Identification

Title

PINN ME: A physics-informed neural network framework for accurate Milneâ€“Eddington inversions of solar magnetic fields

Abstract

Spectropolarimetric inversions of solar observations are fundamental for the estimation of the magnetic field in the solar atmosphere. However, instrumental noise, computational requirements, and varying levels of physical realism make it challenging to derive reliable solar magnetic field estimates. In this study, we present a novel approach for spectropolarimetric inversions based on physics-informed neural networks to infer the photospheric magnetic field under the Milne–Eddington approximation (PINN ME). Our model acts as a representation of the parameter space, mapping input coordinates ( t , x , y ) to the respective spectropolarimetric parameters, which are used to synthesize the corresponding Stokes profiles. By iteratively sampling coordinate points, synthesizing profiles, and minimizing the deviation from the observed stokes profiles, our method can find the set of Milne–Eddington parameters that best fit the observations. In addition, we directly include the point-spread function to account for instrumental effects. We use a predefined parameter space as well as synthetic profiles from a radiative MHD simulation to evaluate the performance of our method and to estimate the impact of instrumental noise. Our results demonstrate that PINN ME achieves an intrinsic spatiotemporal coupling, which can largely mitigate observational noise and provides a memory-efficient inversion even for extended fields of view. Finally, we apply our method to observations and show that our method provides a high spatial coherence and can resolve small-scale features in both strong- and weak-field regions.

Resource type

document

Resource locator

Unique resource identifier

code

https://n2t.net/ark:/85065/d7db869v

codeSpace

Dataset language

eng

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

geoscientificInformation

Keywords

Keyword set

keyword value

Text

originating controlled vocabulary

title

Resource Type

reference date

date type

publication

effective date

2016-01-01T00:00:00Z

Geographic location

West bounding longitude

East bounding longitude

North bounding latitude

South bounding latitude

Temporal reference

Temporal extent

Begin position

End position

Dataset reference date

date type

publication

effective date

2025-05-20T00:00:00Z

Frequency of update

Quality and validity

Lineage

Conformity

Data format

name of format

version of format

Constraints related to access and use

Constraint set

Use constraints

<span style="font-family:Arial;font-size:10pt;font-style:normal;" data-sheets-root="1">Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</span>

Limitations on public access

None

Responsible organisations

Responsible party

contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact