Performance of the Segment Anything Model in Various RFI/Events Detection in Radio Astronomy

TL;DR

This paper evaluates the Segment Anything Model (SAM) and its optimized version HQ-SAM for detecting radio frequency interference and solar radio bursts, demonstrating strong generalization and competitive performance in radio astronomy data analysis.

Contribution

It introduces the application of SAM and HQ-SAM to radio astronomy, showing their effectiveness in RFI and SRB detection tasks, which is a novel use case for these models.

Findings

HQ-SAM outperforms traditional methods in RFI detection.

SAM models effectively identify solar radio bursts.

Models show strong generalization across diverse radio astronomy data.

Abstract

The emerging era of big data in radio astronomy demands more efficient and higher-quality processing of observational data. While deep learning methods have been applied to tasks such as automatic radio frequency interference (RFI) detection, these methods often face limitations, including dependence on training data and poor generalization, which are also common issues in other deep learning applications within astronomy. In this study, we investigate the use of the open-source image recognition and segmentation model, Segment Anything Model (SAM), and its optimized version, HQ-SAM, due to their impressive generalization capabilities. We evaluate these models across various tasks, including RFI detection and solar radio burst (SRB) identification. For RFI detection, HQ-SAM (SAM) shows performance that is comparable to or even superior to the SumThreshold method, especially with large-area broadband RFI data. In the search for SRBs, HQ-SAM demonstrates strong recognition abilities for Type II and Type III bursts. Overall, with its impressive generalization capability, SAM (HQ-SAM) can be a promising candidate for further optimization and application in RFI and event detection tasks in radio astronomy.