用于CCAT深度光谱巡天的光谱数据立方体清洗。I. 相关噪声和滤波对功率谱的影响
Spectral Data-cube Cleaning for CCAT Deep Spectroscopic Survey. I. Effect of correlated noise and filtering on the power spectrum
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中文总结 AI 辅助
研究针对CCAT深度光谱巡天中大气噪声等影响功率谱恢复的问题，采用滤波和分箱流程进行模拟，结果显示该流程能有效抑制噪声，在特定条件下可检测功率谱，对散粒噪声 regime有效，但大尺度测量需改进绘图技术。
中文摘要 AI 辅助
弗雷德·杨亚毫米波望远镜（FYST）上的再电离时代光谱仪（EoR-Spec）将进行CCAT深度光谱巡天（DSS）以对红移的[C II]发射进行线强度映射。大气1/f噪声和仪器系统误差影响功率谱恢复。我们进行了实际的端到端模拟来量化这些影响并评估一种滤波和分箱（F&B）流程。模拟观测包括仪器响应、天体物理发射、大气噪声和观测策略。该流程在低时间频率下将大气1/f噪声抑制约四个数量级，仅留下少量残余相关噪声。对于以50%观测效率运行的单个EoR-Spec模块，DSS预计在散粒噪声主导的尺度（k>0.1 Mpc-1）上检测到[C II]+CO组合功率谱。两个模块全效率运行时，可在所有目标空间尺度上实现检测。传递函数在kgtrsim0.5 Mpc-1时超过80%，在klesssim0.1 Mpc-1时低于20%，表明对大尺度模式有显著抑制。这些结果表明F&B流程对恢复散粒噪声 regime有效，而准确的大尺度聚类测量需要改进的绘图技术。
英文摘要
The Epoch of Reionization Spectrometer (EoR-Spec) on the Fred Young Submillimeter Telescope (FYST) will conduct the CCAT Deep Spectroscopic Survey (DSS) to perform line-intensity mapping of redshifted [C II] emission. Atmospheric $1/f$ noise and instrumental systematics affect power-spectrum recovery. We present realistic end-to-end simulations to quantify these effects and evaluate a Filter-and-Bin (F&B) pipeline. The simulated observations include instrument response, astrophysical emission, atmospheric noise, and observing strategy. The pipeline suppresses atmospheric $1/f$ noise by about four orders of magnitude at low temporal frequencies while leaving only minor residual correlated noise. For a single EoR-Spec module operating at 50% observing efficiency, the DSS is expected to detect the combined [C II] + CO power spectrum on shot-noise-dominated scales ($k > 0.1\,\mathrm{Mpc}^{-1}$). With two modules operating at full efficiency, detections are achievable over all targeted spatial scales. The transfer function exceeds 80% at $k \gtrsim 0.5\,\mathrm{Mpc}^{-1}$ but falls below 20% at $k \lesssim 0.1\,\mathrm{Mpc}^{-1}$, indicating significant suppression of large-scale modes. These results demonstrate that the F&B pipeline is effective for recovering the shot-noise regime, while improved map-making techniques will be required for accurate large-scale clustering measurements.
