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Multi-resolution Filtering: An Empirical Method for Isolating Faint, Extended Emission in Dragonfly Data and Other Low Resolution Images
Publications of the Astronomical Society of the Pacific ( IF 3.3 ) Pub Date : 2020-06-11 , DOI: 10.1088/1538-3873/ab9416
Pieter van Dokkum 1 , Deborah Lokhorst 2 , Shany Danieli 3 , Jiaxuan Li 4 , Allison Merritt 5 , Roberto Abraham 2 , Colleen Gilhuly 2 , Johnny P. Greco 6 , Qing Liu 2
Affiliation  

We describe an empirical, self-contained method to isolate faint, large-scale emission in imaging data of low spatial resolution. Multi-resolution filtering (MRF) uses independent data of superior spatial resolution to create a model for all compact and high surface brightness objects in the field. This model is convolved with an appropriate kernel and subtracted from the low resolution image. The halos of bright stars are removed in a separate step and artifacts are masked. The resulting image only contains extended emission fainter than a pre-defined surface brightness limit. The method was developed for the Dragonfly Telephoto Array, which produces images that have excellent low surface brightness sensitivity but poor spatial resolution. We demonstrate the MRF technique using Dragonfly images of a satellite of the spiral galaxy M101, the tidal debris surrounding M51, and two ultra-diffuse galaxies in the Coma cluster. As part of the analysis we present a newly-identified very faint galaxy in the filtered Dragonfly image of the M101 field. We also discuss variations of the technique for cases when no low resolution data are available (self-MRF and cross-MRF), and introduce a new method for robustly measuring the surface brightness depth of images. All codes are implemented in mrf, an open-source MIT licensed Python package.

中文翻译:

多分辨率滤波:一种在蜻蜓数据和其他低分辨率图像中隔离微弱、扩展发射的经验方法

我们描述了一种经验性的、独立的方法来隔离低空间分辨率的成像数据中的微弱、大规模的发射。多分辨率滤波 (MRF) 使用具有卓越空间分辨率的独立数据为现场所有紧凑且高表面亮度的物体创建模型。该模型与适当的内核卷积并从低分辨率图像中减去。明亮恒星的光晕在单独的步骤中被去除,伪影被掩盖。生成的图像仅包含比预定义的表面亮度限制更暗的扩展发射。该方法是为蜻蜓长焦阵列开发的,该阵列产生的图像具有出色的低表面亮度灵敏度但空间分辨率较差。我们使用螺旋星系 M101 卫星的蜻蜓图像演示 MRF 技术,M51周围的潮汐碎片,以及彗发星团中的两个超扩散星系。作为分析的一部分,我们在 M101 场的过滤蜻蜓图像中展示了一个新发现的非常微弱的星系。我们还讨论了在没有低分辨率数据可用(自 MRF 和交叉 MRF)的情况下该技术的变化,并介绍了一种用于稳健测量图像表面亮度深度的新方法。所有代码都在 mrf 中实现,这是一个开源的 MIT 许可的 Python 包。并介绍了一种鲁棒地测量图像表面亮度深度的新方法。所有代码都在 mrf 中实现,这是一个开源的 MIT 许可的 Python 包。并介绍了一种鲁棒地测量图像表面亮度深度的新方法。所有代码都在 mrf 中实现,这是一个开源的 MIT 许可的 Python 包。
更新日期:2020-06-11
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