AI 中文总结
本研究针对SDSS-V LVM抖动观测数据的三维重建需求，提出3DCubeGen工具，合并海量LVM抖动数据以提升数据质量，为该巡天的科学应用提供关键支撑。
AI 中文摘要
斯隆数字巡天第五期（SDSS-V）本地体积测绘仪（LVM）正在开展前所未有的广域积分场光谱巡天，观测对象包括银河系、麦哲伦云及邻近星系，采用多次抖动观测策略以实现完整空间覆盖、提升空间采样精度并增强光谱深度。然而，对这些观测数据的科学利用需要一套可靠方法，将单个行堆叠光谱（RSS）合并为均匀的三维数据立方体。本研究提出3DCubeGen，这是一款灵活且可扩展的重建工具，设计用于合并多个LVM抖动数据，同时保持流量并传递不确定性。该方法支持合并大型数据集，提升信噪比、增强空间分辨率并提高对微弱发射特征的灵敏度，遵循并扩展了CALIFA等积分场巡天中已实现的方法。我们将3DCubeGen应用于大量LVM观测数据，包括大麦哲伦云、小麦哲伦云及邻近星系，合并对应数百万条光谱的数千次抖动数据。生成的数据产品在空间采样和光谱深度上展现出显著提升，支持对延展区域内的电离气体、恒星族群及运动学进行详细研究。3DCubeGen为LVM数据立方体重建提供了可靠且可扩展的解决方案，是挖掘SDSS-V本地体积测绘仪科学潜力的关键工具。
英文摘要
The Sloan Digital Sky Survey V (SDSS-V) Local Volume Mapper (LVM) is conducting an unprecedented wide-field integral field spectroscopic survey of the Milky Way, the Magellanic Clouds, and nearby galaxies using a strategy based on multiple dithered observations to achieve full spatial coverage, improved spatial sampling, and enhanced spectral depth. However, the scientific exploitation of these observations requires a robust methodology to combine the individual row-stacked spectra (RSS) into homogeneous three-dimensional data cubes. In this work, we present 3DCubeGen, a flexible and scalable reconstruction tool designed to combine multiple LVM dithers while preserving flux and propagating uncertainties. The method enables the coaddition of large datasets, improving the signal-to-noise ratio, enhancing spatial resolution, and increasing sensitivity to faint emission features, following and extending approaches previously implemented in integral field surveys such as CALIFA. We apply 3DCubeGen to a large set of LVM observations, including the Large and Small Magellanic Clouds and nearby galaxies, combining thousands of dithers corresponding to millions of spectra. The resulting data products demonstrate significant improvements in spatial sampling and spectral depth, enabling detailed studies of the ionised gas, stellar populations, and kinematics across extended regions. 3DCubeGen provides a robust and scalable solution for LVM data cube reconstruction and represents a key tool for exploiting the scientific potential of the SDSS-V Local Volume Mapper.
CommentsSubmitted for publication to RevMexAA; currently under review
