POLISH'ing the Sky: Wide-Field and High-Dynamic Range Interferometric Image Reconstruction with Application to Strong Lens Discovery

2026-03-09

AI Summary

This work addresses the challenges faced by existing deep learning methods in radio interferometric imaging—particularly their limited performance under high dynamic range, wide fields of view, and mismatches between training and test conditions—which hinder the discovery of strong gravitational lenses. Building upon the POLISH framework, the authors introduce a novel block-wise training strategy coupled with image stitching to enable wide-field reconstruction, and employ a nonlinear arcsinh intensity transformation to effectively handle high dynamic range. Integrated with the T-RECS simulation platform and precise point spread function modeling, the proposed method significantly enhances reconstruction robustness and super-resolution capabilities. It successfully recovers strong lens systems with Einstein radii approaching the scale of the PSF under realistic observational conditions, potentially enabling a tenfold increase in the number of galaxy–galaxy lenses detectable in DSA surveys.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Learning to Search
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Radio interferometry enables high-resolution imaging of astronomical radio sources by synthesizing a large effective aperture from an array of antennas and solving a deconvolution problem to reconstruct the image. Deep learning has emerged as a promising solution to the imaging problem, reducing computational costs and enabling super-resolution. However, existing DL-based methods often fall short of the requirements for real-world deployment due to limitations in handling high dynamic range, large field of view, and mismatches between training and test conditions. In this work, we build upon and extend the POLISH framework, a recent DL model for radio interferometric imaging. We introduce key improvements to enable robust reconstruction and super-resolution under real-world conditions: (1) a patch-wise training and stitching strategy for scaling to wide-field imaging and (2) a nonlinear arcsinh-based intensity transformation to manage high dynamic range. We conduct comprehensive evaluations using the T-RECS simulation suite with realistic sky models and point spead functions (PSF), and demonstrate that our approach significantly improves reconstruction quality and robustness. We test the model on realistic simulated strong gravitational lenses and show that lens systems with Einstein radii near the PSF scale can be recovered after deconvolution with our POLISH model, potentially yielding 10$\times$ more galaxy-galaxy lensing systems from the Deep Synoptic Array (DSA) survey than with image-plane CLEAN. Our results highlight the potential of DL models as practical, scalable tools for next-generation radio astronomy.

Problem

• radio interferometry
• high dynamic range
• wide-field imaging
• deep learning
• image reconstruction

Innovation

• wide-field imaging
• high dynamic range
• deep learning
• radio interferometry
• super-resolution