Spectral Variability Research Articles

SUSHI: An algorithm for source separation of hyperspectral images with non-stationary spectral variation

Context. Hyperspectral images are data cubes with two spatial dimensions and a third spectral dimension, providing a spectrum for each pixel, and thus allowing the mapping of extended sources’ physical properties. Aims. In this article, we present the Semi-blind Unmixing with Sparsity for Hyperspectral Images (SUSHI), an algorithm for non-stationary unmixing of hyperspectral images with spatial regularization of spectral parameters. The method allows for the disentangling of physical components without the assumption of a unique spectrum for each component. Thus, unlike most source separation methods used in astrophysics, all physical components obtained by SUSHI vary in spectral shape and in amplitude across the data cube. Methods. Non-stationary source separation is an ill-posed inverse problem that needs to be constrained. We achieve this by training a spectral model and applying a spatial regularization constraint on its parameters. For the spectral model, we used an Interpolatory Auto-Encoder, a generative model that can be trained with limited samples. For spatial regularization, we applied a sparsity constraint on the wavelet transform of the model parameter maps. Results. We applied SUSHI to a toy model meant to resemble supernova remnants in X-ray astrophysics, though the method may be used on any extended source with any hyperspectral instrument. We compared this result to the one obtained by a classic 1D fit on each individual pixel. We find that SUSHI obtains more accurate results, particularly when it comes to reconstructing physical parameters. We then applied SUSHI to real X-ray data from the supernova remnant Cassiopeia A and to the Crab Nebula. The results obtained are realistic and in accordance with past findings but have a much better spatial resolution. Thanks to spatial regularization, SUSHI can obtain reliable physical parameters at fine scales that are out of reach for pixel-by-pixel methods.

Long Term X-Ray Spectral Variations of the Seyfert-1 Galaxy Mrk 279

We present the results from a long term X-ray analysis of Mrk 279 during the period 2018–2020. We use data from multiple missions – AstroSat, NuSTAR and XMM-Newton, for the purpose. The X-ray spectrum can be modeled as a double Comptonization along with the presence of neutral Fe Kα line emission, at all epochs. We determined the source’s X-ray flux and luminosity at these different epochs. We find significant variations in the source’s flux state. We also investigate the variations in the source’s spectral components during the observation period. We find that the photon index and hence the spectral shape follow the variations only over longer time periods. We probe the correlations between fluxes of different bands and their photon indices, and found no significant correlations between the parameters.

An Informed and Systematic Method to Identify Variable Mid-L Dwarfs

Most brown dwarfs show some level of photometric or spectral variability. However, finding the most variable dwarfs more suited for a thorough variability monitoring campaign remained a challenge until a few years ago with the design of spectral indices to find the most likely L and T dwarfs using their near-infrared (NIR) single-epoch spectrum. In this work, we designed and tested NIR spectral indices to preselect the most likely variable L4–L8 dwarfs, complementing the indices presented by Ashraf et al. and Oliveros-Gomez et al. We used time-resolved NIR Hubble Space Telescope Wide Field Camera 3 spectra of an L6.0 dwarf, LP 261–75b, to design our novel spectral indices. We tested these spectral indices on 75 L4.0–L8.0 NIR SpeX/IRTF spectra, providing 27 new variable candidates. Our indices have a recovery rate of ∼80% and a false negative rate of ∼25%. All the known nonvariable brown dwarfs were found to be nonvariable by our indices. We estimated the variability fraction of our sample to be 51−38+4 %, which agrees with the variability fractions provided by Buenzli et al., Radigan et al., and Metchev et al. for L4–L8 dwarfs. These spectral indices may support the future selection of the most likely variable directly imaged exoplanets for studies with the James Webb Space Telescope and as well as the 30 m telescopes.

Spectral consistency in sound sequence affects perceptual accuracy in discriminating subdivided rhythmic patterns.

Musical compositions are distinguished by their unique rhythmic patterns, determined by subtle differences in how regular beats are subdivided. Precise perception of these subdivisions is essential for discerning nuances in rhythmic patterns. While musical rhythm typically comprises sound elements with a variety of timbres or spectral cues, the impact of such spectral variations on the perception of rhythmic patterns remains unclear. Here, we show that consistency in spectral cues affects perceptual accuracy in discriminating subdivided rhythmic patterns. We conducted online experiments using rhythmic sound sequences consisting of band-passed noise bursts to measure discrimination accuracy. Participants were asked to discriminate between a swing-like rhythm sequence, characterized by a 2:1 interval ratio, and its more or less exaggerated version. This task was also performed under two additional rhythm conditions: inversed-swing rhythm (1:2 ratio) and regular subdivision (1:1 ratio). The center frequency of the band noises was either held constant or alternated between two values. Our results revealed a significant decrease in discrimination accuracy when the center frequency was alternated, irrespective of the rhythm ratio condition. This suggests that rhythm perception is shaped by temporal structure and affected by spectral properties.

Probabilistic generation of hazard‐consistent suites of fully non‐stationary seismic records

AbstractA novel, practical, and computationally efficient probabilistic methodology for the stochastic generation of suites of fully non‐stationary artificial accelerograms is presented. The proposed methodology ensures that the produced ground motion suites match a given target spectral mean and target variability for the whole period range of interest. This is achieved by first producing an ensemble of random target spectra with the given mean and variability and then using them to generate artificial, target spectrum‐compatible, acceleration time‐histories with spectral representation techniques. Spectral correlation can also be assumed for the generated ground motion spectra. Based on the same backbone, two different formulations are proposed for generating spectrum‐compatible acceleration time‐histories of the non‐stationary kind. The distinction between these two variants lies in the techniques employed for modeling the temporal and spectral modulation, focusing on the site‐compatibility of the produced records. The first approach uses past‐recorded seismic accelerograms as seed records, and the second proposes and uses a new, probabilistic time‐frequency modulating function. The outcome of the proposed methodology is suites containing site‐compatible ground motion time‐histories whose spectral mean and variability match those obtained from any of the usually employed target spectra used in the earthquake engineering practice. An online tool implementing the proposed methodology is also freely provided.

Monitoring Long-Term Land Cover Change in Central Yakutia Using Sparse Time Series Landsat Data

Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s.

Bounds for the Relative and Absolute Spectral Variations of Matrices

Bounds for the Relative and Absolute Spectral Variations of Matrices

Molecular Mechanisms behind Circular Dichroism Spectral Variations between Channelrhodopsin and Heliorhodopsin Dimers.

Channelrhodopsin (ChR) and heliorhodopsin (HeR) are microbial rhodopsins with similar structures but different circular dichroism (CD) spectra: ChR shows biphasic negative and positive bands, whereas HeR shows a single positive band. We explored the physicochemical factors underlying these differences through computational methods. Using the exciton model based on first-principles computations, we obtained the CD spectra of ChR and HeR. The obtained spectra indicate that the protein dimer structures and the quantum mechanical treatment of the retinal chromophore and its interacting amino acids are crucial for accurately reproducing the experimental spectra. Further calculations revealed that the sign of the excitonic coupling was opposite between the ChR and HeR dimers, which was attributed to the contrasting second term of the orientation factor between the two retinal chromophores. These findings demonstrate that slight variations in the intermolecular orientation of the two chromophores can result in significant differences in the CD spectral shape.

Constraining Free–Free Emission and Photoevaporative Mass-loss Rates for Known Proplyds and New VLA–identified Candidate Proplyds in NGC 1977

We present NSF's Karl G. Jansky Very Large Array observations covering the NGC 1977 region at 3.0, 6.4, and 15.0 GHz. We search for compact radio sources and detect continuum emission from 34 NGC 1977 cluster members and 37 background objects. Of the 34 radio-detected cluster members, 3 are associated with known proplyds in NGC 1977, 22 are associated with additional young stellar objects in NGC 1977, and 9 are newly identified cluster members. We examine the radio spectral energy distributions, circular polarization, and variability of the detected NGC 1977 sources and identify 10 new candidate proplyds whose radio fluxes are dominated by optically thin free–free emission. We use measurements of free–free emission to calculate the mass-loss rates of known proplyds and new candidate proplyds in NGC 1977, and find values ∼10−9 to 10−8 M ⊙ yr−1, which are lower than the mass-loss rates measured toward proplyds in the Orion Nebula Cluster but consistent with the mass-loss rates predicted by external photoevaporation models for spatially extended disks that are irradiated by the typical external ultraviolet (UV) fields encountered in NGC 1977. Finally, we show that photoevaporative disk winds in NGC 1977 may be illuminated by internal or external sources of ionization, depending on their positions within the cluster. This study provides new constraints on disk properties in a clustered star-forming region with a weaker UV environment than the Orion Nebula Cluster but a stronger UV environment than low-mass star-forming regions like Taurus. Such intermediate UV environments represent the typical conditions of Galactic star and planet formation.

Мultivariate analysis for rapid screening and prediction of solid-state compatibility in pharmaceutical preformulation studies-paving the road for machine learning

Multivariate analysis models were developed to evaluate the results obtained from a compatibility study designed for ibuprofen with a large group of different types of excipients, as a possible approach for rapid screening of the incompatibility between the active pharmaceutical ingredient (API) and excipients. The solid-state characterization of the binary mixtures was performed by Fourier transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC). Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) using SIMCA® software were applied for evaluation of the experimentally obtained results. The optimal PCA model for the FTIR spectra explains 96.2 % of the variations in the dataset with good statistical indicators (R2X = 0.960, Q2 = 0.900), which was also the case for the PCA model for the DSC curves (R2X = 0.981, Q2 = 0.866). The applied PLS-DA models have shown similar behaviour to the PCA. Moreover, the main spectral variations in the FTIR spectra and the thermal events in the DSC data were attributed the highest variable importance for the projection (VIP) scores in the corresponding VIP plots, confirming the model capability for predicting ibuprofen interactions. Furthermore, the prediction power of the optimal models for FTIR and DSC experimental data was evaluated by the root mean square error of prediction (RMSEP) of 0.10 and 0.16, respectively. The obtained results demonstrated the potential of multivariate statistical analysis as a machine learning-based technique for screening and prediction of ibuprofen-excipients solid-state compatibility in the preformulation phase of the pharmaceutical development of dosage forms.

Using Artificial Intelligence to Improve the Accuracy of a Wrist-Worn, Noninvasive Glucose Monitor: A Pilot Study.

Self-monitoring of glucose is important to the successful management of diabetes; however, existing monitoring methods require a degree of invasive measurement which can be unpleasant for users. This study investigates the accuracy of a noninvasive glucose monitoring system that analyses spectral variations in microwave signals. An open-label, pilot design study was conducted with four cohorts (N = 5/cohort). In each session, a dial-resonating sensor (DRS) attached to the wrist automatically collected data every 60 seconds, with a novel artificial intelligence (AI) model converting signal resonance output to a glucose prediction. Plasma glucose was measured in venous blood samples every 5 minutes for Cohorts 1 to 3 and every 10 minutes for Cohort 4. Accuracy was evaluated by calculating the mean absolute relative difference (MARD) between the DRS and plasma glucose values. Accurate plasma glucose predictions were obtained across all four cohorts using a random sampling procedure applied to the full four-cohort data set, with an average MARD of 10.3%. A statistical analysis demonstrates the quality of these predictions, with a surveillance error grid (SEG) plot indicating no data pairs falling into the high-risk zones. These findings show that MARD values approaching accuracies comparable to current commercial alternatives can be obtained from a multiparticipant pilot study with the application of AI. Microwave biosensors and AI models show promise for improving the accuracy and convenience of glucose monitoring systems for people with diabetes.

Ball type (4-(tert-butyl)-1,2-phenylene)dimethanolate bridged dinuclear metallo phthalocyanines: Synthesis, characterization, electrochemistry, and in situ spectroelectrochemistry

In this research, cofacial ball type metallo phthalocyanines (MPcs) with a longer bridge, which by adding a –CH2 group to the oxo bridge, were obtained by working the 4,4′-(((4-(tert-butyl)-1,2-phenylene)bis(methylene))bis(oxy))diphthalonitrile beginning complex. Molecular characterizations of the starting compound and dinuclear ball-type MPcs (Co, Fe, Mn, Zn, Ni) obtained from it were made using elemental analysis, 1H-NMR, 13C-NMR, FT-IR, UV–Vis, and MALDI-TOF mass spectroscopy. In addition, how the macromolecule’s spectral, electrochemical, in situ spectroelectrochemical, and electrochromic properties changed was investigated when the phthalocyanine (Pc) rings moved away from each other due to the increasing linking bridge length. The electrochemical properties of ball-type Pcs were investigated via Cyclic Voltammetry (CV) and Square Wave Voltammetry (SWV) in the non-aqueous solvent system on a Pt working electrode (WE). In situ spectroelectrochemical measurements also were carried out to support the electrochemical measurements. In situ electrocolorimetric techniques revealed color changes relevant to the redox processes. Electrochemical and spectroelectrochemical measurements showed that ball type metallo-Pcs form steadfast mixed-valence species based on metal and/or Pc ring on account of the fact that there are forceful and striking intramolecular interactions between the two Pc segments. As a result of the cofacial molecular structure of ball type metallo-Pcs and the split molecular orbitals, the complexes have rich redox properties. They can be utilized as a material in electrochemical technologies since they have plentiful redox behaviors. In situ spectroelectrochemical and in situ electrocolorimetric measurements demonstrated that they also have rich spectral and chromatic features. Due to the apparent color and spectral variations, they are appropriate for electrochromic appliances.

Near-infrared imaging of heat transfer behavior between gadolinium and fluid during magnetization/demagnetization process of magnetocaloric effect

This paper reports on the application of a near-infrared (NIR) imaging system for visualizing heat transfer dynamics from a bulk gadolinium (Gd) sample to the surrounding water during the magnetization/demagnetization process of the magnetocaloric effect (MCE). The suggested approach relied on the spectral variation in water absorption band at 1150 nm wavelength within the NIR spectrum. An experimental setup integrated a telecentric uniform-illumination system, a halogen lamp, and an NIR camera to enable real-time monitoring of a single magnetization and demagnetization cycle induced by an external magnetic field, which was generated by a permanent-magnet-based magnetic circuit. Two-dimensional absorbance images captured during this cycle clearly depicted the thermal energy generated by the MCE in water. Furthermore, an analysis of the thermal boundary layer and the quantification of heat transfer from Gd to water provided insights into the dynamics over time. These results indicated the potential of our NIR imaging techniques in optimizing thermal–fluid interactions within MCE systems, thereby improving the design and efficiency of magnetic refrigeration systems.

Enhancing Semi-Supervised Few-Shot Hyperspectral Image Classification via Progressive Sample Selection

Hyperspectral images (HSIs) provide valuable spatial–spectral information for ground analysis. However, in few-shot (FS) scenarios, the limited availability of training samples poses significant challenges in capturing the sample distribution under diverse environmental conditions. Semi-supervised learning has shown promise in exploring the distribution of unlabeled samples through pseudo-labels. Nonetheless, FS HSI classification encounters the issue of high intra-class spectral variability and inter-class spectral similarity, which often lead to the diffusion of unreliable pseudo-labels during the iterative process. In this paper, we propose a simple yet effective progressive pseudo-label selection strategy that leverages the spatial–spectral consistency of HSI pixel samples. By leveraging spatially aligned ground materials as connected regions with the same semantic and similar spectrum, pseudo-labeled samples were selected based on round-wise confidence scores. Samples within both spatially and semantically connected regions of FS samples were assigned pseudo-labels and joined subsequent training rounds. Moreover, considering the spatial positions of FS samples that may appear in diverse patterns, to fully utilize unlabeled samples that fall outside the neighborhood of FS samples but still belong to certain connected regions, we designed a matching active learning approach for expert annotation based on the temporal confidence difference. We identified samples with the highest training value in specific regions, utilizing the consistency between predictive labels and expert labels to decide whether to include the region or the sample itself in the subsequent semi-supervised iteration. Experiments on both classic and more recent HSI datasets demonstrated that the proposed base model achieved SOTA performance even with extremely rare labeled samples. Moreover, the extended version with active learning further enhances performance by involving limited additional annotation.

Spectral Spatial Neighborhood Attention Transformer for Hyperspectral Image Classification

Hyperspectral image (HSI) can provide rich spectral information, which can be helpful for accurate classification in many applications. However, the hyperspectral image classification task has challenges, including limited labeled data, data redundancy, data sparsity, and imbalanced class samples. Over time, various methods have been proposed to solve the above-mentioned problems. To mitigate the issue mentioned above in this paper, we present the neighborhood attention transformer with a channel-wise shift technique for hyperspectral image classification. The neighborhood attention transformer leverages the power of the attention mechanism to capture the spatial relationship between neighboring pixels and extract discriminative features. The channel-wise shift techniques empower the model to adjust the spectral characteristics of each channel adaptively, enhancing its ability to handle the spectral variations present in hyperspectral data. To validate the effectiveness of the proposed model, we conduct comprehensive experiments on the publically available dataset. The results demonstrate that our model consistently outperforms other state-of-the-art methods. The overall accuracy of the proposed model reached 99.41 % , 99.93 % , 98.35 % 99.59 % four datasets.

Exploring the spectral variation hypothesis for α- and β-diversity: a comparison of open vegetation and forests

Airborne hyperspectral imaging holds great promise for estimating plant diversity and composition, given its unprecedented combination of aerial coverage, spatial resolution, and spectral detail. Recently, there has been renewed attention toward the spectral variation hypothesis (SVH), which predicts that higher spectral variation is correlated with greater plant diversity. While several studies have highlighted methodological challenges involved with the SVH, there is little consensus about when it yields strong predictions of taxonomic, functional, and phylogenetic diversity. In part, this may be because prior studies have not explicitly considered how underlying environmental gradients drive changes in spectral and species composition. In this study, we tested the SVH separately in open vegetation (i.e. grasses and shrubs) and in forests at five sites across Canada. Generalized additive models revealed that spectral diversity was a better predictor of functional α-diversity than of taxonomic or phylogenetic α-diversity in both vegetation types. Mantel tests and Procrustes analyses revealed weak to moderate associations between spectral and plant β-diversity and composition in open vegetation, and moderate associations in forests. The better fit in forests appeared to be influenced by the presence of an elevational gradient and associated species turnover (from deciduous to coniferous trees); we observed weaker relationships when examining only a subset of this gradient. We suggest that the high variability in the strength of associations between plant and spectral diversity reported to date might be affected by the presence of environmental gradients. Finally, we found that different wavelength bands contributed to spectral α-diversity in open vegetation vs. forests, suggesting different spectral features are important for different vegetation types. In conclusion, spectral diversity is a potentially powerful tool for biodiversity assessment, but it requires a context-specific approach.

High-resolution soil erosion mapping in croplands via Sentinel-2 bare soil imaging and a two-step classification approach

Erosion-induced lateral soil redistribution leads to spatially heterogenous soil composition, which can be captured through the distinctive spectral reflectance of soils under varying levels of erosion influence. This points to the potential of using remotely sensed soil spectra to detect severe erosion and deposition hotspots in exposed croplands and, importantly, further differentiate the intra-class spectral variability of moderate erosion that often occupies the largest proportion. Here, we aim to develop a two-step erosion classification and mapping approach based on multitemporal compositing of Sentinel-2 bare soil images of a typical agricultural region (11,500 km2) of northeast China. A random forest classifier was firstly trained against the ground-truth data derived from very high resolution (VHR) imagery in Google Earth, with an overall accuracy of 91 % that allowed for clear delineation of severe erosion and deposition areas based on their distinct topographic and spectral features particularly in the red and red-edge bands. In the second step, the remaining area of moderate erosion (60.30 %) was further differentiated using Iterative Self-Organizing cluster unsupervised classification to yield a five-class soil erosion map at 10 m spatial resolution. The accuracy of the predicted map was successfully validated by independent Caesium-137 (137Cs) and soil organic carbon observations at catchment and regional scales, as revealed by significant inter-class differences in soil redistribution rates estimated from 137Cs inventory. The severe erosion class had a soil loss rate of 5.5 mm yr−1, suggesting that previous assessments have underestimated erosion severity. The spatial accordance of crop growth with soil erosion intensity, particularly in localized settings, further highlighted the potential of bare soil imaging for mapping the spatiotemporal development of soil erosion and its response to targeted sustainable cropland management efforts.

Linking aerial hyperspectral data to canopy tree biodiversity: An examination of the spectral variation hypothesis

AbstractImaging spectroscopy is emerging as a leading remote sensing method for quantifying plant biodiversity. The spectral variation hypothesis predicts that variation in plant hyperspectral reflectance is related to variation in taxonomic and functional identity. While most studies report some correlation between spectral and field‐based (i.e., taxonomic and functional) expressions of biodiversity, the observed strength of association is highly variable, and the utility in applying spectral community properties to examine environmental drivers of communities remains unknown. We linked hyperspectral data acquired by airborne imaging spectrometers with precisely geolocated field plots to examine the spectral variation hypothesis along a temperate‐to‐boreal forest gradient in southern Québec, Canada. First, we examine the degree of association between spectral and field‐based dimensions of canopy tree composition and diversity. Second, we ask whether the relationships between field‐based community properties and the environment are reproduced when using spectral community properties. We found support for the spectral variation hypothesis with the strength of association generally greater for the functional than taxonomic dimension, but the strength of relationships was highly variable and dependent on the choice of method or metric used to quantify spectral and field‐based community properties. Using a multivariate approach (comparisons of separate ordinations), spectral composition was moderately well correlated with field‐based composition; however, the degree of association increased when univariately relating the main axes of compositional variation. Spectral diversity was most tightly associated with functional diversity metrics that quantify functional richness and divergence. For predicting canopy tree composition and diversity using environmental variables, the same qualitative conclusions emerge when hyperspectral or field‐based data are used. Spatial patterns of canopy tree community properties were strongly related to the turnover from temperate‐to‐boreal communities, with most variation explained by elevation. Spectral composition and diversity provide a straightforward way to quantify plant biodiversity across large spatial extents without the need for a priori field observations. While commonly framed as a potential tool for biodiversity monitoring, we show that spectral community properties can be applied more widely to assess the environmental drivers of biodiversity, thereby helping to advance our understanding of the drivers of biogeographical patterns of plant communities.

Spectral variations within solar flare ribbons

Context. Solar flare ribbons are intense brightenings of primarily chromospheric material that are responsible for a large fraction of the chromospheric emission in solar and stellar flares. We present an on-disc observation of flare ribbon substructures in an X9.3-class flare observed by the Swedish 1-m Solar Telescope. Aims. We aim to identify categories of ribbon substructures seen in the Ca II 8542 Å, Hα, and Ca II K lines, focusing on their spatial locations and their (spectro-)polarimetric properties. Methods. We used COlor COllapsed Plotting (COCOPLOT) software to assist in identifying areas of interest. Results. We present five categories of spectral profiles within the general body of the flare ribbon: (1) extremely broadened spectral line profiles, where the standard Fabry–Perot interferometer wavelength windows (≈70 km s−1) are not sufficiently wide to allow for a complete analysis of the dynamics and atmospheric conditions. The mechanisms causing this degree of this broadening are not yet clearly understood; (2) long-lived, dense kernels that manifest as more saturated chromospheric line profiles with lower signal in both Stokes parameters. They are interpreted as footpoints of bunched magnetic field loops, whose chromospheric lines form at greater heights than the nearby areas; (3) Doppler-shifted leading edges of the flare ribbon in regions that transiently display lower Stokes signals due to the emission dominating at greater heights in the atmosphere; (4) condensed coronal rain overlapping the flare ribbons in the line of sight, producing exceptionally high Doppler shifts near the footpoints; and (5) compact blueshifted areas close to areas with coronal rain down-flows, which are understood to be material that has been thrown up as a result of the down-flowing material impacting the chromosphere. Additionally, a ribbon formation height of about 700 km with respect to penumbral features is estimated using correlating structures on the ribbon and the underlying photosphere. Conclusions. When selecting areas of the flare ribbon for more general analysis (especially small regions consisting of a few pixels or low-resolution averages), it is important to be aware of the variety of substructures present within a flare ribbon and of the spatial context that can produce these differences. General behaviors across the ribbon should not be inferred from regions that show localized differences.

PDRs4All

Context. Extraordinary observations of the Orion Bar by JWST have shown, for the first time, the incredible richness of polycyclic aromatic hydrocarbon (PAH) emission bands and their variation on very small scales. These variations are the result of photochemical evolution of the PAH carrier. Aims. We aim to probe the photochemical evolution of PAHs across the key zones of the ideal photodissociation region (PDR) that is the Orion Bar using unsupervised machine learning. Methods. We used JWST NIRSpec IFU and MIRI MRS observations of the Orion Bar from the JWST Early Release Science programme PDRs4All (ID: 1288). We levered bisecting k-means clustering to generate highly detailed spatial maps of the spectral variability in the 3.2–3.6, 5.95–6.6, 7.25–8.95, and 10.9–11.63 μm wavelength regions. We analysed and subsequently described the variations in the cluster profiles and connected them to the conditions of the physical locations from which they arise. We interpreted the origin of the observed variations with respect to the following key zones: the H II region, the atomic PDR zone, and the layers of the molecular PDR zone stratified by the first, second, and third dissociation fronts (DF 1, DF 2, and DF 3, respectively). Results. Observed PAH emission exhibits spectral variation that is highly dependent on the spatial position in the PDR. We find the 8.6 μm band to behave differently than all other bands, which vary systematically with one another. Notably, we find a uniform variation in the 3.4–3.6 μm bands and 3.4/3.3 intensity ratio. We attribute the carrier of the 3.4–3.6 μm bands to a single side group attached to very similarly sized PAHs. Further, cluster profiles reveal a transition between characteristic profile classes of the 11.2 μm feature from the atomic to the molecular PDR zones. We find the carriers of each of the profile classes to be independent, and reason the latter to be PAH clusters existing solely deep in the molecular PDR. Clustering also reveals a connection between the 11 .2 and 6.2 μm bands and that clusters generated from variation in the 10.9–11.63 μm region can be used to recover those in the 5.95–6.6 μm region. Conclusions. Clustering is a powerful and comprehensive tool for characterising PAH spectral variability on both spatial and spectral scales. For individual bands as well as global spectral behaviours, we find ultraviolet processing to be the most important driver of the evolution of PAHs and their spectral signatures in the Orion Bar PDR.