Detecting and Counting of Each Nested Curve on Spectrum with Gaussian Package

Y. Kocak, S. Sevgen, R. Samli
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Abstract

Spectral analysis plays an important role in the interpretation of astronomical data. The determination of the number and parameters of each nested Gaussian curve on spectrum of any object in the sky gives information about the physical and chemical structure of the object. In this study, determination of each nested Gaussian curve’s number and parameters such as position, width and amplitude in a given frequency range are performed. Thus, we have knowledge of about, which molecules there are in the observation region. Autonomous Gaussian Decomposition algorithm is implemented with additional scipy, numpy, h5py, lmfit packages. The algorithm is tested on spectral data that comes from active star forming region called as Orion KL obtained by SMA (Submillimeter Array) devices are used. Finally, information is given about positive and negative aspects of these study and in the future what should we do to improve the accuracy.
基于高斯包的谱上嵌套曲线的检测与计数
光谱分析在天文数据的解释中起着重要的作用。确定天空中任何物体光谱上每条嵌套高斯曲线的数目和参数,就能得到该物体的物理和化学结构的信息。在本研究中,确定每条嵌套高斯曲线的数量和在给定频率范围内的位置、宽度和幅度等参数。这样,我们就知道在观测区域有哪些分子。自主高斯分解算法通过附加的scipy、numpy、h5py、lmfit包实现。利用SMA(亚毫米波阵列)装置获得的猎户座KL活跃恒星形成区光谱数据对算法进行了验证。最后,给出了这些研究的积极和消极方面的信息,以及在未来我们应该做些什么来提高准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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