Cutana：面向拍字节级天文图像切图生成的高性能工具
Cutana: A High-Performance Tool for Astronomical Image Cutout Generation at Petabyte Scale
AI总结：
本文提出Cutana，一款基于向量化NumPy和内存感知调度的批处理切图工具，可在四小时内用四线程处理欧几里得Q1全部数据，性能显著优于Astropy。
摘要：
欧几里得快速数据发布1（Q1）涵盖63.1平方度内的3000万个源，标志着通过数据发布1（DR1）及后续发布进入拍字节级数据交付阶段。对此类数据集的系统性利用需要提取数百万个特定源的切图，然而Astropy的Cutout2D等标准工具逐个处理源，为大型星表带来了瓶颈。我们提出Cutana，这是一款内存高效的软件工具，针对本地和云原生环境中的批处理进行了优化。Cutana采用向量化NumPy运算，从FITS瓦片中同时批量提取切图，实现了自动内存感知调度，并支持Zarr和FITS输出格式以及多种常用归一化方案（asinh、log、zscale）。在所有测试的Q1子集场景中，Cutana均优于Astropy，实现了近线性扩展，每秒可处理数千个切图。仅使用四个工作线程，Cutana即可在四小时内处理完整个Q1。该工具包含一个用于参数配置和实时监控的ipywidget界面。目前正与ESA Datalabs集成以用于欧几里得DR1发布，开源发布有待ESA开源许可流程完成。
英文摘要：
The Euclid Quick Data Release 1 (Q1) encompasses 30 million sources across 63.1 square degrees, marking the beginning of petabyte-scale data delivery through Data Release 1 (DR1) and subsequent releases. Systematic exploitation of such datasets requires extracting millions of source-specific cutouts, yet standard tools like Astropy's Cutout2D process sources individually, creating bottlenecks for large catalogues. We introduce Cutana, a memory-efficient software tool optimised for batch processing in both local and cloud-native environments. Cutana employs vectorised NumPy operations to extract cutout batches simultaneously from FITS tiles, implements automated memory-aware scheduling, and supports both Zarr and FITS output formats with multiple common normalisation schemes (asinh, log, zscale). Cutana outperforms Astropy in all tested Q1 subset scenarios achieving near linear scaling and processing thousands of cutouts per second. On just four worker threads, Cutana can process all of Q1 in under four hours. The tool includes an ipywidget interface for parameter configuration and real-time monitoring. Integration with ESA Datalabs is underway for the Euclid DR1 release, with open-source release pending ESA open-source licensing processes.
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