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arXiv 2204.09288astro-ph.IM
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面向下一代射电巡天的轻量级HI源搜寻

Lightweight HI source finding for next generation radio surveys

Emma Tolley, Damien Korber, Aymeric Galan, Austin Peel, Mark T. Sargent, Jean-Paul Kneib, Frederic Courbin, Jean-Luc Starck

AI总结:

提出轻量级模块化框架LiSA,结合小波去噪、零假设检验与卷积神经网络,在SDC2数据集上实现HI源的自动化检测与属性预测,能找到95%信噪比大于3的源。

摘要:

未来的深度HI巡天对于理解星系的本质和宇宙的内容至关重要。然而,这些数据的海量规模将需要分布式和自动化的处理技术。我们引入了LiSA,这是一组用于在3D光谱数据中对HI源进行去噪、检测和特征描述的python模块。LiSA在Square Kilometer Array Science Data Challenge 2数据集上开发和测试,包含用于轻松实现域分解和并行执行的模块与流水线。LiSA包含使用starlet变换进行2D-1D小波去噪的算法,以及使用零假设检验进行灵活源搜寻的算法。这些算法轻量且可移植,仅需少数反映数据分辨率的用户定义参数。LiSA还包含两个为分析数据立方体而开发的卷积神经网络,用于将HI源与伪影分离并预测HI源属性。所有这些组件都设计得尽可能模块化,允许用户混合搭配不同组件以创建其理想流水线。我们在SDC2数据集上展示了LiSA各组件的性能,它能够找到95% SNR > 3的HI源并准确预测其属性。

英文摘要:

Future deep HI surveys will be essential for understanding the nature of galaxies and the content of the Universe. However, the large volume of these data will require distributed and automated processing techniques. We introduce LiSA, a set of python modules for the denoising, detection and characterization of HI sources in 3D spectral data. LiSA was developed and tested on the Square Kilometer Array Science Data Challenge 2 dataset, and contains modules and pipelines for easy domain decomposition and parallel execution. LiSA contains algorithms for 2D-1D wavelet denoising using the starlet transform and flexible source finding using null-hypothesis testing. These algorithms are lightweight and portable, needing only a few user-defined parameters reflecting the resolution of the data. LiSA also includes two convolutional neural networks developed to analyse data cubes which separate HI sources from artifacts and predict the HI source properties. All of these components are designed to be as modular as possible, allowing users to mix and match different components to create their ideal pipeline. We demonstrate the performance of the different components of LiSA on the SDC2 dataset, which is able to find 95% of HI sources with SNR > 3 and accurately predict their properties.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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