The study, led by Garima Karki of Kumaun University in Nainital, India, together with Brigitte Schmieder of the Observatoire de Paris and KU Leuven, Ramesh Chandra, Pooja Devi, Pascal Démoulin and Stefaan Poedts, focused on a quiescent filament observed on September 28, 2023. Quiescent filaments are the long-lived, relatively stable cousins of the structures that sometimes erupt as the engines of coronal mass ejections. They consist of dense, cool plasma at chromospheric temperatures, roughly 10,000 kelvin, embedded within the million-degree corona and supported against gravity by magnetic fields. Understanding how plasma enters, circulates within, and drains out of these structures is a central problem in solar physics, because the mass budget of a filament over its lifetime cannot be explained by static conditions alone. Material must be continuously supplied and removed, and the mechanisms responsible remain actively debated.

The key instrument in this investigation was the Télescope Héliographique pour l’Étude du Magnétisme et des Instabilités Solaires, or THEMIS, located on the Canary Island of Tenerife. THEMIS observed the filament in the H-alpha spectral line, the workhorse diagnostic of cool solar plasma, using its Multi-channel Subtractive Double Pass spectrograph in a scanning mode that delivered both high spatial resolution and high spectral resolution. Crucially, the THEMIS field of view happened to be centred on the northern end of the filament at the precise moment when one of a series of repetitive plasma motions swept through it. That coincidence of timing and pointing allowed the team to capture spectra of an elongated cool blob as it fell, and those spectra revealed H-alpha line profiles that were dramatically broadened and shifted, the spectroscopic fingerprints of extremely fast motion.

To convert those distorted line profiles into a velocity, the researchers employed a technique known as the cloud model, a classical method in solar spectroscopy that treats an individual absorbing structure as a uniform slab of plasma with well-defined properties: a source function, a Doppler width, a line-of-sight velocity, and an optical thickness. By fitting synthetic profiles to the observed ones and minimising the root-mean-square difference between model and measurement, the team derived a downflow speed of 89.8 kilometres per second, with a remarkably tight standard deviation of plus or minus 0.25 kilometres per second. For context, the sound speed in the cool filament plasma is on the order of 10 kilometres per second, so the falling material was moving at roughly nine times the local sound speed, unambiguously supersonic. Such speeds are rarely measured with this degree of confidence in quiescent filament structures, and the precision of the cloud-model fit lends the result considerable weight.

The supersonic measurement alone would be noteworthy, but the broader context of the observation makes it genuinely intriguing. Using data from the Global Oscillation Network Group, or GONG, a worldwide network of instruments that monitors the Sun in H-alpha around the clock, the team tracked plasma moving along the filament body toward its northern end. These repetitive motions suggested a conveyor-bear-like flow of material along the filament’s internal magnetic structure. When the flowing plasma reached the region imaged by THEMIS, it appeared to be falling toward the chromosphere. The researchers first considered a ballistic interpretation: material launched or displaced along the filament’s magnetic dips simply falling under gravity along a curved trajectory. A ballistic path could, in principle, account for the observed acceleration and speed, painting a picture of cool plasma draining from the filament body down into the dense lower atmosphere.

However, the team could not rule out a second and arguably more exotic scenario: coronal rain. Coronal rain occurs when plasma trapped in coronal magnetic loops undergoes a thermal instability. Under certain conditions, the delicate balance between heating and radiative cooling in the corona breaks down, and localised pockets of plasma cool catastrophically from millions of kelvin to chromospheric temperatures in a matter of minutes. The resulting condensations become dense and heavy, lose magnetic support, and plummet along the curved field lines of the hosting loop system, often appearing as shimmering showers of dark blobs in H-alpha and other cool-line observations. Coronal rain is now recognised as a ubiquitous phenomenon in the solar corona, and numerical simulations with modern magnetohydrodynamic codes have reproduced its formation in striking detail, including its connection to cycles of heating and cooling in coronal loops.

A crucial clue linking the falling plasma to the hot corona came from the Atmospheric Imaging Assembly, AIA, aboard NASA’s Solar Dynamics Observatory. In AIA’s hot extreme-ultraviolet channels, which sample plasma at temperatures of a million kelvin and above, the team identified a coronal bright point at exactly the same location as the supersonic downflow, with the temperature there reaching approximately 6 million kelvin. Coronal bright points are small, compact regions of enhanced emission associated with magnetic bipoles and frequent small-scale reconnection events. The spatial coincidence between a 6-million-kelvin bright point and a stream of 10,000-kelvin plasma falling at 90 kilometres per second suggests a physical connection: heating and reconnection at the bright point may destabilise nearby coronal plasma, triggering the thermal condensation that then rains down, or alternatively may inject material that subsequently follows a ballistic descent along the filament’s magnetic architecture.

The THEMIS spectra also confirmed a phenomenon that has fascinated solar physicists for decades: counter-streaming flows. Along the fine internal strands of the filament, which the high-resolution observations resolved to widths of less than one arcsecond, plasma was observed flowing in opposite directions simultaneously. Counter-streaming was first documented in prominences in the late 1990s and has since been attributed to various mechanisms, including intermittent heating events at the filament’s footpoints, waves propagating along the strands, and networks of small-scale jets from the chromosphere below. The fact that THEMIS measured these bidirectional flows in individual sub-arcsecond strands, using both imaging and spectroscopy, provides some of the most direct evidence yet that the filament’s fine structure is a dynamically active web of plasma conduits rather than a static reservoir of gas.

What makes this study compelling is the way it stitches together observations across an enormous range of scales and temperatures. A single arcsecond-scale blob of cool plasma, falling at nine times the sound speed, is connected to a million-degree bright point, to repetitive flows threading a filament tens of thousands of kilometres long, and to the grand question of how the Sun’s atmosphere cycles mass between its coolest and hottest layers. Whether the supersonic downflow proves to be ballistic drainage or coronal rain, the measurement demonstrates that quiescent filaments are anything but quiet: they are sites of continuous, violent mass transport that ground-based spectrometers of the THEMIS class are now capable of quantifying with exquisite precision. As solar physicists refine the interplay between thermal instability, magnetic topology and reconnection in these structures, each such observation brings the field closer to understanding not only the life cycle of filaments themselves but also the precursors of the eruptions that can send space weather hurtling toward Earth.

Subject of Research: Supersonic downflows in a quiescent solar filament observed with THEMIS and their relation to a coronal bright point

Article Title: Supersonic Flows Observed by THEMIS Related to a Coronal Bright Point and Filament

Article References: Karki, G., Schmieder, B., Chandra, R., Devi, P., Démoulin, P., & Poedts, S. (2026). Supersonic Flows Observed by THEMIS Related to a Coronal Bright Point and Filament. Solar Physics, 301(9), Article 148. https://doi.org/10.1007/s11207-026-02744-9

Image Credits: AI Generated

DOI: 10.1007/s11207-026-02744-9

Keywords: solar physics, solar filament, prominence, THEMIS, coronal bright point, coronal rain, H-alpha spectroscopy, cloud model, counter-streaming flows, SDO/AIA, GONG, supersonic downflows

The research, led by Ying Li of Kunming University of Science and Technology in Yunnan, China, together with colleagues at the same institution and at Yunnan Observatories of the Chinese Academy of Sciences, introduces a tracking architecture called BF-TrackFormer. The name reflects its core innovation: a bidirectional, autoregressive approach to tracking that works both forward and backward in time. The team built the system specifically to address a problem that has long frustrated automated filament monitoring, namely that filaments do not behave like tidy, well-behaved objects. They split, merge, twist, and fragment, and conventional tracking algorithms routinely lose track of them when their outlines break apart or when a detector momentarily fails to recognize them.

To train and evaluate the new method, the researchers assembled a dataset of 630 H-alpha full-disk solar images drawn from three distinct observational sources: the Big Bear Solar Observatory in California, the Solar Magnetic Activity Research Telescope operated by Kyoto University in Japan, and the Global Oscillation Network Group, a worldwide network of six stations operated by the U.S. National Solar Observatory that provides nearly continuous coverage of the Sun. The dataset was divided into one training set and five testing sets, a deliberate design choice meant to expose the algorithm to different data sources, time intervals, and observing conditions. This diversity matters because H-alpha imagery varies considerably from one instrument to another in resolution, calibration, and image quality, and a tracking system that only works on data resembling its training set would be of limited practical value for operational forecasting centers.

The technical foundation of BF-TrackFormer builds on TrackFormer, a transformer-based multi-object tracking framework originally developed for computer vision applications. Transformers, the same architectural family that powers modern language models, process images by attending to relationships between different regions of the frame, allowing a detector to identify objects and assign them consistent identities across successive frames in a single pass. The Chinese team adapted this paradigm to the peculiar challenges of solar imagery, where filaments appear as dark absorption features against the bright chromosphere and where their boundaries can be diffuse, elongated, and highly irregular compared with the compact objects typical of pedestrian or vehicle tracking benchmarks.

The genuinely novel component is the backward autoregressive branch, which consists of two modules the authors call the ID Embedding Head and the Recheck Network. In a standard forward-only tracker, each new frame is processed in sequence, and objects are matched to tracks established in previous frames. If a filament splits into two pieces, the detector may capture only one fragment, or if a filament temporarily fades below the detection threshold, the track is broken and the object reappears later as a brand-new entity with a new identity. The backward branch effectively gives the algorithm a second chance: it revisits frames where objects were missed or misclassified as background, re-embedding identity information so that fragments of a split filament can be reassociated with their parent track, and discontinuous segments of a single filament’s lifecycle can be stitched back together into one coherent trajectory.

This capability is scientifically significant because fragmented filament evolution is not merely a nuisance for software; it is a physical signal. Observational studies in recent years have documented that filament splitting, bifurcation, and reconstruction are often driven by magnetic reconnection and the emergence of new magnetic flux, processes that can precede or accompany eruption. A filament that breaks apart in H-alpha images may be undergoing the very magnetic restructuring that signals instability. If an automated system fragments a single physical filament into several spurious tracks, or loses it entirely at the critical moment, the observational clues that forecasters and researchers rely on are corrupted. By maintaining identity through splitting events, BF-TrackFormer preserves the integrity of the lifecycle record, which is precisely what statistical studies of filament behavior and eruption prediction models require as input.

The performance figures reported in the paper illustrate both the promise and the remaining difficulty of the task. Averaged across the five testing sets, the method achieved an IDF1 score of 70.1 percent, a measure of how consistently identities are maintained over time; a MOTA of 46.6 percent, the standard multi-object tracking accuracy metric that combines false positives, misses, and identity switches; precision of 75.1 percent; recall of 70.9 percent; and an identity survival rate of 10.1 percent. The authors note a clear trend: as the time interval between consecutive images decreases, all of these metrics improve. This makes intuitive sense, since filaments evolve slowly by human forecasting standards but can change enough between widely spaced frames to confuse any tracker, and denser temporal sampling gives the autoregressive machinery more reliable continuity to work with.

The relatively low identity survival rate underscores how hard the problem remains. Filaments can persist on the Sun for days or weeks, and tracking one across its full lifetime requires surviving changing illumination, projection effects as solar rotation carries features across the disk, overlapping structures, and the episodic fragmentation that motivated the study in the first place. Even so, the authors report that BF-TrackFormer performs particularly well on fragmented filaments, the case that conventional methods handle worst. In the ecosystem of automated solar monitoring, where earlier systems relied on classical image processing techniques such as morphological operations, region growing, and hand-tuned thresholds, the shift to learned, identity-aware tracking represents a generational change in how filament catalogs can be built.

The broader context is the growing recognition that space weather is an operational hazard demanding the same vigilance as terrestrial weather. Filament eruptions are closely associated with coronal mass ejections, the massive expulsions of plasma and magnetic field that are the primary drivers of major geomagnetic storms. Statistical studies spanning multiple solar cycles have established that a large fraction of filament disappearances coincide with coronal mass ejections, making filaments one of the most informative precursors available to forecasters. Automated detection and tracking systems, running continuously on ground-based H-alpha networks, can in principle flag filament activation, disappearance, and fragmentation in near real time, extending the reach of human observers who cannot watch every active region simultaneously.

The authors acknowledge the contributions of the observatories whose data made the work possible, including Big Bear Solar Observatory, the Global Oscillation Network Group, the Solar Magnetic Activity Research Telescope, and the CHASE satellite mission supported by the China National Space Administration. As solar maximum approaches its most active phases and humanity’s dependence on space-based infrastructure deepens, tools like BF-TrackFormer point toward a future in which the Sun’s most dangerous structures are watched continuously, their every split and twist logged by algorithms that never blink, and their eruptive potential assessed with the statistical rigor that only complete, unbroken lifecycle records can provide. The research is a reminder that forecasting the storm begins with faithfully following the calm before it.

Subject of Research: A bidirectional autoregressive deep learning method for detecting and tracking solar filaments across their lifecycles to improve space weather forecasting.

Article Title: Bidirectional Autoregressive Tracking Method for Solar Filaments

Article References: Li, Y., Yang, Y., Zhang, X., Feng, S., Dai, W., Liang, B., & Xiong, J. (2026). Bidirectional Autoregressive Tracking Method for Solar Filaments. Solar Physics, 301(9), Article 136. https://doi.org/10.1007/s11207-026-02737-8

Image Credits: AI Generated

DOI: 10.1007/s11207-026-02737-8

Keywords: solar filaments, space weather, deep learning, multi-object tracking, H-alpha imaging, coronal mass ejections, BF-TrackFormer, solar physics, transformers, Big Bear Solar Observatory, GONG, filament eruptions