Source-linked AI summary
Discovering Dual-Origin Slow Wind from Solar Orbiter with Self-Supervised Contrastive Learning
Henry Han, Jorge Yero Salazar
TL;DR
The paper addresses whether slow solar wind has one coronal source or two channels that overlap in speed and differ mainly in heavy-ion composition. Solar-CDC learns a latent representation with physically initialized, dynamically updated pseudo-labels and a triplet margin objective. On 30,602 Solar Orbiter observations, it achieves stronger geometric separation than standard baselines while recovering physically aligned populations, although the data cannot test Alfvénicity.
Problem
The unresolved problem is separating slow-wind populations that have nearly identical bulk speeds but differ in joint heavy-ion composition using unlabeled in-situ measurements.
Method
Solar-CDC uses a Transformer encoder, triplet margin learning, and dynamically updated pseudo-labels initialized by physical observables to learn a source-oriented latent space.
Results
0.869 silhouette: Solar-CDC outperforms thirty dimensionality-reduction and clustering combinations peaking at 0.454 and recovers a compositionally aligned dual-origin partition.
Takeaways & Limitations
The recovered intermediate-composition population supports the dual-origin hypothesis, while the result depends on physically aware initialization rather than separation alone.
Takeaways & Limitations
The learned objective can separate an unphysical partition, and the silhouette coefficient cannot statistically distinguish k = 2 from k = 3.
Abstract
from arXiv · showhide
Whether the slow solar wind originates from one coronal source or two distinct channels remains a central open question in heliophysics. Resolving this requires unsupervised separation of two populations that arrive at nearly the same bulk speed and differ mainly in heavy-ion composition. We present Solar-CDC, a self-supervised contrastive deep clustering (CDC) framework that maps plasma observables to a latent space via a Transformer encoder, optimizes a triplet margin loss, and updates pseudo-labels via $k$-means. Theoretically, we prove that neighborhood-preserving embeddings such as t-SNE and UMAP are fundamentally constrained. Preserving the neighbor graph leaves the cross-cluster cut fraction unchanged, and preserving all but a fraction $\varepsilon$ of its links moves that fraction by at most $\varepsilon$. Neither bound depends on the target dimension. A margin objective rewrites the graph and drives the cut fraction to zero. Empirically, on 30,602 Solar Orbiter observations, thirty combinations of dimensionality reduction and clustering peak at a silhouette of $0.454$, whereas Solar-CDC reaches $0.869$. Escaping the geometric bound alone does not guarantee physical validity: TriMap also optimizes triplets and reaches $0.824$, yet its clusters score below chance against the published composition taxonomy. Solar-CDC instead recovers clusters with mean charge-state ratios of $0.080$, $0.160$, and $0.400$, placing the intermediate population inside the window associated with coronal-hole boundaries. Even when the defining charge-state ratio is withheld from the inputs entirely, the model still recovers the taxonomy defined on it. Solar-CDC thus connects self-supervised representation learning to coronal source diagnostics. Importantly, a learning loss recovers physical populations only when driven by dynamically updated physically-aware clusters rather than distances.
1 Introduction
The paper frames dual-origin slow-wind separation as an unsupervised composition problem because the candidate populations overlap in speed. Solar-CDC combines physically initialized pseudo-labels with contrastive representation learning to recover physically meaningful populations.
- The slow wind may originate from one coronal source or two distinct channels, a central unresolved question in heliophysics.
- The task requires distinguishing fast wind, streamer-belt slow wind, and boundary-type slow wind using in-situ plasma measurements alone.
- The two slow-wind populations overlap in bulk speed, so their boundary lies in joint heavy-ion composition rather than any single threshold variable.
- Solar-CDC learns a source-oriented latent representation and splits the slow wind into populations differing by 4 km s−1 in speed and a factor of 2.5 in oxygen charge state.
- Solar-CDC alternates representation updates with group re-formation, using a Transformer encoder, contrastive pulls and pushes, and unlabeled pseudo-labels.
- Physically measured warm-up groups anchor the final partition, while the theoretical and empirical contributions distinguish objective-driven separation from arbitrary geometric structure.
2 Related Work: Why Standard Tools Fall Short
Standard thresholds and neighborhood-preserving projections cannot separate slow-wind populations tangled in composition space. Solar-CDC addresses this by actively separating populations without labels or physics-damaging augmentations.
- Traditional thresholds and bulk-parameter clustering separate fast from slow wind but struggle because candidate slow-wind populations overlap almost perfectly in bulk speed.
- Predefined-label composition schemes motivate removing dependence on human-assigned class labels.
- t-SNE, UMAP, and PHATE preserve local neighborhoods, keeping mutually neighboring slow-wind populations tangled rather than actively separating them.
- TriMap escapes the neighborhood-preservation bound through triplet relationships but produces a geometrically tight partition that does not match the composition taxonomy.
- Standard image augmentations do not transfer to seven-dimensional composition vectors because altering charge-state ratios changes the physical meaning of the measurement.
- Solar-CDC combines observable-based initialization, an alternating triplet-loss loop, label-free learning, and no augmentation to separate overlapping populations.
3 Solar Orbiter Data and Feature Space
The study uses Solar Orbiter in-situ measurements combining bulk plasma properties and heavy-ion composition. Its seven physical observables provide the feature space for distinguishing solar-wind populations.
- Solar Orbiter observations from January 2022 through April 2023 combine SWA-PAS bulk plasma measurements with SWA-HIS ion composition.
- The model input consists of seven physical observables drawn from bulk plasma properties and ion composition.
- Proton bulk speed separates fast from slow wind but cannot distinguish the two slow-wind sources, while proton density is higher in slow wind.
4 Methods
Solar-CDC learns a physically informed latent representation and partition from unlabeled plasma observations through warm-started, iteratively refreshed pseudo-labels and contrastive training. Its margin objective separates compositionally distinct slow-wind populations without manual thresholds or ground-truth labels.
- Representation learning: Solar-CDC maps seven plasma observables into a latent space where distances reflect coronal-source similarity rather than bulk-speed similarity.A Transformer encoder produces the latent codes, whose dimension is not required to be smaller than the input dimension.
- Representation learning: The method jointly learns latent codes and clusters intended to recover fast wind, boundary-type slow wind, and streamer-belt slow wind.The partition is defined over latent codes rather than directly in the original observable space.
- Contrastive objective: Triplet training pulls each observation toward a same-group real observation and pushes it from a different-group observation by a fixed margin.The margin imposes relative distance constraints that break cross-boundary neighbor links rather than merely drawing points toward cluster centers.
- Iterative refinement: Every p epochs, k-means reruns on updated latent codes, refreshing the pseudo-labels that define subsequent positive and negative triplets.This feedback loop lets the grouping and representation refine one another during training.
- Physical separation: The contrastive loop separates two slow-wind streams that bulk speed cannot distinguish, without manual velocity thresholds or ground-truth labels.The learned boundary is placed according to joint charge-state structure.
- Pseudo-label initialization: Warmup initializes pseudo-labels with k-means on standardized observables, anchoring the first assignments to physical plasma properties instead of random encoder noise.After initialization, physical knowledge is not supplied through external labels; subsequent supervision is self-supplied.
5 A Separability Bound for Neighborhood-Preserving Embeddings
The section shows that neighborhood-preserving embeddings cannot improve cross-cluster separability when populations overlap in the input neighbor graph, regardless of target dimension. Margin-based embeddings can rewrite that graph and remove cross-cluster neighbors, but physical initialization determines which partition is separated.
- The target dimension does not determine separability; the embedding objective does.A mapping can separate arbitrary clusters in two dimensions, while neighborhood preservation constrains separation independently of dimension.
- Exact κ-neighborhood preservation leaves the cut fraction unchanged for every partition.The result also applies to graph functionals such as conductance, modularity, and spectral-clustering objectives.
- ε-approximate neighborhood preservation changes the cut fraction by at most ε.This bound follows from the limited number of altered neighbor links and applies to methods such as t-SNE and UMAP.
- A margin-satisfying embedding drives the cut fraction to zero when κ is smaller than the smallest cluster size.The margin places every observation at least α farther from other clusters than from any same-cluster member.
- At κ = 10, the raw cut fraction is β10 = 0.064, while t-SNE and UMAP return 0.071 and 0.084 and do not improve it.PHATE and PCA return 0.148 and 0.174; none of these four methods moves the cut fraction downward.
- Solar-CDC reduces the cut fraction to β10 = 0.001, but the margin objective alone does not ensure a physically meaningful partition.The learned objective separates the partition it receives, so warmup from physical composition ratios determines which partition is sharpened.
6 Results
Solar-CDC achieves sharply separated clusters while showing that geometric tightness alone does not establish physical validity. Its recovered populations align with composition-based solar-wind structure, including when the defining oxygen ratio is withheld.
- 0.869 is Solar-CDC’s optimal silhouette, compared with a 0.454 peak across thirty neighborhood-preserving baseline combinations.The optimal configuration uses one attention head and a 2-D latent code; the grid mean is 0.795.
- Solar-CDC’s latent dimension significantly affects performance, whereas attention-head count does not.The Friedman test gives χ2 = 22.3, p = 1.8 × 10−4 for latent dimension and χ2 = 7.4, p = 0.19 for attention heads.
- TriMap reaches a 0.824 silhouette, but its geometrically tight clusters are less physically meaningful than lower-silhouette neighborhood-preserving embeddings.Neighborhood-preserving methods achieve NMI 0.33–0.42, while Solar-CDC is the only compared method reported to achieve both geometric separation and physical agreement.
- 0.321 NMI and 0.653 accuracy remain achievable when O7+/O6+ is withheld, showing that the recovered boundaries are not memorized from that single ratio.On the same six observables, k-means reaches NMI 0.311, statistically indistinguishable from Solar-CDC, while Solar-CDC retains a matched-accuracy advantage.
- The three recovered clusters have mean O7+/O6+ ratios of 0.080, 0.160, and 0.400, matching coronal-hole, boundary, and streamer-belt windows.The two slow populations differ by only 4 km s−1 in bulk speed but by a factor of 2.5 in oxygen charge state.
7 Discussion
The recovered intermediate-composition population supports the dual-origin hypothesis, but its Alfvénic identity cannot be tested without magnetic and velocity-fluctuation data. Cross-method silhouette comparisons are indicative rather than absolute, and model selection still relies on external reference labels.
- The recovered intermediate-composition slow-wind population supports the dual-origin hypothesis despite nearly overlapping bulk speeds.The evidence is extracted without labels and separates the population from streamer-belt wind by composition.
- Alfvénicity remains untested because the seven observables omit magnetic and velocity fluctuations.Cluster 1 is therefore designated boundary type, while its identification as Alfvénic slow wind remains plausible rather than established.
- Silhouette comparisons between Solar-CDC and dimensionality-reduction baselines are indicative rather than absolute because each method is scored in its own representation space.The internal Solar-CDC ablations use identical protocols and are directly comparable.
- Configuration choice currently relies on external reference labels, leaving a fully unsupervised criterion for physical correctness as an open challenge.The discussion also identifies incorporating magnetic fluctuation statistics as the most valuable extension.
8 Conclusion
Solar-CDC recovers composition-ordered solar-wind clusters while surpassing conventional dimensionality-reduction and clustering combinations. The paper argues that margin-based objectives and physically anchored initialization overcome geometric constraints, while identifying broader validation needs.
- 0.869 silhouette on 30,602 Solar Orbiter observations surpassed thirty dimensionality-reduction and clustering combinations, which peaked at 0.454 with zero distribution overlap.
- Recovered clusters were monotonically ordered across every composition variable, mirroring the established charge-sequence of coronal source regions.
- Neighborhood-preserving embeddings cannot reduce a partition’s cross-cluster cut fraction below its input-metric baseline, regardless of target dimensionality.
- A margin objective can rewrite the graph and drive the cut fraction to zero, but success depends on initialization anchored in physical plasma measurements.
- Future work proposes testing broader archives, integrating magnetic-field and fluctuation data, and applying the domain-agnostic bound to other near-neighbor populations.
A Supplementary Results
Table 7 compares silhouette across the full configuration grid using seven observables versus those observables plus two additional input columns.
- Table 7 evaluates 30,602 observations with k = 3, 200 epochs, and p = 4 across grids with seven or nine observables.