利用神经网络对中等分辨率恒星光谱进行去噪处理

Balázs Pál, László Dobos
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引用次数: 0

摘要

我们在中等分辨率的晚期恒星模拟光学光谱上训练了去噪器自动编码神经网络,证明在典型信噪比为每像素 10 的情况下,它可以在典型相对误差为百分之一的情况下构建原始通量。我们的研究表明,相对简单的网络能够学习恒星光谱的特征,同时还能灵活地适应不同的消光值、改变连续面整体形状的通量缺陷以及不同的多普勒频移值。去噪光谱可用于为传统的恒星模板拟合算法寻找初始值,由于评估预训练神经网络的速度明显快于传统的模板拟合,当需要快速分析噪声光谱时,例如在观测过程中,在单个曝光之间,去噪网络就会非常有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Denoising medium resolution stellar spectra with neural networks
We trained denoiser autoencoding neural networks on medium resolution simulated optical spectra of late-type stars to demonstrate that the reconstruction of the original flux is possible at a typical relative error of a fraction of a percent down to a typical signal-to-noise ratio of 10 per pixel. We show that relatively simple networks are capable of learning the characteristics of stellar spectra while still flexible enough to adapt to different values of extinction and fluxing imperfections that modifies the overall shape of the continuum, as well as to different values of Doppler shift. Denoised spectra can be used to find initial values for traditional stellar template fitting algorithms and - since evaluation of pre-trained neural networks is significantly faster than traditional template fitting - denoiser networks can be useful when a fast analysis of the noisy spectrum is necessary, for example during observations, between individual exposures.
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