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Abstract We present a novel deep generative model, named GenMDI, to improve the temporal resolution of line-of-sight (LOS) magnetograms of solar active regions (ARs) collected by the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory. Unlike previous studies that focus primarily on spatial super-resolution of MDI magnetograms, our approach can perform temporal super-resolution, which generates and inserts synthetic data between observed MDI magnetograms, thus providing finer temporal structure and enhanced details in the LOS data. The GenMDI model employs a conditional diffusion process, which synthesizes images by considering both preceding and subsequent magnetograms, ensuring that the generated images are not only of high quality but also temporally coherent with the surrounding data. Experimental results show that the GenMDI model performs better than the traditional linear interpolation method, especially in ARs with dynamic evolution in magnetic fields. 

Abstract We examine a century of radial velocity, visual magnitude, and astrometric observations of the nearest red supergiant, Betelgeuse, in order to reexamine the century-old assertion that Betelgeuse might be a spectroscopic binary. These data reveal Betelgeuse varying stochastically over years and decades due to its boiling, convective envelope, periodically with a 5.78 yr long secondary period (LSP), and quasiperiodically from pulsations with periods of several hundred days. We show that the LSP is consistent between astrometric and radial velocity data sets, and argue that it indicates a low-mass companion to Betelgeuse, less than a solar mass, orbiting in a 2110 day period at a separation of just over twice Betelgeuse’s radius. The companion star would be nearly 20 times less massive and a million times fainter than Betelgeuse, with similar effective temperature, effectively hiding it in plain sight near one of the best-studied stars in the night sky. The astrometric data favor an edge-on binary with orbital plane aligned with Betelgeuse’s measured spin axis. Tidal spin–orbit interaction drains angular momentum from the orbit and spins up Betelgeuse, explaining the spin–orbit alignment and Betelgeuse’s anomalously rapid spin. In the future, the orbit will decay until the companion is swallowed by Betelgeuse in the next 10,000 yr. 

Abstract Direct imaging observations are biased toward wide-separation, massive companions that have degenerate formation histories. Although the majority of exoplanets are expected to form via core accretion, most directly imaged exoplanets have not been convincingly demonstrated to follow this formation pathway. We obtained new interferometric observations of the directly imaged giant planet AF Lep b with the VLTI/GRAVITY instrument. We present three epochs of ∼50μas relative astrometry and theK-band spectrum of the planet for the first time at a resolution ofR= 500. Using only these measurements, spanning less than 2 months, and the Hipparcos-Gaia Catalogue of Accelerations, we are able to significantly constrain the planet’s orbit; this bodes well for interferometric observations of planets discovered by Gaia DR4. Including all available measurements of the planet, we infer an effectively circular orbit (e< 0.02, 0.07, and 0.13 at 1σ, 2σ, and 3σ, respectively) in spin–orbit alignment with the host and measure a dynamical mass ofM_p= 3.75M_Jup± 0.5M_Jup. Models of the spectrum of the planet show that it is metal-rich ([M/H] = 0.75 ± 0.25), with a C/O abundance encompassing the solar value. This ensemble of results shows that the planet is consistent with core accretion formation. 

Abstract We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short‐term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short‐term forecasts of the SYM‐H index based on 1‐ and 5‐min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM‐H index value at time pointt + whours for a given time pointtwherewis 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM‐H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1‐ and 5‐min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM‐H indices (1 hr in advance) in a large storm (SYM‐H = −393 nT) using 5‐min resolution data. When predicting the SYM‐H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot. 

Abstract Solar flares are explosions on the Sun. They happen when energy stored in magnetic fields around solar active regions (ARs) is suddenly released. Solar flares and accompanied coronal mass ejections are sources of space weather, which negatively affects a variety of technologies at or near Earth, ranging from blocking high-frequency radio waves used for radio communication to degrading power grid operations. Monitoring and providing early and accurate prediction of solar flares is therefore crucial for preparedness and disaster risk management. In this article, we present a transformer-based framework, named SolarFlareNet, for predicting whether an AR would produce a$$\gamma$$ $\gamma$-class flare within the next 24 to 72 h. We consider three$$\gamma$$ $\gamma$classes, namely the$$\ge$$ $\ge$M5.0 class, the$$\ge$$ $\ge$M class and the$$\ge$$ $\ge$C class, and build three transformers separately, each corresponding to a$$\gamma$$ $\gamma$class. Each transformer is used to make predictions of its corresponding$$\gamma$$ $\gamma$-class flares. The crux of our approach is to model data samples in an AR as time series and to use transformers to capture the temporal dynamics of the data samples. Each data sample consists of magnetic parameters taken from Space-weather HMI Active Region Patches (SHARP) and related data products. We survey flare events that occurred from May 2010 to December 2022 using the Geostationary Operational Environmental Satellite X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and build a database of flares with identified ARs in the NCEI flare catalogs. This flare database is used to construct labels of the data samples suitable for machine learning. We further extend the deterministic approach to a calibration-based probabilistic forecasting method. The SolarFlareNet system is fully operational and is capable of making near real-time predictions of solar flares on the Web. 

Abstract Interplanetary magnetic flux ropes (MFRs) are commonly observed structures in the solar wind, categorized as magnetic clouds (MCs) and small-scale MFRs (SMFRs) depending on whether they are associated with coronal mass ejections. We apply machine learning to systematically compare SMFRs, MCs, and ambient solar wind plasma properties. We construct a data set of 3-minute averaged sequential data points of the solar wind’s instantaneous bulk fluid plasma properties using about 20 years of measurements from Wind. We label samples by the presence and type of MFRs containing them using a catalog based on Grad–Shafranov (GS) automated detection for SMFRs and NASA's catalog for MCs (with samples in neither labeled non-MFRs). We apply the random forest machine learning algorithm to find which categories can be more easily distinguished and by what features. MCs were distinguished from non-MFRs with an area under the receiver-operator curve (AUC) of 94% and SMFRs with an AUC of 89%, and had distinctive plasma properties. In contrast, while SMFRs were distinguished from non-MFRs with an AUC of 86%, this appears to rely solely on the 〈B〉 > 5 nT threshold applied by the GS catalog. The results indicate that SMFRs have virtually the same plasma properties as the ambient solar wind, unlike the distinct plasma regimes of MCs. We interpret our findings as additional evidence that most SMFRs at 1 au are generated within the solar wind. We also suggest that they should be considered a salient feature of the solar wind’s magnetic structure rather than transient events.