An automated, self-calibration based pipeline for high-fidelity solar imaging with LOFAR: SIMPL
Abstract
The LOw Frequency ARray (LOFAR) is capable of imaging spectroscopy of the Sun in the 10-240 MHz frequency range, with high spectral, temporal, and spatial resolution. However, the complex and rapidly varying nature of solar radio emission - spanning several orders of magnitude in brightness further exacerbated by the strong ionospheric phase distortions during daytime observations, poses major challenges for calibration, imaging, and automation. We aim to develop a fully automated, high-fidelity imaging pipeline optimized for LOFAR solar observations, capable of handling the intrinsic variability of solar emission and producing science-ready images with minimal human intervention. We have built the Solar Imaging Pipeline for LOFAR (SIMPL), which integrates excision of radio-frequency interference (RFI) for the solar-specific scenarios, calibration strategies, and self-calibration. The pipeline is designed to enable scalable and uniform processing of large archival datasets. SIMPL achieves more than an order-of-magnitude improvement in imaging dynamic range compared to previous efforts and reliably produces high-quality spectroscopic snapshot images. It has been tested across a wide range of solar conditions. It is currently being employed to process a decade of LOFAR solar observations, providing science-ready FITS images for the community and enabling both comprehensive and novel studies of solar radio phenomena -- ranging from quiet Sun emission and faint non-thermal features to active regions and their associated dynamic events, such as transient bursts.
Cite
@article{arxiv.2508.10619,
title = {An automated, self-calibration based pipeline for high-fidelity solar imaging with LOFAR: SIMPL},
author = {Soham Dey and Divya Oberoi and Pietro Zucca and Mattia Mancini and Deepan Patra and Devojyoti Kansabanik},
journal= {arXiv preprint arXiv:2508.10619},
year = {2025}
}
Comments
12 pages, 9 figures, submitted to A&A