Machine learning initialization to accelerate Stokes profile inversions

Abstract

In this work, we discuss the application of convolutional neural networks (CNNs) as a tool to advantageously initialize Stokes profile inversions. To demonstrate the usefulness of CNNs, we concentrate in this paper on the inversion of LTE Stokes profiles. We use observations taken with the spectropolarimeter onboard the Hinode spacecraft as a test benchmark. First, we carefully analyze the data with the SIR inversion code using a given initial atmospheric model. The code provides a set of atmospheric models that reproduce the observations. These models are then used to train a CNN. Afterwards, the same data are again inverted with SIR but using the trained CNN to provide the initial guess atmospheric models for SIR. The CNNs allow us to significantly reduce the number of inversion cycles when used to compute initial guess model atmospheres, decreasing the computational time for LTE inversions by a factor of two to four. CNN's alone are much faster than assisted inversions, but the latter are more robust and accurate. The advantages and limitations of machine learning techniques for estimating optimum initial atmospheric models for spectral line inversions are discussed. Finally, we describe a python wrapper for the SIR and DeSIRe codes that allows for the easy setup of parallel inversions. The assisted inversions can speed up the inversion process, but the efficiency and accuracy of the inversion results depend strongly on the solar scene and the data used for the CNN training. This method (assisted inversions) will not obviate the need for analyzing individual events with the utmost care but will provide solar scientists with a much better opportunity to sample large amounts of inverted data, which will undoubtedly broaden the physical discovery space.

arXiv comment14 pages, 10 figures, Accepted for publication on Astronomy and Astrophysics

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• Diagnostic capabilities of spectropolarimetric observations for understanding solar phenomena I. Zeeman-sensitive photospheric lines
• Multiple Stokes I inversions to infer magnetic fields in the spectral range around Cr I 5782 Å
• Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D): I. Overview, Magnetohydrodynamic Modeling, and Stokes Profile Synthesis
• SunnyNet: A neural network approach to 3D non-LTE radiative transfer
• Constraining the magnetic vector in the quiet solar photosphere and the impact of instrumental degradation
• Inferring Line-of-Sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks
• Mapping the Sun's upper photosphere with artificial neural networks
• Convolutional Neural Networks and Stokes Response Functions
• A study of the capabilities for inferring atmospheric information from high-spatial-resolution simulations
• Understanding the thermal and magnetic properties of a X-class flare in the low solar atmosphere
• Neural translation for Stokes inversion and synthesis
• Exploring magnetic loops and serpentine fields in the quiet Sun with the GRIS-IFU
• Non-Local Thermodynamic Equilibrium inversions of the Si I 10827 A spectral line
• Compression Method for Solar Polarization Spectra Collected from Hinode SOT/SP Observations