Intense solar eruptions can be followed by solar proton events (SPEs) and geomagnetic storms (GMSs), posing potential risks to spacecraft operations, communication and navigation systems, and power grids. Existing studies mainly forecast a single disturbance type, with limited attention to subsequent disturbances associated with an individual solar eruption. In this study, we construct a space weather event database in which each solar eruption is treated as the basic sample unit. The database records observational features of flares and coronal mass ejections (CMEs) and labels whether SPEs and GMS subsequently occurred. Based on this database, we propose Solar Contrastive Embedding (SOLACE) with Attentional Factorization Machines, a representation learning framework that integrates contrastive learning and feature interaction modeling to support historical-event retrieval and probabilistic prediction. Experimental results show that SOLACE performs well in probabilistic prediction of SPEs and GMS. For SPE prediction, the mean F1 score, false-alarm ratio (FAR), and area under the receiver operating characteristic curve (AUC) are 0.734 ± 0.065, 0.287 ± 0.077, and 0.951 ± 0.021, respectively. For GMS prediction, the corresponding values are 0.733 ± 0.036, 0.311 ± 0.062, and 0.924 ± 0.014. Interpretation analyses show that SPE prediction mainly relies on CME speed, CME angular width, heliolongitude, and flare soft X-ray fluence, whereas GMS prediction emphasizes CME angular width and source-region location, with propagation characteristics and interactions involving angular width also contributing. By learning physically meaningful representations of solar eruptive events, SOLACE provides a unified framework for the early prediction of subsequent space weather disturbances.
Abstract Photochemical hazes play an important role in shaping the spectra and radiative balance of N 2 -dominated planetary atmospheres. We present newly acquired Fourier transform infrared (FTIR) and retrieved optical constants ( N = n + ik ) of laboratory-generated haze analogs from N 2 /CH 4 and N 2 /CH 4 /CO gas mixtures under plasma discharge conditions. The retrievals use particle densities newly measured for the CH 4 series and previously published particle densities for the CO series. The experiments systematically explored CH 4 concentrations from 0.5% to 10% and CO concentrations from 0% to 5% with fixed 5% CH 4 . Using measured particle densities together with the Beer–Lambert law and the subtractive Kramers–Kronig (SKK) relation, we derived optical constants over the 350–25,000 cm −1 (0.4–28.6 μ m) spectral range, with the 0.4–25 μ m results presented in the main text. The infrared spectra reveal prominent absorption features associated with hydrocarbon-, nitrogen-, and oxygen-bearing functional groups. Increasing CH 4 abundance enhances aliphatic hydrocarbon features and corresponds to decreasing particle density, whereas increasing CO abundance promotes oxygen incorporation, broader mid-infrared absorptions, and higher particle density. The derived k spectra exhibit strong absorptions near ∼3 μ m, ∼4.6 μ m, and ∼6–10 μ m, while the real refractive index n generally ranges from ∼1.2 to 1.7. The controlled CH 4 and CO series establish composition-dependent variations in haze optical properties. A benchmark comparison among Titan-, Pluto-, and Triton-like haze analogs then uses these experimentally identified trends to interpret the optical differences among N 2 -dominated planetary haze compositions. The density and optical constants provide laboratory constraints for atmospheric radiative transfer models and for interpreting planetary and exoplanetary spectra from spacecraft and telescopes.
On 2026 February 4, an interplanetary shock crossed the L1 region and was followed about 7.7 hr later by a heliospheric current sheet/heliospheric plasma sheet (HCS/HPS), while energetic-ion intensities continued to evolve throughout the intervening downstream region. The shock was observed by IMAP, Wind, ACE, DSCOVR, and SOLAR-1, providing a multispacecraft view of its geometry. Multispacecraft timing and local shock-normal analyses show that the shock is highly quasiperpendicular but spatially nonplanar across L1. Energetic-ion intensities exhibit a pronounced energy-dependent response. Sub-MeV intensities increase well upstream of the shock and remain enhanced throughout the downstream region until approaching the HCS/HPS, whereas intensities above ∼1 MeV show a much weaker downstream enhancement and tend to peak near the shock. The energy spectra also soften downstream, with the fitted spectral break shifting from several MeV near the shock to lower energies farther downstream. Pitch-angle distributions evolve from pronounced field-aligned streaming upstream to enhanced intensities at oblique-to-perpendicular pitch angles downstream, with the anisotropy persisting through much of the downstream sheath and gradually broadening toward the HCS/HPS. A secondary current sheet embedded within the downstream HPS exhibits signatures consistent with a localized reconnection exhaust. Overall, the observations indicate that the sub-MeV ion population is strongly influenced by the shock, while its subsequent downstream evolution is shaped primarily by energy-dependent transport. Localized energization within the disturbed downstream sheath may provide an additional contribution to the sub-MeV population.
IBEX-Hi observed four heliotail lobes above ∼2 keV: the energetic neutral atom (ENA)-suppressed port (P) and starboard (SB) near the ecliptic and the ENA-enhanced north (N) and south (S) at midlatitudes. They trace how the latitude-dependent solar wind (SW) and solar and interstellar magnetic fields shape the downwind heliosheath (HS). Prior work, using five nonoverlapping periods over 11 yr, found cyclic P–SB motion and equatorward N–S motion in phase with the solar cycle. We extend this analysis through 2022 using 14 smoothed yearly maps and add three methods: an intensity-weighted moment tensor (for lobe shapes), spatial and temporal block uncertainty estimates, and elliptical fits to lobe centers (for motion loci). At ∼4.3 keV, downwind-hemispheric intensity drops ∼2.4× from 2009 to 2016, partially recovering by 2022; all four lobes weaken and recover together, lagging the polar coronal hole area by ∼3 yr. P and SB track the reported cyclic motion; SB shows the largest range (∼52° longitude, ∼36° latitude). N and S move equatorward through 2017 (N ∼59° to ∼27°, S ∼−48° to ∼−24°), partially returning by 2022. The N–S great circle lies near an ecliptic meridian ( α ∼ 89 $\mathop{.}\limits^{{\unicode{x000b0}}}$ 3), P–SB near the ecliptic ( β ∼ 23 $\mathop{.}\limits^{{\unicode{x000b0}}}$ 8), and they are not orthogonal ( γ ∼78 $\mathop{.}\limits^{{\unicode{x000b0}}}$ 1), qualitatively consistent with interstellar field draping around the heliopause. Fitted motion loci constrain models: the P-lobe is least elongated, the S-lobe most elongated, the SB-lobe longest, and the N-lobe least reliable owing to ribbon contribution. Results constrain the distant tailward HS and its evolution with the solar cycle, SW, and charge-exchange cooling.
Magnetic reconnection is a fundamental plasma process that plays a crucial role in energy dissipation in plasma turbulence, in which magnetic energy is rapidly converted into particle energy. However, the identification of reconnection within turbulence remains a significant challenge. To automatically detect magnetic reconnection in plasma turbulence, we propose a novel and efficient classifier called a dual-branch lightweight hybrid convolutional neural network (DHCNN), which, for the first time, achieves automatic localization of reconnection sites by effectively leveraging negative-valued information in physical variables. We adopt a temporal partitioning scheme together with diverse data augmentation strategies to enhance the model’s generalization ability. Our results show that the DHCNN accurately identifies reconnection sites, achieving a Matthews correlation coefficient ( M ) of 0.67, a true skill statistic ( T ) of 0.67, a recall ( R ) of 0.83, a specificity ( S ) of 0.84, and an accuracy ( A ) of 0.84. Finally, by analyzing the impact of different physical variables, we find that the total current density (| J |), in-plane components of the magnetic field ( B _x , B _z ), and energy conversion term ( D _e ) are the primary drivers of the model’s performance.
