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 H0. 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 ($10\!-\!20{{\ \rm per\ cent}}$) while remaining accurate. In Rung 2, the methods that exploit the full information of the imaging and kinematic data sets can recover H0 within the target accuracy (|A| < 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.

中文翻译：

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延时镜头建模挑战

近年来，方法和数据的突破使得引力时延成为测量哈勃常数 H0 的非常有力的工具。然而，已发表的最先进的分析需要专家调查员 1 年的时间和每个系统最多 100 万小时的计算时间。此外，随着精度的提高，识别和减轻系统不确定性至关重要。在这次延时镜头建模挑战中，我们旨在通过对模拟数据集的盲分析来评估目前足够快以处理 50 颗镜头的建模技术的精度和准确度水平。梯级 1 和 2 中的结果表明，仅使用点源位置的方法往往具有较低的精度 ($10\!-\!20{{\ \rm per\ cent}}$)，但仍保持准确。在梯级 2 中，利用成像和运动学数据集的全部信息的方法可以在目标精度（|A| < 2%）和精度（每个系统<6%）范围内恢复 H0，即使存在较差的已知点扩散函数和复杂的源形态。对梯级 3 的揭盲后分析表明，光线追踪宇宙学模拟的数值精度不足以在百分比水平上测试透镜建模方法，使得结果难以解释。需要改进模拟的新挑战才能在系统不确定性的研究中取得进一步进展。为了完整起见，我们在附录中展示了 Rung 3 结果，并使用它们来讨论在未来盲人挑战中减轻类似微妙数据生成影响的各种方法。