刻画LAMOST的仪器轮廓
Characterizing the Instrumental Profile of LAMOST
AI总结：
本文提出利用神经网络构建LAMOST仪器轮廓模型，通过训练提高精度，降低径向速度测量的分散度，促进长周期双星搜索。
摘要：
望远镜的仪器轮廓（IP）对光谱分析至关重要，尤其对波长校准和恒星参数测量。LAMOST使用弧灯进行波长校准，这些灯产生已知波长的尖锐发射线，观测到的弧灯光谱可很好地表征IP。然而，IP受多种因素影响，传统方法难以准确建模。神经网络能自动捕捉数据中的复杂模式和非线性特征，为高精度IP测量提供新途径。本文基于Payne神经网络构建多层感知机（MLP）以推导LAMOST的IP。训练后，模型可检索任意光纤、任意波长和任意时间的IP。随后将推导的IP应用于恒星径向速度（RV）测量，并分析不同IP中心定位方法对结果的影响。最终，测量的RV分散度减少约3 km/s。此改进将促进通过RV变化搜索长周期双星。
英文摘要：
The instrumental profile (IP) of a telescope is of great significance for spectroscopic analyses, especially for wavelength calibration and stellar parameter measurements. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) employs arc lamps for wavelength calibration. These lamps produce sharp emission lines with known wavelengths, and the observed arc lamp spectra can well characterize the IP. However, IPs are influenced by multiple factors, making them difficult to model accurately with traditional methods. Neural networks, which can automatically capture complex patterns and nonlinear features in data, provide a promising approach for high-precision IP measurement. We therefore construct a multi-layer perceptron (MLP) based on The Payne neural network to derive IPs for LAMOST. After training, the model can retrieve the IP for any fiber, at any wavelength, and at any time. We then apply the derived IP to stellar radial velocity (RV) measurements and analyze the impact of different IP center localization methods on the results. Finally, the dispersion of the measured RVs is reduced by approximately 3 km/s. This improvement will facilitate the search for long-period binary stars via RV variations.
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