We present a comprehensive analysis of 66 interplanetary shocks observed by the Parker Solar Probe between 2018 November and 2024 January. Among these, 33 events fulfilled the Rankine–Hugoniot (R-H) conditions, ensuring reliable asymptotic plasma parameter solutions. The remaining 33 events could not be confirmed by the standard R-H approach—potentially including wave-like structures—yet were analyzed via averaging and mixed-data methods to obtain robust shock parameters. Utilizing our ShOck Detection Algorithm database, the shocks are categorized into fast-forward, fast-reverse, slow-forward, and slow-reverse types. We investigate the statistical properties of these shocks, focusing on correlations between key parameters—magnetic field compression, density compression, shock normal angle, and change in velocity—and heliocentric distance. Significant positive correlations are identified between heliocentric distance and both magnetic field compression and density compression, suggesting that shocks strengthen as they propagate away from the Sun, largely due to the high local magnetosonic speeds closer to the Sun that can suppress shock formation except in extremely fast events. These findings provide new insights into the dynamic processes governing shock evolution in the inner heliosphere, including scenarios where the near-radial magnetic field geometry may lead to predominantly quasi-parallel shock configurations and thus affect near-Sun particle acceleration efficiency. We also provide strong evidence for the existence of slow-mode shocks near the Sun, contributing to the understanding of shock formation and evolution in the inner heliosphere.

We present a hybridized unsupervised clustering algorithm Hisaxy as a novel way to identify frequently occurring magnetic structures embedded in the interplanetary magnetic field (IMF) carried by the solar wind. The Hisaxy algorithm utilizes a combination of indexable Symbolic Aggregate approXimation (iSAX) and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to efficiently identify clusters of patterns embedded in time series data. We utilized Hisaxy to identify small-scale structures, known as discontinuities, embedded in time series measurements of the IMF. In doing so, we demonstrate the capability of the algorithm to significantly reduce the amount of human analysis hours required to identify these structures, all the while maintaining a high degree of self similarity within a given cluster of time series data.

Decades of in-situ solar-wind measurements have clearly established the variation of solar-wind physical parameters. These variable parameters have been used to classify the solar-wind magnetized plasma into different types, leading to several classification schemes being developed. These classification schemes, while useful for understanding the solar wind’s originating processes at the Sun and early detection of space weather events, have left open questions regarding which physical parameters are most useful for classification and how recent advances in our understanding of solar-wind transients impact classification. In this work, we use neural networks trained with different solar-wind magnetic and plasma characteristics to automatically classify the solar wind in coronal hole, streamer belt, sector reversal and solar transients such as coronal mass ejections comprised of both magnetic obstacles and sheaths. Furthermore, our work demonstrates how probabilistic neural networks can enhance the classification by including a measure of prediction uncertainty. Our work also provides a ranking of the parameters that lead to an improved classification scheme with ∼ 96% accuracy. Our new scheme paves the way for incorporating uncertainty estimates into space-weather forecasting with the potential to be implemented on real-time solar-wind data.

Detecting large-scale flux ropes (FRs) embedded in interplanetary coronal mass ejections (ICMEs) and assessing their geoeffectiveness are essential, since they can drive severe space weather. At 1 au, these FRs have an average duration of 1 day. Their most common magnetic features are large, smoothly rotating magnetic fields. Their manual detection has become a relatively common practice over decades, although visual detection can be time-consuming and subject to observer bias. Our study proposes a pipeline that utilizes two supervised binary classification machine-learning models trained with solar wind magnetic properties to automatically detect large-scale FRs and additionally determine their geoeffectiveness. The first model is used to generate a list of autodetected FRs. Using the properties of the southward magnetic field, the second model determines the geoeffectiveness of FRs. Our method identifies 88.6% and 80% of large-scale ICMEs (duration >= 1 day) observed at 1 au by the Wind and the Solar TErrestrial RElations Observatory missions, respectively. While testing with continuous solar wind data obtained from Wind, our pipeline detected 56 of the 64 large-scale ICMEs during the 2008-2014 period (recall = 0.875), but also many false positives (precision = 0.56), as we do not take into account any additional solar wind properties other than the magnetic properties. We find an accuracy of 0.88 when estimating the geoeffectiveness of the autodetected FRs using our method. Thus, in space-weather nowcasting and forecasting at L1 or any planetary missions, our pipeline can be utilized to offer a first-order detection of large-scale FRs and their geoeffectiveness.

The magnitude and complexity of the problems confronting Solar and SpacePhysics from 2024-2033 require the integration of many individuals, groups, data sources, and technologies (i.e., the interconnection of all of our knowledge)2. The solution is a 'knowledge commons': a combination of intelligent information representation and the openness, governance, and trust required to create a participatory ecosystem whereby the whole community maintains and evolves this shared information space 3. To build and sustain a flourishing knowledge commons requires broader participation (e.g., bringing Heliophysicists together with data scientists), adoption of open science practices, and centering responsible and ethical considerations for how we do our research

Magnetic flux ropes manifest as twisted bundles of magnetic field lines. They carry significant amounts of solar mass in the heliosphere. This paper underlines the need to advance our understanding of the fundamental physics of heliospheric flux ropes and provides the motivation to significantly improve the status quo of flux rope research through novel and requisite approaches. It briefly discusses the current understanding of flux rope formation and evolution, and summarizes the strategies that have been undertaken to understand the dynamics of heliospheric structures. The challenges and recommendations put forward to address them are expected to broaden the in-depth knowledge of our nearest star, its dynamics, and its role in its region of influence, the heliosphere.

This white paper underlines the lack of fundamental understanding of heliospheric flux ropes and provides the motivation to significantly improve the status quo of flux rope research through novel and requisite approaches.The recommendations put forward in this paper will broaden the in-depth knowledge of our nearest star, its dynamics, and its role in its sphere of influence, the heliosphere.

Traditionally, data analysis and theory have been viewed as separate disciplines, each feeding into fundamentally different types of models. Modern deep learning technology is beginning to unify these two disciplines and will produce a new class of predictively powerful space weather models that combine the physical insights gained by data and theory. We call on NASA to invest in the research and infrastructure necessary for the heliophysics' community to take advantage of these advances.

Over the last decade, Heliophysics researchers have increasingly adopted a variety of machine learning methods such as artificial neural networks, decision trees, and clustering algorithms into their workflow. Adoption of these advanced data science methods had quickly outpaced institutional response, but many professional organizations such as the European Commission, the National Aeronautics and Space Administration (NASA), and the American Geophysical Union have now issued (or will soon issue) standards for artificial intelligence and machine learning that will impact scientific research. These standards add further (necessary) burdens on the individual researcher who must now prepare the public release of data and code in addition to traditional paper writing. Support for these is not reflected in the current state of institutional support, community practices, or governance systems. We examine here some of these principles and how our institutions and community can promote their successful adoption within the Heliophysics discipline.

Geomagnetic disturbance forecasting is based on the identification of solar wind structures and accurate determination of their magnetic field orientation. For nowcasting activities, this is currently a tedious and manual process. Focusing on the main driver of geomagnetic disturbances, the twisted internal magnetic field of interplanetary coronal mass ejections (ICMEs), we explore a convolutional neural network’s (CNN) ability to predict the embedded magnetic flux rope’s orientation once it has been identified from in situ solar wind observations. Our work uses CNNs trained with magnetic field vectors from analytical flux rope data. The simulated flux ropes span many possible spacecraft trajectories and flux rope orientations. We train CNNs first with full duration flux ropes and then again with partial duration flux ropes. The former provides us with a baseline of how well CNNs can predict flux rope orientation while the latter provides insights into real-time forecasting by exploring how accuracy is affected by percentage of flux rope observed. The process of casting the physics problem as a machine learning problem is discussed as well as the impacts of different factors on prediction accuracy such as flux rope fluctuations and different neural network topologies. Finally, results from evaluating the trained network against observed ICMEs from Wind during 1995–2015 are presented.

Type III bursts are manifestations of energetic electrons associated with solar flares. Since propagation of radio waves in the interplanetary medium is strongly affected by random electron density fluctuations, interplanetary type III bursts provide us with a unique diagnostic tool for solar wind remote plasma measurements. We developed a type III burst automated recognition tool applicable to the Wind and STEREO measurements in order to build an extensive database of radio events. We compared parameters of radio bursts associated with solar energetic particles (SEPs). We investigate possible correlations between solar flares, type III bursts, and SEP events. We discuss possible capabilities of real-time interplanetary radio observations, including direction-finding, to enhance current SEP forecasting.

We investigate the effects of the evolutionary processes in the internal magnetic structure of two interplanetary coronal mass ejections (ICMEs) detected in situ between 2020 November 29 and December 1 by the Parker Solar Probe (PSP). The sources of the ICMEs were observed remotely at the Sun in EUV and subsequently tracked to their coronal counterparts in white light. This period is of particular interest to the community as it has been identified as the first widespread solar energetic particle event of solar cycle 25. The distribution of various solar and heliospheric-dedicated spacecraft throughout the inner heliosphere during PSP observations of these large-scale magnetic structures enables a comprehensive analysis of the internal evolution and topology of such structures. By assembling different models and techniques, we identify the signatures of interaction between the two consecutive ICMEs and the implications for their internal structure. We use multispacecraft observations in combination with a remote-sensing forward modeling technique, numerical propagation models, and in situ reconstruction techniques. The outcome, from the full reconciliations, demonstrates that the two coronal mass ejections (CMEs) are interacting in the vicinity of the PSP. Thus, we identify the in situ observations based on the physical processes that are associated with the interaction and collision of both CMEs. We also expand the flux rope modeling and in situ reconstruction technique to incorporate the aging and expansion effects in a distorted internal magnetic structure and explore the implications of both effects in the magnetic configuration of the ICMEs.

The solar wind consists of charged particles ejected from the Sun into interplanetary space and towards Earth. Understanding the magnetic field of the solar wind is crucial for predicting future space weather and planetary atmospheric loss. Compared to large-scale magnetic events, smaller-scale structures like magnetic discontinuities are hard to detect but entail important information on the evolution of the solar wind. A lack of labeled data makes an automated detection of these discontinuities challenging. We propose Deep-SWIM, an approach leveraging advances in contrastive learning, pseudo-labeling and online hard example mining to robustly identify discontinuities in solar wind magnetic field data. Through a systematic ablation study, we show that we can accurately classify discontinuities despite learning from only limited labeled data. Additionally, we show that our approach generalizes well and produces results that agree with expert hand-labeling.

In this paper, we present an analysis of the internal structure of a coronal mass ejection (CME) detected by in situ instruments on board the Parker Solar Probe (PSP) spacecraft during its first solar encounter. On 2018 November 11 at 23:53 UT, the FIELDS magnetometer measured an increase in strength of the magnetic field as well as a coherent change in the field direction. The SWEAP instrument simultaneously detected a low proton temperature and signatures of bidirectionality in the electron pitch angle distribution (PAD). These signatures are indicative of a CME embedded in the slow solar wind. Operating in conjunction with PSP was the STEREO A spacecraft, which enabled the remote observation of a streamer blowout by the SECCHI suite of instruments. The source at the Sun of the slow and well-structured flux rope was identified in an overlying streamer, the details of which are described in Korreck et al. Our detailed inspection of the internal transient structure magnetic properties suggests high complexity in deviations from an ideal flux rope 3D topology. Reconstructions of the magnetic field configuration reveal a highly distorted structure consistent with the highly elongated "bubble" observed remotely. A double-ring substructure observed in the SECCHI-COR2 field of view (FOV) is suggestive of a double internal flux rope. Furthermore, we describe a scenario in which mixed topology of a closed flux rope is combined with the magnetically open structure, which helps explain the flux dropout observed in the measurements of the electron PAD. Our justification for this is the plethora of structures observed by the EUV imager (SECCHI-EUVI) in the hours preceding the streamer blowout evacuation. Finally, taking advantage of the unique observations from PSP, we explore the first stages of the effects of coupling with the solar wind and the evolutionary processes in the magnetic structure. We found evidence of bifurcated current sheets in the structure boundaries, suggestive of magnetic reconnection. Our analysis of the internal force imbalance indicates that internal Lorentz forces continue to dominate the evolution of the structure in the COR2 FOV and serve as the main driver of the internal flux rope distortion detected in situ at PSP solar distance.

In the first orbit of the Parker Solar Probe (PSP), in situ thermal plasma and magnetic field measurements were collected as close as 35 RSun from the Sun, an environment that had not been previously explored. During the first orbit of PSP, the spacecraft flew through a streamer blowout coronal mass ejection (SBO-CME) on 2018 November 11 at 23:50 UT as it exited the science encounter. The SBO-CME on November 11 was directed away from the Earth and was not visible by L1 or Earth-based telescopes due to this geometric configuration. However, PSP and the STEREO-A spacecraft were able to make observations of this slow (v ≈ 380 km s−1) SBO-CME. Using the PSP data, STEREO-A images, and Wang–Sheeley–Arge model, the source region of the CME is found to be a helmet streamer formed between the northern polar coronal hole and a mid-latitude coronal hole. Using the YGUAZU-A model, the propagation of the CME is traced from the source at the Sun to PSP. This model predicts the travel time of the flux rope to the PSP spacecraft as 30 hr, which is within 0.33 hr of the actual measured arrival time. The in situ Solar Wind Electrons Alphas and Protons data were examined to determine that no shock was associated with this SBO-CME. Modeling of the SBO-CME shows that no shock was present at PSP; however, at other positions along the SBO-CME front, a shock could have formed. The geometry of the event requires in situ and remote sensing observations to characterize the SBO-CME and further understand its role in space weather.