天文源寻找中小波去噪方法的研究
Examining alternatives to wavelet de-noising for astronomical source finding
摘要：
平方公里阵列及其探路者ASKAP和MeerKAT将产生大量数据，需要自动源查找。自动源查找器的性能可以通过预处理数据集来提高。在WALLABY和DINGO巡天的准备工作中，我们使用了一个由实际Westerbork望远镜噪声和WHISP HI星系构成的HI数据立方体，测试了线性平滑、{ \sc Duchamp}寻源器的小波去噪、迭代中值平滑和数学形态减法对谱线数据集强度阈值寻源的实际改进。为了比较这些预处理方法，我们为每种方法和一系列输入参数生成了完整性-可靠性性能曲线。我们发现，迭代中值平滑产生最好的ASKAP HI谱线观测源发现的结果，但小波去噪是一个更安全的预处理技术。在本文中，我们还提出了我们的实现迭代中值平滑和数学形态减法。
英文摘要：
The Square Kilometre Array and its pathfinders ASKAP and MeerKAT will produce prodigious amounts of data that necessitate automated source finding. The performance of automated source finders can be improved by pre-processing a dataset. In preparation for the WALLABY and DINGO surveys, we have used a test HI datacube constructed from actual Westerbork Telescope noise and WHISP HI galaxies to test the real world improvement of linear smoothing, the {\sc Duchamp} source finder's wavelet de-noising, iterative median smoothing and mathematical morphology subtraction, on intensity threshold source finding of spectral line datasets. To compare these pre-processing methods we have generated completeness-reliability performance curves for each method and a range of input parameters. We find that iterative median smoothing produces the best source finding results for ASKAP HI spectral line observations, but wavelet de-noising is a safer pre-processing technique. In this paper we also present our implementations of iterative median smoothing and mathematical morphology subtraction.
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