Speaker
Description
Image reconstruction in radio interferometry is a classic ill-posed inverse problem: recovering a continuous sky brightness distribution from sparse Fourier (uv-plane) samples. While the standard CLEAN algorithm is robust for point sources, it often introduces artifacts when imaging extended, diffuse structures. Regularized Maximum Likelihood (RML) methods offer an alternative but face significant computational overhead and tuning challenges as target resolutions increase.
In this talk, I present a framework that overcomes these limitations by modeling the sky brightness as a continuous neural network. Unlike traditional "black box" deep learning, our approach is self-supervised, optimizing the network to fit the visibility data of a single observation directly. By mapping 2D sky coordinates to intensity values, the network functions as a resolution-independent representation rather than a fixed pixel grid. This architecture captures large-scale structures and fine details simultaneously, surpassing CLEAN with double the resolution and four times the fidelity. I will demonstrate the method’s performance on both synthetic tests and real ALMA datasets, proposing a new paradigm for high-fidelity interferometric imaging.