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1. High-resolution imaging of solar pores

   https://doi.org/10.1051/0004-6361/202245410  

   Kamlah, R. ; Verma, M. ; Denker, C. ; Wang, H. ( July 2023 , Astronomy & Astrophysics)

   Context.Light bridges are bright, long, and narrow features that are typically connected to the formation or decay processes of sunspots and pores.

   Aims.The interaction of magnetic fields and plasma flows is investigated in the trailing part of an active region, where pores and magnetic knots evolve into a complex sunspot. The goal is to identify the photospheric and chromospheric processes, which transform the mainly vertical magnetic fields of pores into a sunspot with multiple umbral cores, light bridges, and rudimentary penumbrae.

   Methods.Conducting observations with a broad variety of telescopes and instruments provides access to different atmospheric layers and the changing morphology of features connected to strong magnetic fields. While the Helioseismic and Magnetic Imager (HMI) of the Solar Dynamics Observatory (SDO) provides full-disk continuum images and line-of-sight magnetograms, the fine structure and flows around a pore can be deduced from high-resolution observations in various wavelengths as provided by theGoodeSolar Telescope (GST) at the Big Bear Solar Observatory (BBSO). Horizontal proper motions are evaluated applying local correlation tracking (LCT) to the available time series, whereas the connectivity of sunspot features can be established using the background-subtracted activity maps (BaSAMs).

   Results.Photospheric flow maps indicate radial outflows, where the light bridge connects to the surrounding granulation, whereas inflows are present at the border of the pores. In contrast, the chromospheric flow maps show strong radial outflows at superpenumbral scales, even in the absence of a penumbra in the photosphere. The region in between the two polarities is characterized by expanding granules creating strong divergence centers. Variations in BaSAMs follow locations of significant and persistent changes in and around pores. The resulting maps indicate low variations along the light bridge, as well as thin hairlines connecting the light bridge to the pores and strong variations at the border of pores. Various BaSAMs demonstrate the interaction of pores with the surrounding supergranular cell. The Hαline-of-sight velocity maps provide further insights into the flow structure, with twisted motions along some of the radial filaments around the pore with the light bridge. Furthermore, flows along filaments connecting the two polarities of the active region are pronounced in the line-of-sight velocity maps.

   Conclusions.The present observations reveal that even small-scale changes of plasma motions in and around pores are conducive to transform pores into sunspots. In addition, chromospheric counterparts of penumbral filaments appear much earlier than the penumbral filaments in the photosphere. Penumbra formation is aided by a stable magnetic feature that anchors the advection of magnetic flux and provides a connection to the surrounding supergranular cell, whereas continuously emerging flux and strong light bridges are counteragents that affect the appearance and complexity of sunspots and their penumbrae.

    

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2. Analysis of quiet-sun turbulence on the basis of SDO/HMI and goode solar telescope data

   https://doi.org/10.1093/mnras/staa2427  

   Abramenko, Valentina I ; Yurchyshyn, Vasyl B ( October 2020 , Monthly Notices of the Royal Astronomical Society)

   null (Ed.)

   ABSTRACT We analysed line-of-sight magnetic fields and magnetic power spectra of an undisturbed photosphere using magnetograms acquired by the Helioseismic and Magnetic Imager (HMI) on-board the Solar Dynamic Observatory and the Near InfraRed Imaging Spectrapolarimeter (NIRIS) operating at the Goode Solar Telescope of the Big Bear Solar Observatory. In the NIRIS data, we revealed thin flux tubes of 200–400 km in diameter and of 1000–2000 G field strength. The HMI power spectra determined for a coronal hole, a quiet sun, and a plage areas exhibit the same spectral index of −1 on a broad range of spatial scales from 10–20 Mm down to 2.4 Mm. This implies that the same mechanism(s) of magnetic field generation operate everywhere in the undisturbed photosphere. The most plausible one is the local turbulent dynamo. When compared to the HMI spectra, the −1.2 slope of the NIRIS spectrum appears to be more extended into the short spatial range until the cut-off at 0.8–0.9 Mm, after which it continues with a steeper slope of −2.2. Comparison of the observed and Kolmogorov-type spectra allowed us to infer that the Kolmogorov turbulent cascade cannot account for more than 35 per cent of the total magnetic energy observed in the scale range of 3.5–0.3 Mm. The energy excess can be attributed to other mechanisms of field generation such as the local turbulent dynamo and magnetic superdiffusivity observed in an undisturbed photosphere that can slow down the rate of the Kolmogorov cascade leading to a shallower resulting spectrum. 

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3. SMARPs and SHARPs: Two Solar Cycles of Active Region Data

   https://doi.org/10.3847/1538-4365/ac1f1d  

   Bobra, Monica G. ; Wright, Paul J. ; Sun, Xudong ; Turmon, Michael J. ( September 2021 , The Astrophysical Journal Supplement Series)

   Abstract We present a new data product, called Space-Weather MDI Active Region Patches (SMARPs), derived from maps of the solar surface magnetic field taken by the Michelson Doppler Imager on board the Solar and Heliospheric Observatory. Together with the Space-Weather HMI Active Region Patches (SHARPs), derived from similar maps taken by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory, these data provide a continuous and seamless set of maps and keywords that describe every active region observed over the last two solar cycles, from 1996 to the present day. In this paper, we describe the SMARP data and compare it to the SHARP data. 

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4. Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine-learning Methods

   https://doi.org/10.3847/1538-4365/ac9b17  

   Zhang, Hewei ; Li, Qin ; Yang, Yanxing ; Jing, Ju ; Wang, Jason T. L. ; Wang, Haimin ; Shang, Zuofeng ( December 2022 , The Astrophysical Journal Supplement Series)

   Solar flares, especially the M- and X-class flares, are often associated with coronal mass ejections. They are the most important sources of space weather effects, which can severely impact the near-Earth environment. Thus it is essential to forecast flares (especially the M- and X-class ones) to mitigate their destructive and hazardous consequences. Here, we introduce several statistical and machine-learning approaches to the prediction of an active region’s (AR) flare index (FI) that quantifies the flare productivity of an AR by taking into account the number of different class flares within a certain time interval. Specifically, our sample includes 563 ARs that appeared on the solar disk from 2010 May to 2017 December. The 25 magnetic parameters, provided by the Space-weather HMI Active Region Patches (SHARP) from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory, characterize coronal magnetic energy stored in ARs by proxy and are used as the predictors. We investigate the relationship between these SHARP parameters and the FI of ARs with a machine-learning algorithm (spline regression) and the resampling method (Synthetic Minority Oversampling Technique for Regression with Gaussian Noise). Based on the established relationship, we are able to predict the value of FIs for a given AR within the next 1 day period. Compared with other four popular machine-learning algorithms, our methods improve the accuracy of FI prediction, especially for a large FI. In addition, we sort the importance of SHARP parameters by the Borda count method calculated from the ranks that are rendered by nine different machine-learning methods.

    

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5. Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters

   https://doi.org/10.1029/2019SW002214  

   Chen, Yang ; Manchester, Ward B. ; Hero, Alfred O. ; Toth, Gabor ; DuFumier, Benoit ; Zhou, Tian ; Wang, Xiantong ; Zhu, Haonan ; Sun, Zeyu ; Gombosi, Tamas I. ( October 2019 , Space Weather)

   In this paper we present several methods to identify precursors that show great promise for early predictions of solar flare events. A data preprocessing pipeline is built to extract useful data from multiple sources, Geostationary Operational Environmental Satellites and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), to prepare inputs for machine learning algorithms. Two classification models are presented: classification of flares from quiet times for active regions and classification of strong versus weak flare events. We adopt deep learning algorithms to capture both spatial and temporal information from HMI magnetogram data. Effective feature extraction and feature selection with raw magnetogram data using deep learning and statistical algorithms enable us to train classification models to achieve almost as good performance as using active region parameters provided in HMI/Space‐Weather HMI‐Active Region Patch (SHARP) data files. Case studies show a significant increase in the prediction score around 20 hr before strong solar flare events.

    

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