Jon Todd Hoeksema

The interplanetary magnetic field (IMF) measured near Earth can be up to 2 times greater than that derived from models using remote solar observations. We investigate this discrepancy by modeling the IMF using a potential field source surface (PFSS) model using synoptic maps of the photospheric magnetic field from 2010 May to 2024 April. Five types of radial field synoptic maps are used in this work: the B _r synoptic maps from vector magnetic field data, the M _r synoptic maps from the line-of-sight field data, the rescaled M _r synoptic maps rescaled from the M _r maps by a center-to-limb distance dependent rescaling factor of B _r / M _r , and composite and rescaled composite synoptic maps comprised of a combination of strong-field pixels from the B _r maps and the rest from either the original M _r or rescaled M _r maps. The modeled IMFs from all five types of synoptic maps agree with each other well in the solar maximum phase, when they are about 2 times smaller than in situ measurements. The IMF calculated from the B _r and both composite and rescaled composite synoptic maps match well with in situ observations during solar minimum from 2017 to 2022. The IMF values modeled from both the M _r and rescaled M _r synoptic maps are still significantly smaller in this time interval. This suggests that (1) the B _r maps represent the radial field better than the M _r ; and (2) the PFSS model is appropriate to model the heliospheric magnetic field in solar minimum, but has limitations when used near solar maximum.

We report a point-spread function (PSF) and deconvolution procedure to remove stray light from the Helioseismic and Magnetic Imager (HMI) data. Prelaunch calibration observations, postlaunch Venus transit, and lunar transit data were used to develop the PSF and evaluate how well it reproduced the observed scattering. The PSF reported differs from previous stray light removal efforts since we do not use Gaussians as the central mathematical component. Instead, we use a Lorenztian convolved with an Airy function. In 2018, the HMI team began providing full-disk, stray-light-corrected data daily. Intensity, Doppler, magnetogram, and vector magnetic field data are provided. The deconvolution uses a Richardson–Lucy algorithm and takes less than 1 s per full-disk image. The results, on average, show decreases in umbral continuum intensity, a doubling of the granulation intensity contrast, increases in the total field strength, most notably in plage by ∼1.4–2.5 times the original value, and a partial correction for the convective blueshift. Local helioseismology analyses using corrected data yield results that are consistent with those from uncorrected data, with only negligible differences, except in sunspot regions. The new data are found in JSOC with names similar to the original but with the qualifying term “_ dcon ” or “_ dconS ” appended, denoting whether the deconvolution was applied to the filtergrams or Stokes images. The HMI team recommends using the corrected data for improved visual clarity, more accurate irradiance reconstruction, better coalignment with high-resolution data, reduced errors in tracking algorithms, and improved magnetic field strengths.

High-resolution helioseismology observations with the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO) provide a unique three-dimensional view of the solar interior structure and dynamics, revealing a tremendous complexity of the physical processes inside the Sun. We present an overview of the results of the HMI helioseismology program and discuss their implications for modern theoretical models and simulations of the solar interior.

Data from the Michelson Doppler Imager (MDI) and Helioseismic and Magnetic Imager (HMI) are analyzed from 1996 to 2023 to investigate tilt angles (γ) of bipolar magnetic regions and Joy’s law for Solar Cycles 23 and 24 and a portion of Cycle 25. The HMI radial magnetic field (B _r ) and MDI magnetogram (B _los) data are used to calculate (γ) using the flux-weighted centroids of the positive and negative polarities. Each active region (AR) is only sampled once. The analysis includes only Beta (β)-class ARs since computing γ of complex ARs is less meaningful. During the emergence of the ARs, we find that the average tilt angle ( γ ¯ ) increases from 3.°30 ± 0.75 when 20% of the flux has emerged to 6.°79° ± 0.66 when the ARs are at their maximum flux. Cycle 24 has a larger average tilt, γ ¯ 24 =6.67 ± 0.66, than Cycle 23, γ ¯ 23 = 5.11 ± 0.61. No significant difference is found in the slope of Joy’s law or γ ¯ when sampling the ARs at the time of maximum flux or central meridian crossing. There are persistent differences in γ ¯ in the hemispheres, with the Sun's southern hemisphere having higher γ ¯ in Cycles 23 and 24, but the uncertainties are such that these differences are not statistically significant.

Knowledge of the global magnetic field distribution and its evolution on the Sun’s surface is crucial for modeling the coronal magnetic field, understanding the solar wind dynamics, computing the heliospheric open flux distribution, and predicting the solar cycle strength. As the far side of the Sun cannot be observed directly and high-latitude observations always suffer from projection effects, we often rely on surface flux transport (SFT) simulations to model the long-term global magnetic field distribution. Meridional circulation, the large-scale north–south component of the surface flow profile, is one of the key components of the SFT simulation that requires further constraints near high latitudes. Prediction of the photospheric magnetic field distribution requires knowledge of the flow profile in the future, which demands reconstruction of that same flow at the current time so that it can be estimated at a later time. By performing Observing System Simulation Experiments, we demonstrate how the ensemble Kalman filter technique, when used with an SFT model, can be utilized to make “posterior” estimates of flow profiles into the future that can be used to drive the model forward to forecast the photospheric magnetic field distribution.

Using Helioseismic and Magnetic Imager/Solar Dynamics Observatory data, we search for a relationship between kinetic helicity and magnetic helicity in solar active regions (ARs) using a sample of 62 ARs from 2010 May to 2015 May. The sample includes 32 mature ARs and 30 emerging ARs. We calculate kinetic helicity in the interior in the depth range from 0.6 to 11.6 Mm, magnetic helicity in the corona, helicity flux across the photosphere, and the magnetic twist and magnetic writhe of the ARs at the photosphere. From these data, relationships are found between magnetic helicity, helicity flux, and magnetic twist. However, magnetic writhe appears not to be related to the other magnetic quantities. No relationship is found between the kinetic helicity and any magnetic quantity. In particular, no relationship is found between the kinetic helicity and any of the following: magnetic helicity, magnetic helicity flux, magnetic twist, or magnetic writhe. These results suggest that (1) the magnetic helicity in the corona above ARs is mainly derived from the magnetic twist, and (2) the flow dynamics in the region from 0.6 to 11.6 Mm below the photosphere is not the primary source for the generation of magnetic helicity in ARs.

Vector magnetograms of the Sun’s photosphere are cornerstones for much of solar physics research. These data are often produced by data-analysis pipelines combining per-pixel Stokes polarization vector inversion with a disambiguation that resolves an intrinsic 180° ambiguity. We introduce a learning-based method, SuperSynthIA, that produces full-disk vector magnetograms from Stokes vector observations. As input, SuperSynthIA uses Stokes polarization images from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI). As output, SuperSynthIA simultaneously emulates the inversion and disambiguation outputs from the Hinode/Solar Optical Telescope-Spectro-Polarimeter (SOT-SP) pipeline. Our method extends our previous approach SynthIA with heliographic outputs as well as using an improved data set and inference method. SuperSynthIA provides a new tool for improved magnetic fields from full-disk SDO/HMI observations using information derived from the enhanced capabilities of Hinode/SOT-SP. Compared to our previous SynthIA, SuperSynthIA provides physics-ready vector magnetograms and mitigates unphysical angle preferences and banding artifacts in SynthIA. SuperSynthIA data are substantially more temporally consistent than those from the SDO/HMI pipeline, most notably seen in, e.g., evolving active regions. SuperSynthIA substantially reduces noise in low-signal areas, resulting in less center-to-limb bias outside of strong-signal areas. We show that outputs from SuperSynthIA track the SDO/HMI-recorded evolution of the magnetic field. We discuss the limitations of SuperSynthIA that the user must understand, and we demonstrate a broad set of evaluations to test SuperSynthIA and discuss remaining known artifacts. Our tests provide both methodology and evidence that SuperSynthIA outputs are ready for use by the community, and that learning-based approaches are suitable for physics-ready magnetograms.

We have developed a comprehensive catalog of the variable differential rotation measured near the solar photosphere. This catalog includes measurements of these flows obtained using several techniques: direct Doppler, granule tracking, magnetic pattern tracking, global helioseismology, as well as both time-distance and ring-diagram methods of local helioseismology. We highlight historical differential rotation measurements to provide context, and thereafter provide a detailed comparison of the MDI-HMI-GONG-Mt. Wilson overlap period (April 2010 – Jan 2011) and investigate the differences between velocities obtained from different techniques and attempt to explain discrepancies. A comparison of the rotation rate obtained by magnetic pattern tracking with the rotation rates obtained using local and global helioseismic techniques shows that magnetic pattern tracking measurements correspond to helioseismic flows located at a depth of 25 to 28 Mm. In addition, we show the torsional oscillation from Sunspot Cycles 23 and 24 and discuss properties that are consistent across measurement techniques. We find that acceleration derived from torsional oscillation is a better indicator of long-term trends in torsional oscillation compared to the residual velocity magnitude. Finally, this analysis will pave the way toward understanding systematic effects associated with various flow measurement techniques and enable more accurate determination of the global patterns of flows and their regular and irregular variations.

Whitepaper #097 in the Decadal Survey for Solar and Space Physics (Heliophysics) 2024-2033. Main topics: space weather applications; basic research. Additional topics: space weather research/operations/research loop; ground-based missions/projects; solar physics; […]