遇见数据集

Unlabeled Rung 0 Dataset for Roman Strong Lens Data Challenge

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Zenodo2026-05-16 更新2026-05-29 收录
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Data Challenge Overview The Roman Space Telescope is expected to observe O(10^5) galaxy-galaxy strong gravitational lenses, providing high angular resolution images of galaxy-galaxy strong gravitational lenses that can be used to probe the nature of dark matter at sub-galactic scales (Daylan and Birrer 2023, Wedig et al. 2025). The Roman Data Challenge for Dark Matter Substructure with Galaxy-Galaxy Strong Gravitational Lenses provides realistic simulated Roman images of strong lenses with various dark matter substructure populations and challenges the community to test out substructure detection and characterization pipelines. Dataset Description The goal of this rung is to serve as a tutorial for participants. You will familiarize yourself with the systematics of the challenge (e.g. where and how to submit, how to set up mejiro and obtain/generate datasets, where to find relevant documentation) and for us as the organizers to make sure that the data challenge runs smoothly. In this rung, you will train a regression model to determine the Einstein radius of lenses. This is the unlabeled dataset. It does not include the Einstein radius and a few other related parameters that were included in the labeled dataset. Rung 0 submissions will be scored for this dataset. Changelog v1.1: Correction of minor typos in Jupyter notebook v1.0: Initial version

数据挑战赛概述 罗曼空间望远镜(Roman Space Telescope)预计将观测O(10^5)量级的星系-星系强引力透镜(galaxy-galaxy strong gravitational lenses),获取此类星系-星系强引力透镜的高角分辨率图像,以探究亚星系尺度下的暗物质性质(Daylan与Birrer,2023;Wedig等人,2025)。基于星系-星系强引力透镜的暗物质亚结构罗曼数据挑战赛(Roman Data Challenge for Dark Matter Substructure with Galaxy-Galaxy Strong Gravitational Lenses)提供了包含各类暗物质亚结构群体的逼真模拟罗曼望远镜强引力透镜图像,并面向学界开展亚结构探测与特征刻画流水线的测试挑战。 数据集说明 本关卡旨在为参赛者提供入门教程:参与者将熟悉本次挑战赛的各项规范(例如提交渠道与方式、如何配置mejiro工具并获取/生成数据集、何处查阅相关文档),同时也便于主办方确保挑战赛顺利开展。在本关卡中,参赛者需训练回归模型以测定透镜的爱因斯坦半径(Einstein radius)。 本数据集为无标注数据集,未包含标注数据集中原有的爱因斯坦半径及其他若干相关参数。第0关的提交结果将基于本数据集进行评分。 更新日志 v1.1:修正了Jupyter Notebook中的少量拼写错误 v1.0:初始版本

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2026-05-16
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