论文标题

J-Comb:一种图像融合算法,用于结合观测值,涵盖不同的空间频率范围

J-comb: An image fusion algorithm to combine observations covering different spatial frequency ranges

论文作者

Jiao, Sihan, Lin, Yuxin, Shui, Xiangyu, Wu, Jingwen, Ren, Zhiyuan, Li, Di

论文摘要

地面的高分辨率(亚)毫米连续映射观测值对空间扩展的目标来源通常会受到明显缺失的通量。这阻碍了准确的定量分析。可以通过将高分辨率图像与保留扩展结构的观测值融合来恢复缺失通量。但是,通常采用的图像融合方法不能保持光束响应函数的简单性,也不会试图详细说明屈服的光束响应函数的细节。这些使观测值在多个波长不直接的情况下进行比较。我们提出了一种新的算法J-Comb,该算法将高分辨率图像线性结合在一起。通过将锥度函数应用于低通滤波的图像,并使用适当的权重将其与高通滤波图像结合在一起,可以保证我们组合图像的光束响应函数可以具有接近高斯的形状。这使得在多个波长的观测值中容易地共享相同的光束响应函数。此外,我们介绍了一种策略,以解决现在日期基于地面的侧仪仪的850 um成像的特定问题,并且使用普朗克卫星采取的仪器不会重叠在傅立叶域中。我们对其他两种广泛使用的图像组合算法,Casa-Feather和Miriad-Immerge进行了基准测试,并对恒星形成分子云进行了模拟观察。我们证明J-Comb算法的性能优于其他两种算法的性能。我们将J-Comb算法应用于Orion A形成区域的实际观察数据。我们成功地产生了灰尘温度和柱密度图,其角度分辨率〜1英寸,揭示了比以前的结果更大的细节。

Ground-based, high-resolution bolometric (sub)millimeter continuum mapping observations on spatially extended target sources are often subject to significant missing fluxes. This hampers accurate quantitative analyses. Missing flux can be recovered by fusing high-resolution images with observations that preserve extended structures. However, the commonly adopted image fusion approaches do not maintain the simplicity of the beam response function and do not try to elaborate the details of the yielded beam response functions. These make the comparison of the observations at multiple wavelengths not straightforward. We present a new algorithm, J-comb, which combines the high and low-resolution images linearly. By applying a taper function to the low-pass filtered image and combining it with a high-pass filtered image using proper weights, the beam response functions of our combined images are guaranteed to have near-Gaussian shapes. This makes it easy to convolve the observations at multiple wavelengths to share the same beam response functions. Moreover, we introduce a strategy to tackle the specific problem that the imaging at 850 um from the present-date ground-based bolometric instrument and that taken with the Planck satellite do not overlap in the Fourier domain. We benchmarked our method against two other widely-used image combination algorithms, CASA-feather and MIRIAD-immerge, with mock observations of star-forming molecular clouds. We demonstrate that the performance of the J-comb algorithm is superior to those of the other two algorithms. We applied the J-comb algorithm to real observational data of the Orion A star-forming region. We successfully produced dust temperature and column density maps with ~10" angular resolution, unveiling much greater details than the previous results.

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