发表机构
University of California, Santa Cruz; University of California, Los Angeles; W. M. Keck Observatory（加州大学圣克鲁兹分校; 加州大学洛杉矶分校; W. M. 凯克天文台）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文对SCALES DRP的中分辨率IFS模式开展性能验证，采用其低温测试数据，验证了校正矩阵性能与提取算法鲁棒性，表明该流水线可用于仪器调试及早期科学运行，代码在GitHub公开。
AI 中文摘要
我们利用近期低温冷却测试期间获取的数据，对SCALES数据处理流水线（SCALES-DRP）的中分辨率积分场光谱仪（IFS）模式开展性能验证。SCALES（系外行星光谱学用透镜阵列结合切片器，Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy）计划于2026年末安装在凯克II望远镜上，将成为首个设施级高对比度热红外IFS，工作波长范围为2-5微米。SCALES-DRP在凯克数据处理流水线框架内开发，采用Python实现，利用与位置相关的单色透镜点扩散函数推导的校正矩阵，将原始探测器读数转换为校准后的三维数据立方体，同时支持基于最优和卡方的光谱提取，以最大化信噪比和提取保真度。我们给出了中分辨率IFS模式的实验室验证结果，该模式结合了0.34×0.36角秒的透镜子阵列与图像切片器，实现了R≈2000-5000的光谱分辨率，重点关注校正矩阵的性能及提取算法的鲁棒性。这些结果表明SCALES-DRP已可用于仪器调试及早期科学运行，该流水线在GitHub上公开可用。
英文摘要
We present a performance verification of the medium-resolution integral field spectrograph (IFS) mode of the SCALES Data Reduction Pipeline (SCALES-DRP) using data obtained during recent cryogenic cooldown testing. SCALES (Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy), scheduled for deployment on the Keck II Telescope in late 2026, will be the first facility-class high-contrast thermal-infrared IFS, operating over the 2-5 micrometer wavelength range. The SCALES-DRP, developed within the Keck Data Reduction Pipeline Framework and implemented in Python, converts raw detector reads into calibrated three-dimensional datacubes using rectification matrices derived from position-dependent monochromatic lenslet point-spread functions, and supports both optimal and chi-sqaure-based spectral extraction to maximize signal-to-noise and extraction fidelity. We present laboratory verification results for the medium-resolution IFS mode, which combines a 0.34x0.36 arcsec lenslet subarray with an image slicer to achieve spectral resolutions of R ~ 2,000 - 5,000, focusing on the performance of the rectification matrices and the robustness of the extraction algorithms. These results demonstrate the readiness of the SCALES-DRP for instrument commissioning and early science operations. The pipeline is publicly available on GitHub.
