发表机构
INAF–OAS, Osservatorio di Astrofisica e Scienza dello Spazio di Bologna; INFN–Sezione di Bologna; Ciela – Montreal Institute for Astrophysical Data Analysis and Machine Learning; Mila – Quebec Artificial Intelligence Institute; Department of Physics, Université de Montréal; Dipartimento di Fisica e Astronomia "A. Righi", Alma Mater Studiorum Università di Bologna; Dipartimento di Fisica, Università degli Studi di Milano（意大利国家天体物理研究所-博洛尼亚天文与空间科学观测站; 意大利国家核物理研究所-博洛尼部分部; Ciela-蒙特利尔天体物理学数据分析与机器学习研究所; Mila-魁北克人工智能研究所; 蒙特利尔大学物理系; 博洛纳大学A. Righi物理与天文学系; 米兰大学物理系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种新颖的星系团强透镜模拟代码，利用观测经验关系建模成员星系，结合MPI并行和扩散模型增强，生成百张模拟图像以验证分析方法和训练机器学习算法。
AI 中文摘要
星系团是最强大的强引力透镜：它们极大地放大了遥远且暗淡源的通量。强引力透镜还允许以约1%的精度重建其质量分布，并有助于研究宇宙学参数。得益于宽视场成像巡天，这类已知系统的数量在未来几年必将增加。在此背景下，使用模拟对于在实际数据上应用分析方法之前验证这些方法至关重要，并且对于训练能够处理大量图像的机器学习算法也至关重要。在这项工作中，我们展示了一组由一百个星系团图像组成的模拟数据集，这些图像是我们用一种新颖的模拟星系团尺度强透镜的代码生成的。我们方法的主要创新之一，也是与其他现有代码的区别所在，是使用源自最新观测的经验关系来建模星系团成员星系的特性，例如形态、颜色和空间分布。这使我们能够可靠地再现真实观测的复杂性。模拟部分使用最新版本的SkyLens进行，该代码可在不同系统和观测设置下创建强透镜事件的模拟观测。我们引入的主要改进包括使用消息传递接口（MPI）范式（一种利用多个处理器执行给定任务的并行编程标准），以及实现基于分数的扩散模型来增强背景源的图像。这些改进共同带来了更高效、更真实的图像模拟。我们还通过将模拟星系团的属性与真实星系团的属性进行比较，展示了代码和模拟的验证。[已删节]
英文摘要
Galaxy clusters are the most powerful strong lenses: they greatly magnify the flux of distant and faint sources. Strong lensing also allows for the reconstruction of their mass distribution with-1% level accuracy and enables investigating cosmological parameters. The number of known systems of this type is bound to increase in the next years thanks to wide imaging surveys. Using simulations in this context is crucial to validate the analysis methods before they are applied to real data, and to train machine learning algorithms that can handle large volumes of images. In this work, we present a simulated set of one hundred images of galaxy clusters that we have produced with a novel code for simulating cluster-scale strong lenses. One of the main novelties of our approach, distinguishing it from other existing codes, is the use of empirical relations, derived from state-of-the-art observations, for modelling the characteristics, such as morphology, color, and spatial distribution of the cluster member population. This allows us to reliably reproduce the complexity of real observations. The simulations are partly carried out with the latest version of SkyLens, a code that creates mock observations of strong lensing events in different systems and observational setups. The main improvements we introduce are the use of the message passage interface (MPI) paradigm, a standard for parallel programming that leverages the use of several processors to perform some given task, and the implementation of score-based diffusion models to augment the images of the background sources. Together, they lead to more efficient and realistic image simulations. We also present the validation of the code and simulations by comparing the properties of the mock clusters to those of real ones. [Abridged]
CommentsAccepted for publication in Astronomy & Astrophysics (15 pages, 10 figures), comments are welcome
