
Daniel Gilman
S
Stefan Hilbert
S
Sushil Kumar
J
Joshua Lin
J
Ji Won Park
P
Prasenjit Saha
V
Van de Vyvere, L.
L
Liliya L. R. Williams
DOI:10.1093/mnras/stab484delete
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摘要

摘要

En 中文
In recent years, breakthroughs in methods and data have enabled gravitational time delays to emerge as a very powerful tool to measure the Hubble constant H-0. However, published state-of-the-art analyses require of order 1 yr of expert investigator time and up to a million hours of computing time per system. Furthermore, as precision improves, it is crucial to identify and mitigate systematic uncertainties. With this time delay lens modelling challenge, we aim to assess the level of precision and accuracy of the modelling techniques that are currently fast enough to handle of order 50 lenses, via the blind analysis of simulated data sets. The results in Rungs 1 and 2 show that methods that use only the point source positions tend to have lower precision (10-20 per cent) while remaining accurate. In Rung 2, the methods that exploit the full information of the imaging and kinematic data sets can recover H-0 within the target accuracy (vertical bar A vertical bar < 2 per cent) and precision (<6 per cent per system), even in the presence of a poorly known point spread function and complex source morphology. A post-unblinding analysis of Rung 3 showed the numerical precision of the ray-traced cosmological simulations to be insufficient to test lens modelling methodology at the percent level, making the results difficult to interpret. A new challenge with improved simulations is needed to make further progress in the investigation of systematic uncertainties. For completeness, we present the Rung 3 results in an appendix and use them to discuss various approaches to mitigating against similar subtle data generation effects in future blind challenges.
Keyword:
gravitational lensing: strong
methods: data analysis
cosmology: observations

期刊

Monthly Notices of the Royal Astronomical Society 封面图
Monthly Notices of the Royal Astronomical Society
IF:
4.8
论文数:
7.0W
被引数:
25.0W

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U
University of Tokyo
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pennsylvania state university - university park
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Stanford University
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university of zurich
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university of california davis
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university of california los angeles
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Pennsylvania State University
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Ecole Polytechnique Federale de Lausanne
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pennsylvania commonwealth system of higher education (pcshe)
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Max Planck Society
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