Wide-field submillimetre 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 auto-encoder with a novel loss function to overcome this problem in the submillimeter wavelength range. This approach is successfully demonstrated on Herschel SPIRE 500 $\mu\mathrm{m}$ COSMOS data, with the superresolving target being the JCMT SCUBA-2 450 $\mu\mathrm{m}$ observations of the same field. We reproduce the JCMT SCUBA-2 images with high fidelity using this auto-encoder. This is quantified through the point source fluxes and positions, the completeness, and the purity.

中文翻译：

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超分辨率赫歇尔成像：使用深度神经网络的概念证明

在过去十年中，广域亚毫米测量推动了星系演化的许多重大进展，但如果没有广泛的后续观察，这些测量的粗角分辨率限制了科学开发。这推动了各种分析反卷积方法的发展。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，我们提出了一种具有新颖损失函数的自动编码器，以克服亚毫米波长范围内的这个问题。这种方法在 Herschel SPIRE 500 $\mu\mathrm{m}$ COSMOS 数据上成功演示，超分辨目标是 JCMT SCUBA-2 450 $\mu\mathrm{m}$ 对同一场的观测。我们使用此自动编码器以高保真度再现 JCMT SCUBA-2 图像。