Extreme-scale precision Imaging in Radio Astronomy (EIRA)
射电天文学中的超尺度精密成像 (EIRA)
基本信息
- 批准号:EP/T028270/1
- 负责人:
- 金额:$ 94.31万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Research Grant
- 财政年份:2020
- 资助国家:英国
- 起止时间:2020 至 无数据
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Aperture synthesis by interferometry in radio astronomy is a powerful technique allowing observation of the sky with antennae arrays at otherwise inaccessible angular resolutions and sensitivities. Image formation is however a complicated problem. Radio-interferometric measurements provide incomplete linear information about the sky, defining an ill-posed inverse imaging problem. Powerful computational imaging algorithms are needed to inject prior information into the data and recover the underlying image.The transformational science envisaged from radio astronomical observations for the next decades has triggered the development of new gigantic radio telescopes, such as the Square Kilometre Array (SKA), capable of imaging the sky at much higher resolution, with much higher sensitivity than current instruments, over wide fields of view. In this context, wide-band image cubes will exhibit rich structure and reach sizes between 1 Terabyte (TB) and 1 Petabyte (PB), while associated data volumes will reach the Exabyte (EB) scale. Endowing SKA and pathfinders with their expected acute vision requires image formation algorithms capable to transform the data and provide the target imaging precision (i.e. resolution and dynamic range), while simultaneously being robust (i.e. addressing calibration and uncertainty quantification challenges), and scalable to the extreme image sizes and data volumes at stake.The commonly used imaging algorithm in the field, dubbed CLEAN, owes its success to its simplicity and computational speed. CLEAN however crucially lacks the versatility to handle complex signal models, thereby limiting the achievable resolution and dynamic range of the formed images. The same holds for the existing associated calibration methods that need to correct for instrumental and ionospheric effects affecting the data. Another major limitation in radio-interferometric imaging is the absence of a proper methodology to quantify the uncertainty around the image estimate.A decade of research pioneered by Wiaux and his collaborators suggests that the theory of optimisation is a powerful and versatile framework to design new radio-interferometric imaging algorithms. In the optimisation framework, an objective function is defined as sum of a data-fidelity term and a regularisation term promoting a given prior signal model. Our research hypothesis is that algorithmic structures currently emerging at the interface of optimisation and deep learning can take the challenge of delivering the expected generation of algorithms for precision robust scalable radio-interferometric imaging, in a wide-band wide-field polarisation context.A novel approach will be developed in this context, based on the decomposition of the data into blocks and of the image cube into small, regular, overlapping 3D facets. Facet-specific regularisation terms and block-specific data-fidelity terms will all be handled in parallel through so-called proximal splitting optimisation methods, thereby unlocking simultaneously the image and data size bottlenecks. Injecting prior information into the inverse imaging problem at facet level also offers potential to better promote local spatio-spectral correlation, and eventually provide the target image precision. Sophisticated prior models based on advanced regularisation simultaneously promoting sparsity, correlation, positivity etc., will firstly be considered, to be substituted by learned priors using deep neural networks in a second stage with the aim to further improve precision and scalability. Facets and neural networks will percolate from the imaging module to calibration and uncertainty quantification for robustness. Our algorithms will be validated up to 10TB image size on High Performance Computing (HPC) machines. A technology transfer at 1GB image size will be performed in medical imaging, specifically 3D magnetic resonance and ultrasound imaging, as proof of their wider applicability.
在射电天文学中,通过干涉测量进行孔径合成是一种强大的技术,可以用天线阵列以其他方式无法达到的角分辨率和灵敏度观测天空。然而,图像形成是一个复杂的问题。无线电干涉测量提供了关于天空的不完整的线性信息,定义了不适定的逆成像问题。需要强大的计算成像算法将先验信息注入数据并恢复底层图像。未来几十年射电天文观测所设想的变革性科学已经引发了新的巨型射电望远镜的发展,例如平方公里阵列(SKA),能够以比现有仪器更高的分辨率和更高的灵敏度对天空进行成像,在广阔的视野中。在这种情况下,宽带图像立方体将呈现丰富的结构,并达到1 TB和1 PB之间的大小,而相关的数据量将达到EB规模。赋予SKA和探路者以他们期望的敏锐视觉需要能够转换数据并提供目标成像精度的图像形成算法(即分辨率和动态范围),同时具有鲁棒性(即解决校准和不确定性量化的挑战),并可扩展到极端的图像大小和数据量的风险。该领域常用的成像算法,称为CLEAN,它的成功归功于它的简单性和计算速度。然而,CLEAN缺乏处理复杂信号模型的通用性,从而限制了所形成图像的可实现分辨率和动态范围。这同样适用于需要校正影响数据的仪器效应和电离层效应的现有相关校准方法。无线电干涉成像的另一个主要限制是缺乏适当的方法来量化图像估计的不确定性。Wiaux及其合作者开创的十年研究表明,优化理论是设计新的无线电干涉成像算法的强大而通用的框架。在优化框架中,目标函数被定义为促进给定先验信号模型的数据保真度项和正则化项的总和。我们的研究假设是,目前出现在优化和深度学习界面的算法结构可以在宽带宽场偏振背景下,为精确鲁棒的可扩展无线电干涉成像提供预期一代算法的挑战。在这种背景下,将开发一种新的方法,基于将数据分解为块和将图像立方体分解为小的,规则的,重叠的3D面。特定于面的正则化项和特定于块的数据保真度项都将通过所谓的邻近分裂优化方法并行处理,从而同时解锁图像和数据大小瓶颈。将先验信息注入到小平面级的逆成像问题中,也提供了更好地促进局部空间-光谱相关性的潜力,并最终提供目标图像精度。基于高级正则化的复杂先验模型,同时促进稀疏性、相关性、正性等,将首先被考虑,在第二阶段使用深度神经网络被学习的先验知识取代,目的是进一步提高精度和可扩展性。方面和神经网络将从成像模块渗透到校准和不确定性量化,以实现鲁棒性。我们的算法将在高性能计算(HPC)机器上验证高达10 TB的图像大小。将在医学成像领域进行1GB图像大小的技术转让,特别是3D磁共振和超声成像,以证明其更广泛的适用性。
项目成果
期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Dual Forward-Backward Unfolded Network for Flexible Plug-and-Play
- DOI:10.23919/eusipco55093.2022.9909564
- 发表时间:2022-08
- 期刊:
- 影响因子:0
- 作者:A. Repetti;M. Terris;Y. Wiaux;J. Pesquet
- 通讯作者:A. Repetti;M. Terris;Y. Wiaux;J. Pesquet
Cygnus A jointly calibrated and imaged via non-convex optimization from VLA data
Cygnus A 通过 VLA 数据的非凸优化联合校准和成像
- DOI:10.1093/mnras/stab1903
- 发表时间:2021
- 期刊:
- 影响因子:4.8
- 作者:Dabbech A
- 通讯作者:Dabbech A
Bayesian Activity Estimation and Uncertainty Quantification of Spent Nuclear Fuel Using Passive Gamma Emission Tomography.
- DOI:10.3390/jimaging7100212
- 发表时间:2021-10-14
- 期刊:
- 影响因子:3.2
- 作者:Eldaly AK;Fang M;Di Fulvio A;McLaughlin S;Davies ME;Altmann Y;Wiaux Y
- 通讯作者:Wiaux Y
Deep Network Series for Large-Scale High-Dynamic Range Imaging
用于大规模高动态范围成像的深度网络系列
- DOI:10.1109/icassp49357.2023.10094843
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Aghabiglou A
- 通讯作者:Aghabiglou A
First AI for Deep Super-resolution Wide-field Imaging in Radio Astronomy: Unveiling Structure in ESO 137-006
- DOI:10.3847/2041-8213/ac98af
- 发表时间:2022-07
- 期刊:
- 影响因子:0
- 作者:A. Dabbech;M. Terris;A. Jackson;M. Ramatsoku;O. Smirnov;Y. Wiaux
- 通讯作者:A. Dabbech;M. Terris;A. Jackson;M. Ramatsoku;O. Smirnov;Y. Wiaux
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Yves Wiaux其他文献
CLEANing Cygnus A Deep and Fast with R2D2
使用 R2D2 深度快速地清洁 Cygnus A
- DOI:
10.3847/2041-8213/ad41df - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
A. Dabbech;Amir Aghabiglou;Chung San Chu;Yves Wiaux - 通讯作者:
Yves Wiaux
Yves Wiaux的其他文献
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{{ truncateString('Yves Wiaux', 18)}}的其他基金
Scalable Precision Imaging in Radio Astronomy: from Learned denoisers on GPU to Science (SPIRALS)
射电天文学中的可扩展精密成像:从 GPU 上的学习降噪器到科学 (SPIRALS)
- 批准号:
ST/W000970/1 - 财政年份:2022
- 资助金额:
$ 94.31万 - 项目类别:
Research Grant
Compressive Imaging in Radio Interferometry
射电干涉测量中的压缩成像
- 批准号:
EP/M008843/1 - 财政年份:2015
- 资助金额:
$ 94.31万 - 项目类别:
Research Grant
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