Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging-猫眼课题宝

DOI：

10.48550/arxiv.2312.00125

发表时间：

2023-11

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

T. Liaudat;Matthijs Mars;Matthew Alexander Price;Marcelo Pereyra;M. Betcke;Jason D. McEwen

像平方公里阵列这样的下一代无线电干涉仪由于其前所未有的角分辨率和灵敏度而有可能解开科学发现。释放其潜力的一个关键在于处理大量和复杂的传入数据。这一挑战需要建立无线电干涉成像方法，可以科普大量的数据大小，并提供高质量的图像重建与不确定性量化（UQ）。这项工作提出了一种方法创造QuantifAI解决UQ在无线电干涉成像与数据驱动（学习）先验的高维设置。我们的模型，植根于贝叶斯框架，使用物理激励模型的可能性。该模型利用数据驱动的凸先验，可以对从模拟中隐式学习的复杂信息进行编码，并保证后验的对数收敛性。我们利用高维对数凹后验的概率集中现象，让我们获得有关后验的信息，避免MCMC抽样技术。我们依靠凸优化方法来计算MAP估计，这是已知的速度更快，更好的规模与维比MCMC采样策略。我们的方法允许我们计算局部可信区间，即，贝叶斯误差条，并对重建图像进行结构假设检验。此外，我们还提出了一种新的快速方法来计算不同尺度下的像素不确定性。我们通过在模拟环境中重建无线电干涉图像并进行快速和可扩展的UQ来演示我们的方法，我们使用MCMC采样进行验证。我们的方法显示了改进的图像质量和更有意义的不确定性比基准方法的基础上稀疏促进先验。QuantifAI的源代码：https://github.com/astro-informatics/QuantifAI。

Next-generation radio interferometers like the Square Kilometer Array have the potential to unlock scientific discoveries thanks to their unprecedented angular resolution and sensitivity. One key to unlocking their potential resides in handling the deluge and complexity of incoming data. This challenge requires building radio interferometric imaging methods that can cope with the massive data sizes and provide high-quality image reconstructions with uncertainty quantification (UQ). This work proposes a method coined QuantifAI to address UQ in radio-interferometric imaging with data-driven (learned) priors for high-dimensional settings. Our model, rooted in the Bayesian framework, uses a physically motivated model for the likelihood. The model exploits a data-driven convex prior, which can encode complex information learned implicitly from simulations and guarantee the log-concavity of the posterior. We leverage probability concentration phenomena of high-dimensional log-concave posteriors that let us obtain information about the posterior, avoiding MCMC sampling techniques. We rely on convex optimisation methods to compute the MAP estimation, which is known to be faster and better scale with dimension than MCMC sampling strategies. Our method allows us to compute local credible intervals, i.e., Bayesian error bars, and perform hypothesis testing of structure on the reconstructed image. In addition, we propose a novel blazing-fast method to compute pixel-wise uncertainties at different scales. We demonstrate our method by reconstructing radio-interferometric images in a simulated setting and carrying out fast and scalable UQ, which we validate with MCMC sampling. Our method shows an improved image quality and more meaningful uncertainties than the benchmark method based on a sparsity-promoting prior. QuantifAI's source code: https://github.com/astro-informatics/QuantifAI.

Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging

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