超分辨赫歇尔成像：使用深度神经网络的概念证明
Super-resolving Herschel imaging: a proof of concept using Deep Neural Networks
摘要：
在过去的十年中，宽视场亚毫米巡天在星系演化方面取得了许多重大进展，但由于没有广泛的后续观测，这些巡天的粗角分辨率限制了科学利用。这推动了各种解析解卷积方法的发展。在过去的五年中，生成对抗网络已被用于尝试对光学数据进行反卷积。在这里，我们提出了一种新的损耗函数，以克服这个问题，在亚毫米波长范围内的自动编码器。这种方法在Herschel SPIRE 500 $μ$ m COSMOS数据上得到了成功的验证，超分辨目标是JCMT SCUBA-2 450 $μ$ m观测的同一场。我们重现JCMT SCUBA-2图像高保真使用此自动编码器。这通过点源通量和位置、完整性和纯度来量化。
英文摘要：
Wide-field sub-millimetre surveys have driven many major advances in galaxy evolution in the past decade, but without extensive follow-up observations the coarse angular resolution of these surveys limits the science exploitation. This has driven the development of various analytical deconvolution methods. In the last half a decade Generative Adversarial Networks have been used to attempt deconvolutions on optical data. Here we present an autoencoder with a novel loss function to overcome this problem in the sub-millimeter wavelength range. This approach is successfully demonstrated on Herschel SPIRE 500$μ$m COSMOS data, with the super-resolving target being the JCMT SCUBA-2 450$μ$m observations of the same field. We reproduce the JCMT SCUBA-2 images with high fidelity using this autoencoder. This is quantified through the point source fluxes and positions, the completeness and the purity.
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