AI 中文总结
研究利用模拟检验堆叠动力学SZ效应解释的假设，聚焦速度，将信号分解，探讨不同速度重建方式下非线性项对信号的影响，揭示保留小尺度信息与保守重建在信噪比和解释简化上的权衡。
AI 中文摘要
堆叠动力学Sunyaev-Zel'dovich（kSZ）信号探测星系周围速度加权的投影气体动量，正成为探测气体分数和重子反馈的有力工具。但其解释依赖多个假设，本文对此进行检验。利用FLAMINGO流体动力学模拟和类似DESI的星系模拟，针对不同星系样本确定建模信号所需成分。本文聚焦速度，将信号分解为主导的整体流项和非线性项。仅从实空间线性信息重建堆叠速度时，非线性项抵消，信号与平均光学深度偏差在百分之几内；保留非线性信息或受红移空间畸变影响时，非线性项使信号降低10 - 20%，对当前数据为1 - 2σ效应，对未来调查预计有统计学意义且对重子反馈依赖弱。结果揭示了一种权衡：保留小尺度信息的速度估计器提高信噪比但需基于模拟的建模，保守重建简化解释但以信噪比为代价。
英文摘要
The stacked kinetic Sunyaev-Zel'dovich (kSZ) signal probes the velocity-weighted projected gas momentum around galaxies, and is emerging as a powerful probe of gas fractions and baryonic feedback. Its interpretation, however, rests on several assumptions that we test in this pair of companion papers. Using the FLAMINGO hydrodynamical simulations and DESI-like galaxy mocks for luminous red galaxies (LRGs), the bright galaxy sample (BGS), and emission-line galaxies (ELGs), we identify the ingredients required to model the signal to better than $10\%$. This first paper focuses on velocities. We decompose the signal into a dominant bulk-flow term, proportional to the mean optical depth, plus non-linear terms arising from the small-scale gas momentum and its coupling to the stacking velocity. When the stacking velocities are reconstructed from linear information in real space alone -- an idealisation which is not possible in practice -- the non-linear terms cancel and the signal traces the mean optical depth to within a few per cent. When the stacking velocity instead retains non-linear information or is affected by redshift-space distortions, the non-linear terms suppress the signal by $10-20\%$: a $1-2σ$ effect for current data that is expected to be statistically significant for upcoming surveys, and one that depends only weakly on baryonic feedback. Our results reveal a trade-off: velocity estimators that retain small-scale information boost signal-to-noise but require simulation-based modelling, whereas conservative reconstructions simplify the interpretation at the cost of signal-to-noise.
Comments16 pages, 8 figures
