Abstract
Abstract We take a broad look at the problem of identifying the magnetic solar causes of space weather. With the lackluster performance of extrapolations based upon magnetic field measurements in the photosphere, we identify a region in the near-UV (NUV) part of the spectrum as optimal for studying the development of magnetic free energy over active regions. Using data from SORCE, the Hubble Space Telescope, and SKYLAB, along with 1D computations of the NUV spectrum and numerical experiments based on the MURaM radiation–magnetohydrodynamic and HanleRT radiative transfer codes, we address multiple challenges. These challenges are best met through a combination of NUV lines of bright Mg ii, and lines of Fe ii and Fe i (mostly within the 4s–4p transition array) which form in the chromosphere up to 2 × 104 K. Both Hanle and Zeeman effects can in principle be used to derive vector magnetic fields. However, for any given spectral line the τ = 1 surfaces are generally geometrically corrugated owing to fine structure such as fibrils and spicules. By using multiple spectral lines spanning different optical depths, magnetic fields across nearly horizontal surfaces can be inferred in regions of low plasma β, from which free energies, magnetic topology, and other quantities can be derived. Based upon the recently reported successful sub-orbital space measurements of magnetic fields with the CLASP2 instrument, we argue that a modest space-borne telescope will be able to make significant advances in the attempts to predict solar eruptions. Difficulties associated with blended lines are shown to be minor in an Appendix.
Highlights
Commenting on the origin of solar flares, in 1960 Gold & Hoyle wrote time must be able to lead to instability in which this energy can rapidly be dissipated into heat and mass motion. . .The requirements of the theory can be stated quite definitely
With the lackluster performance of extrapolations based upon magnetic field measurements in the photosphere, we identify a region in the near UV part of the spectrum as optimal for studying the development of magnetic free energy over active regions
With the availability of a decade of high-resolution photospheric magnetic field data from NASA’s Solar Dynamics Observatory (SDO) mission, recent efforts have focused on statistical pattern recognition methods for flare prediction that generally fall under the “machine learning (ML)” sub-field of artificial intelligence research
Summary
Commenting on the origin of solar flares, in 1960 Gold & Hoyle wrote time must be able to lead to instability in which this energy can rapidly be dissipated into heat and mass motion. Measurements of the photospheric magnetic field can be related only indirectly and through certain ad-hoc parameterizations, to the magnetic free energy of the overlying corona, which is the ultimate origin of instabilities leading to flares and CMEs. With the availability of a decade of high-resolution photospheric magnetic field data from NASA’s Solar Dynamics Observatory (SDO) mission, recent efforts have focused on statistical pattern recognition methods for flare prediction that generally fall under the “machine learning (ML)” sub-field of artificial intelligence research. Deshmukh et al (2020) showed that combining Topological Data Analysis (TDA) of the radial magnetic field structure in active regions with the SHARPs vector magnetic field metadata results in similar or slightly higher skill scores compared to using selected SHARPs metadata alone This result implies that inclusion of high-resolution imaging of active region and flows may be an important complement to spectroscopic measurements when deducing atmospheric conditions that evolve to a flaring state. We argue that spectro-polarimetry of the near-UV solar spectrum is a profitable avenue for research
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