自治高斯分解
Autonomous Gaussian Decomposition
摘要：
我们提出了一种新的算法，命名为自治高斯分解（AGD），自动分解成高斯成分的光谱。AGD使用导数光谱和机器学习来提供对数据中高斯分量数量的优化猜测，以及它们的位置，宽度和幅度。我们测试了AGD，发现它产生的结果与来自EVLA（21-SPONGE）巡天中性气体21 cm谱线观测的21 cm吸收谱的人源解相当。我们使用AGD与Monte Carlo方法推导出HI线完整性作为峰值光学深度和速度宽度的函数的21-SPONGE数据，也表明，AGD的结果是稳定的，对不同的观测噪声强度。该方法相对于传统手动高斯拟合的自主性和计算效率允许在观测和模拟之间进行真正无偏的比较，并且能够扩展和解释来自即将到来的平方公里阵列和探路者望远镜的非常大的数据量。
英文摘要：
We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian components in the data, and also their locations, widths, and amplitudes. We test AGD and find that it produces results comparable to human-derived solutions on 21cm absorption spectra from the 21cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the HI line completeness as a function of peak optical depth and velocity width for the 21-SPONGE data, and also show that the results of AGD are stable against varying observational noise intensity. The autonomy and computational efficiency of the method over traditional manual Gaussian fits allow for truly unbiased comparisons between observations and simulations, and for the ability to scale up and interpret the very large data volumes from the upcoming Square Kilometer Array and pathfinder telescopes.
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