arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2202.09201astro-ph.COastro-ph.GAastro-ph.IM
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自动星系-星系强透镜建模:没有留下透镜

Automated galaxy-galaxy strong lens modelling: no lens left behind

Amy Etherington, James W. Nightingale, Richard Massey, XiaoYue Cao, Andrew Robertson, Nicola C. Amorisco, Aristeidis Amvrosiadis, Shaun Cole, Carlos S. Frenk, Q… 展开作者

Amy Etherington, James W. Nightingale, Richard Massey, XiaoYue Cao, Andrew Robertson, Nicola C. Amorisco, Aristeidis Amvrosiadis, Shaun Cole, Carlos S. Frenk, Qiuhan He, Ran Li, Sut-Ieng Tam

摘要:

暗物质和亮物质的分布可以被绘制在星系周围,这些星系通过引力将背景物体透镜成弧形或爱因斯坦环。新的调查将很快观察到数十万个星系透镜,目前的劳动密集型分析方法将无法应对这一挑战。相反,我们开发了一种全自动的贝叶斯方法,我们用它来适应样本的59个镜头成像的哈勃太空望远镜在统一的条件。我们着手于\textit{leave no lens behind},并专注于自动化拟合在少数镜片中失败的方式,描述了对管道的调整,使我们能够推断出准确的镜片模型。我们的生产线最终在我们的样本中匹配了{ \em all} 59个晶状体,成功率很高,因为灾难性离群值会使大样本产生偏差,统计误差很小。机器学习技术可能会进一步改善两个最困难的步骤:减去前景镜头光和初始化第一个近似镜头模型。在此之后,增加模型的复杂性就很简单了。我们发现一个平均$\sim1\%$测量精度的测量爱因斯坦半径的透镜样品{ \em不降低红移}至少$z=0.7$ -形成鲜明对比的其他技术用于研究星系演化,如恒星动力学。我们的\texttt{PyAutoLens}软件是开源的,也安装在欧空局欧几里得任务的科学数据中心。

英文摘要:

The distribution of dark and luminous matter can be mapped around galaxies that gravitationally lens background objects into arcs or Einstein rings. New surveys will soon observe hundreds of thousands of galaxy lenses, and current, labour-intensive analysis methods will not scale up to this challenge. We instead develop a fully automatic, Bayesian method which we use to fit a sample of 59 lenses imaged by the Hubble Space Telescope in uniform conditions. We set out to \textit{leave no lens behind} and focus on ways in which automated fits fail in a small handful of lenses, describing adjustments to the pipeline that allows us to infer accurate lens models. Our pipeline ultimately fits {\em all} 59 lenses in our sample, with a high success rate key because catastrophic outliers would bias large samples with small statistical errors. Machine Learning techniques might further improve the two most difficult steps: subtracting foreground lens light and initialising a first, approximate lens model. After that, increasing model complexity is straightforward. We find a mean $\sim1\%$ measurement precision on the measurement of the Einstein radius across the lens sample which {\em does not degrade with redshift} up to at least $z=0.7$ -- in stark contrast to other techniques used to study galaxy evolution, like stellar dynamics. Our \texttt{PyAutoLens} software is open source, and is also installed in the Science Data Centres of the ESA Euclid mission.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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