MVPinn: Integrating Milne-Eddington Inversion with Physics-Informed Neural Networks for GST/NIRIS Observations

TL;DR

MVPinn is a physics-informed neural network that efficiently and accurately performs Milne-Eddington inversions on solar spectropolarimetric data, outperforming traditional methods in speed and noise resilience.

Contribution

The paper introduces MVPinn, a novel PINN approach that embeds ME radiative transfer equations into neural networks for improved solar magnetic field inversion.

Findings

MVPinn achieves inversion in about 15 seconds, much faster than traditional methods.

It shows high correlation (~90%) with established magnetic field measurements.

MVPinn better handles asymmetric and weak polarization signals.

Abstract

We introduce MVPinn, a Physics-Informed Neural Network (PINN) approach tailored for solving the Milne-Eddington (ME) inversion problem, specifically applied to spectropolarimetric observations from the Big Bear Solar Observatory's Near-InfraRed Imaging Spectropolarimeter (BBSO/NIRIS) at the Fe I 1.56 {\mu}m lines. Traditional ME inversion methods, though widely used, are computationally intensive, sensitive to noise, and often struggle to accurately capture complex profile asymmetries resulting from gradients in magnetic field strength, orientation, and line-of-sight velocities. By embedding the ME radiative transfer equations directly into the neural network training as physics-informed constraints, our MVPinn method robustly and efficiently retrieves magnetic field parameters, significantly outperforming traditional inversion methods in accuracy, noise resilience, and the ability to handle asymmetric and weak polarization signals. After training, MVPinn infers one magnetogram in about 15 seconds, compared to tens of minutes required by traditional ME inversion on high-resolution spectropolarimetric data. Quantitative comparisons demonstrate excellent agreement with well-established magnetic field measurements from the SDO/HMI and Hinode/SOT-SP instruments, with correlation coefficients of approximately 90%. In particular, MVPINN aligns better with Hinode/SOT-SP data, indicating some saturation of HMI data at high magnetic strengths. We further analyze the physical significance of profile asymmetries and the limitations inherent in the ME model assumption. Our results illustrate the potential of physics-informed machine learning methods in high-spatial-temporal solar observations, preparing for more sophisticated, real-time magnetic field analysis essential for current and next-generation solar telescopes and space weather monitoring.