AI 中文总结
本研究提出端到端聚类红移框架Tomographer，通过预计算激活图消除配对计数等技术障碍，可从源星表或强度图输出偏差加权红移分布，经多场景验证具准确性与鲁棒性，适用于多类数据及波段应用。
AI 中文摘要
红移信息是天空巡天几乎所有河外及宇宙学应用的核心，但仅小部分已编目源拥有光谱红移，形成弥散背景的光子则全无光谱红移。基于聚类的红移推断通过与已知红移参考样本的空间交叉关联估计任意数据集的红移分布，仅依赖位置信息，可适用于包括源种群和弥散强度图在内的任意大尺度结构示踪物。然而其更广泛应用受限于技术障碍：组装和表征光谱参考样本、昂贵的配对计数计算、理论修正及系统控制。为消除这些障碍，我们提出Tomographer，一个端到端的聚类红移框架。其关键设计特征是一组预计算的“激活图”，编码了10000平方度范围内、红移z≈4以内的300万 SDSS 光谱星系和类星体的空间配对信息。这消除了用户端的配对计数，将计算复杂度从O(N log N)降至O(1)的图乘法。给定源星表或强度图，Tomographer返回偏差加权的红移分布，即b(z) dN/dz(z)或b(z) dI/dz(z)。我们针对已知红移样本验证该框架，证明其不确定性估计准确，且对巡天天区、空间变化的选择函数、束平滑和前景污染具有鲁棒性。我们展示了其在流量、颜色、测光红移、形态或变异性选择的源星表，以及从射电到X射线的强度图上的应用。未来的Tomographer版本将在可用时纳入更多宽场光谱参考样本。
英文摘要
Redshift information is central to nearly every extragalactic and cosmological application of sky surveys, yet only a small fraction of cataloged sources, and none of the photons forming diffuse backgrounds, have spectroscopic redshifts. Clustering-based redshift inference estimates the redshift distribution of an arbitrary dataset through spatial cross-correlation with a reference sample of known redshifts. It relies only on positional information, making it applicable to any tracer of large-scale structure, including source populations and diffuse intensity maps. Its broader adoption, however, has been limited by technical barriers: assembling and characterizing spectroscopic references, expensive pair-counting computations, theoretical corrections, and systematic control. To remove these barriers, we introduce Tomographer, an end-to-end clustering-redshift framework. The key design feature is a set of precomputed "activation maps" encoding the spatial pair information of 3 million SDSS spectroscopic galaxies and quasars up to $z\sim4$ over $10{,}000\,{\rm deg}^2$. This eliminates user-end pair counting, reducing computational scaling from $O(N\log N)$ to $O(1)$ map multiplications. Given a source catalog or intensity map, Tomographer returns the bias-weighted redshift distribution, $b(z)\,{\rm d}N/{\rm d}z(z)$ or $b(z)\,{\rm d}I/{\rm d}z(z)$. We validate the framework against samples with known redshifts, demonstrate accurate uncertainty estimates, and show robustness to survey footprint, spatially varying selection functions, beam smoothing, and foreground contamination. We showcase applications to source catalogs selected by flux, color, photometric redshift, morphology, or variability, and to intensity maps from radio to X-rays. Future Tomographer releases will incorporate additional wide-field spectroscopic reference samples as they become available.
Commentssubmitted to ApJ, 28 pages, 14 figures, code available at https://github.com/yuvoonng/tomographer
