Wide Range Of Scales Research Articles

A new high-order shock-capturing TENO scheme combined with skew-symmetric-splitting method for compressible gas dynamics and turbulence simulation

The high-order shock-capturing scheme is one of the main building blocks for the simulation of the compressible fluid characterized by strong shockwaves and broadband length scales. However, the classical shock-capturing scheme fails to perform long-time stable and non-dissipative simulations since the quadratic invariants associated with the conservation equations cannot be conserved as a result of the inherent numerical dissipation. Additionally, the overall computational cost for classical shock-capturing schemes is quite expensive as a result of the time-consuming local characteristic decomposition and the nonlinear-weights computing process. In this work, based on a new efficient discontinuity indicator, which distinguishes the non-smooth high-wavenumber fluctuations and discontinuities from smooth scales in the wavenumber space, a paradigm of high-order shock-capturing scheme by recasting the non-dissipative skew-symmetric-splitting method with newly optimized dispersion property for smooth flow scales and invoking the nonlinear targeted ENO (TENO) schemes for non-smooth discontinuities is proposed. The resulting TENO-S scheme not only successfully performs long-time stable computations for smooth flows without numerical dissipation, but also recovers the robust shock-capturing capabilities with adaptive numerical dissipation. Without the necessity of parameter tuning case by case, extensive benchmark simulations involving a wide range of flow length scales and strong discontinuities demonstrate that the proposed TENO-S scheme performs significantly better than the straightforward deployment of WENO/TENO-family schemes with better spectral property and higher computational efficiency.

Effects of Pressure and Characteristic Scales on the Structural and Statistical Features of Methane/Air Turbulent Premixed Flames

In this study, the highly nonlinear and multi-scale flame-turbulence interactions prevalent in turbulent premixed flames are examined by using direct numerical simulation (DNS) datasets to understand the effects of increase in pressure and changes in the characteristic scale ratios at high pressure. Such flames are characterized by length-scale ratio (ratio of integral length scale and laminar thermal flame thickness) and velocity-scale ratio (ratio of turbulence intensity and laminar flame speed). A canonical test configuration corresponding to an initially laminar methane/air lean premixed flame interacting with decaying isotropic turbulence is considered. We consider five cases with the initial Karlovitz number of 18, 37, 126, and 260 to examine the effects of an increase in pressure from 1 to 10 atm with fixed turbulence characteristics and at a fixed Karlovitz number, and the changes to characteristic scale ratios at the pressure of 10 atm. The increase in pressure for fixed turbulence characteristics leads to enhanced flame broadening and wrinkling due to an increase in the range of energetic scales of motion. This further manifests into affecting the spatial and state-space variation of thermo-chemical quantities, single point statistics, and the relationship of heat-release rate to the flame curvature and tangential strain rate. Although these results can be inferred in terms of an increase in Karlovitz number, the effect of an increase in pressure at a fixed Karlovitz number shows differences in the spatial and state-space variations of thermo-chemical quantities and the relationship of the heat release rate with the curvature and tangential strain rate. This is due to a higher turbulent kinetic energy associated with the wide range of scales of motion at atmospheric pressure. In particular, the magnitude of the correlation of the heat release rate with the curvature and the tangential strain rate tend to decrease and increase, respectively, with an increase in pressure. Furthermore, the statistics of the flame-turbulence interactions at high pressure also show sensitivity to the changes in the characteristic length- and velocity-scale ratios. The results from this study highlight the need to accurately account for the effects of pressure and characteristic scales for improved modeling of such flames.

Towards Energy-Balance Closure with a Model of Dispersive Heat Fluxes

In the last decades the energy-balance-closure problem has been thoroughly investigated from different angles, resulting in approaches to reduce but not completely close the surface energy balance gap. Energy transport through secondary circulations has been identified as a major cause of the remaining energy imbalance, as it is not captured by eddy covariance measurements and can only be measured additionally with great effort. Several models have already been developed to close the energy balance gap that account for factors affecting the magnitude of the energy transport by secondary circulations. However, to our knowledge, there is currently no model that accounts for thermal surface heterogeneity and that can predict the transport of both sensible and latent energy. Using a machine-learning approach, we developed a new model of energy transport by secondary circulations based on a large data set of idealized large-eddy simulations covering a wide range of unstable atmospheric conditions and surface-heterogeneity scales. In this paper, we present the development of the model and show first results of the application on more realistic LES data and field measurements from the CHEESEHEAD19 project to get an impression of the performance of the model and how the application can be implemented on field measurements. A strength of the model is that it can be applied without additional measurements and, thus, can retroactively be applied to other eddy covariance measurements to model energy transport through secondary circulations. Our work provides a promising mechanistic energy balance closure approach to 30-min flux measurements.

Quantifying wake dynamics subjected to stream vegetation patch elongation: The influence of patch-edge vortices

Vortices are generated across a wide range of scales due to the interaction between in-stream vegetation and surrounding flows, significantly influencing hydro-geomorphodynamics in earth surface water systems. Recent advance in vegetation patch hydrodynamics has revealed that the elongation of the middle channel patch can generate complex wake flow patterns by adjusting the bleed flow from the patch and triggering the patch-edge Kelvin–Helmholtz (KH) vortices. With a broader range of experimental configurations, this study reveals how the patch wake mixing is apparently strengthened by the presence of KH vortices, indicated by a larger steady wake velocity, a shorter steady wake length, and a damped energy of wake von Karman vortex. Furthermore, we quantify these characteristic metrics of patch wake behavior with and without the influence of KH vortices. Our findings provide insights into the role of vegetation-induced vortex interactions in regulating mixing processes, thereby promoting informed practices in environmental flows.

Dual attention-enhanced SSD: A novel deep learning model for object detection

Object detection is a fundamental task in computer vision with significant implications across various applications, including autonomous driving, surveillance, and image understanding. The accurate and efficient detection of objects within images is crucial for enabling machines to interpret visual information and make informed decisions. In this paper, we present an enhanced version of the Single Shot MultiBox Detector (SSD) for object detection, leveraging the concept of dual attention mechanisms. Our proposed approach, named SSD-Dual Attention, integrates dual attention layers into the SSD framework. These dual attention layers are strategically positioned between feature maps and prediction convolutions, enhancing the model's ability to capture informative feature representations across a wide range of object scales and backgrounds. Experimental results on the PASCAL VOC 2007 and 2012 datasets validate the effectiveness of our approach. Notably, SSD-Dual Attention achieves an impressive mean Average Precision (mAP) of 78.1%, surpassing the performance of SSD models enhanced with attention mechanisms such as SSD-ECA, SSD-CBAM, SSD-Non-local attention and SSD-SE attention, as well as the original SSD. These results underscore the enhanced accuracy and precision of our object detection system, marking a substantial advancement in the field of computer vision. Code is available at https://github.com/AlexHunterLeo/Dual-attention-Enhanced-SSD-A-Novel-Deep-Learning-Model-for-Object-Detection

Pickering oil-in-water emulsions stabilized by hybrid plant protein-flavonoid conjugate particles

Protein and polyphenols are often found as self-assembled structures in plants. Inspired by nature, this study looked at controlled engineering of hybrid nanoparticles using plant-based proteins and plant-derived flavonoid crystals. These nanoparticles have been shown to stabilize Pickering emulsions over several months. Potato protein (PoP) and flavonoid quercetin (QC) crystals in mass ratios (PoP:QC) ranging from 100:1 to 5:1, were used to fabricate the hybrid (PoPQC) nanoparticles at pH 7.0. The hydrodynamic diameter (dH), scattering pattern via small angle X-ray scattering (SAXS) and ζ-potential of the PoPQC as well as the protein conformation via circular dichroism (CD) and fluorescence were studied. The oil-in-water (O/W) emulsions stabilized by PoP and PoPQC nanoparticles with degrees of conjugation ranging 0.18–16.55% were analyzed by droplet sizing, ζ-potential measurements, and microscopy across a wide range of length scales. Both CD and SAXS analyses revealed that the conjugation of PoP with QC caused conformational changes in the secondary structure of PoP with the aromatic amino acids interacting with phenolic rings of QC, mainly through hydrophobic interactions. The addition of QC had a considerable impact on both particle size of PoPQC (∼50–∼400 nm) and consequently the droplet size of the corresponding Pickering emulsions stabilized by these spherical particles (d4,3 ∼2–∼35 μm). The droplet size increased significantly (p < 0.05) as the QC content of the particles was increased, whilst the ζ-potential became more negative. Nevertheless, the Pickering emulsions were capable of resisting coalescence over several months, suggesting the application of nature-inspired hybrid nanoparticles.

Auto- and Cross-Correlation Multifractal Analysis of Sea Surface Temperature Variability

In this study, we investigate multiscale auto- and cross-correlation structural characteristics of sea surface temperature (SST) variability using our new methodology, called the multifractal asymmetric cross-correlation analysis (MF-ACCA), incorporating signs of a segment’s detrended covariance and linear trend. SST is greatly affected by air–sea interactions and the advection of water masses with a wide range of spatiotemporal scales. Since these force factors are imprinted on SST variability, their features can be revealed in terms of long-range auto- and cross-correlation structures of SST variability via a multifractal analysis. By applying the MF-ACCA methodology to SST variability in the East/Japan Sea, we have found the following features: (1) the auto- and cross-correlation multifractal features are dependent on several parameters, such as the location, linear trends (rising or falling), level of fluctuations, and temporal scales; (2) there are crossover behaviors that are discrete for small scales (less than 1000 days) but continuous for large scales (more than 1000 days); (3) long-range persistence of auto- and cross-correlations is random for large scales during the falling phase; (4) long-range persistence is stronger during the rising phase than during the falling phase; (5) the degree of asymmetry is greater for large scales than for small scales.

Spectral Hardness of X‐ and Gamma‐Ray Emissions From Lightning Stepped and Dart Leaders

AbstractDuring the 2022 New Mexico monsoon season, we deployed two X‐ray scintillation detectors, coupled with a 180 MHz data acquisition system to detect X‐rays from natural lightning at the Langmuir Lab mountain‐top facility, located at 3.3 km above mean sea level. Data acquisition was triggered by an electric field antenna calibrated to pick up lightning within a few km of the X‐ray detectors. We report the energies of over 240 individual photons, ranging between 13 keV and 3.8 MeV, as registered by the LaBr3(Ce) scintillation detector. These detections were associated with four lightning flashes. Particularly, four‐stepped leaders and seven dart leaders produced energetic radiation. The reported photon energies allowed us to confirm that the X‐ray energy distribution of natural stepped and dart leaders follows a power‐law distribution with an exponent ranging between 1.09 and 1.96, with stepped leaders having a harder spectrum. Characterization of the associated leaders and return strokes was done with four different electric field sensing antennas, which can measure a wide range of time scales, from the static storm field to the fast change associated with dart leaders.

Water flow in shallow aquifers without the Dupuit hypothesis

In this paper, we present a new model as an alternative to the classical 3D Richards model for the description of water flow in shallow aquifers. The new model is designed to achieve three goals. First, it provides a good approximation to the Richards model over a wide range of time scales. More specifically we show that both models characterize a flow with the same dominant components when the ratio of the horizontal length to the depth of the aquifer is small. Second, the new model accurately describes the velocity field. In particular, it is not based on the Dupuit hypothesis, which is often used in the context of shallow aquifers. This allows the new model to be well accurate even in the presence of wells and in aquifers with variable bedrock. Third, the new model can be viewed as a coupling of numerous 1D vertical Richards problems with a 2D elliptic one. In practice, this coupling is treated numerically using a Picard fixed point scheme, avoiding the need to solve any three-dimensional problem. This results in a significant reduction in computational cost compared to solving the 3D Richards problem directly.

Kinetic stability of Chapman–Enskog plasmas

In this paper, we investigate the kinetic stability of classical, collisional plasma – that is, plasma in which the mean-free-path $\lambda$ of constituent particles is short compared with the length scale $L$ over which fields and bulk motions in the plasma vary macroscopically, and the collision time is short compared with the evolution time. Fluid equations are typically used to describe such plasmas, since their distribution functions are close to being Maxwellian. The small deviations from the Maxwellian distribution are calculated via the Chapman–Enskog (CE) expansion in $\lambda /L \ll 1$ , and determine macroscopic momentum and heat fluxes in the plasma. Such a calculation is only valid if the underlying CE distribution function is stable at collisionless length scales and/or time scales. We find that at sufficiently high plasma $\beta$ , the CE distribution function can be subject to numerous microinstabilities across a wide range of scales. For a particular form of the CE distribution function arising in strongly magnetised plasma (viz. plasma in which the Larmor periods of particles are much smaller than collision times), we provide a detailed analytic characterisation of all significant microinstabilities, including peak growth rates and their associated wavenumbers. Of specific note is the discovery of several new microinstabilities, including one at sub-electron-Larmor scales (the ‘whisper instability’) whose growth rate in certain parameter regimes is large compared with other instabilities. Our approach enables us to construct the kinetic stability maps of classical, two-species collisional plasma in terms of $\lambda$ , the electron inertial scale $d_e$ and the plasma $\beta$ . This work is of general consequence in emphasising the fact that high- $\beta$ collisional plasmas can be kinetically unstable; for strongly magnetised CE plasmas, the condition for instability is $\beta \gtrsim L/\lambda$ . In this situation, the determination of transport coefficients via the standard CE approach is not valid.

Cross‐Scale Modeling of Storm‐Time Radiation Belt Variability

AbstractDuring geomagnetic storms relativistic outer radiation belt electron flux exhibits large variations on rapid time scales of minutes to days. Many competing acceleration and loss processes contribute to the dynamic variability of the radiation belts; however, distinguishing the relative contribution of each mechanism remains a major challenge as they often occur simultaneously and over a wide range of spatiotemporal scales. In this study, we develop a new comprehensive model for storm‐time radiation belt dynamics by incorporating electron wave‐particle interactions with parallel propagating whistler mode waves into our global test‐particle model of the outer belt. Electron trajectories are evolved through the electromagnetic fields generated from the Multiscale Atmosphere‐Geospace Environment (MAGE) global geospace model. Pitch angle scattering and energization of the test particles are derived from analytical expressions for quasi‐linear diffusion coefficients that depend directly on the magnetic field and density from the magnetosphere simulation. Using a study of the 17 March 2013 geomagnetic storm, we demonstrate that resonance with lower band chorus waves can produce rapid relativistic flux enhancements during the main phase of the storm. While electron loss from the outer radiation belt is dominated by loss through the magnetopause, wave‐particle interactions drive significant atmospheric precipitation. We also show that the storm‐time magnetic field and cold plasma density evolution produces strong, local variations of the magnitude and energy of the wave‐particle interactions and is critical to fully capturing the dynamic variability of the radiation belts caused by wave‐particle interactions.

Wavelet Analysis of Differential TEC Measurements Obtained Using LOFAR

AbstractRadio interferometers used to make astronomical observations, such as the LOw Frequency ARray (LOFAR), experience distortions imposed upon the received signal due to the ionosphere as well as those from instrumental errors. Calibration using a well‐characterized radio source can be used to mitigate these effects and produce more accurate images of astronomical sources, and the calibration process provides measurements of ionospheric conditions over a wide range of length scales. The basic ionospheric measurement this provides is differential Total Electron Content (TEC, the integral of electron density along the line of sight). Differential TEC measurements made using LOFAR have a precision of &lt;1 mTECu and therefore enable investigation of ionospheric disturbances which may be undetectable to many other methods. We demonstrate an approach to identify ionospheric waves from these data using a wavelet transform and a simple plane wave model. The noise spectra are robustly characterized to provide uncertainty estimates for the fitted parameters. An example is shown in which this method identifies a wave with an amplitude an order of magnitude below those reported using Global Navigation Systems Satellite TEC measurements. Artificially generated data are used to test the accuracy of the method and establish the range of wavelengths which can be detected using this method with LOFAR data. This technique will enable the use of a large and mostly unexplored data set to study traveling ionospheric disturbances over Europe.

Anisotropic Green Tide Patch Information Extraction Based on Deformable Convolution

Green tides are marine disasters caused by the explosive proliferation or high concentration of certain large algae in seawater, which causes discoloration of the water body. Accurate monitoring of its distribution area is highly important for early warning and the protection of marine ecology. However, existing deep learning methods have difficulty in effectively identifying green tides with anisotropic characteristics due to the complex and variable shapes of the patches and the wide range of scales. To address this issue, this paper presents an anisotropic green tide patch extraction network (AGE-Net) based on deformable convolution. The main structure of AGE-Net consists of stacked anisotropic feature extraction (AFEB) modules. Each AFEB module contains two branches for extracting green tide patches. The first branch consists of multiple connected dense blocks. The second branch introduces a deformable convolution module and a depth residual module based on a multiresolution feature extraction network for extracting anisotropic features of green tide patches. Finally, an irregular green tide patch feature enhancement module is used to fuse the high-level semantic features extracted from the two branches. To verify the effectiveness of the AGE-Net model, experiments were conducted on the MODIS Green Tide dataset. The results show that AGE-Net has better recognition performance, with F1-scores and IoUs reaching 0.8317 and 71.19% on multi-view test images, outperforming other comparison methods.

Search for hidden neutrinos at the European Spallation Source: the SHiNESS experiment

The upcoming European Spallation Source (ESS) will soon provide the most intense neutrino source in the world. We propose the Search for Hidden Neutrinos at the ESS (SHiNESS) experiment, highlighting its unique opportunities to search for the existence of sterile neutrinos across a wide range of scales: anomalous oscillations at short baselines; non-unitarity mixing in the active neutrino sector; or an excess of events with multiple leptons in the final state, produced in the decay of heavy neutrinos. The baseline design of the detector comprises an active volume filled with 42 ton of liquid scintillator, located 25 m far from the ESS beam target. We show that SHiNESS will be able to considerably improve current global limits for the three cases outlined above. Although in this work we focus on new physics in the neutrino sector, the proposed setup may also be used to search for signals from weakly interacting particles in a broader context.

Abstract 7383: G2PT: Mechanistic genotype-phenotype translation using hierarchical transformers

Abstract Motivation: Many machine learning models have been proposed that use genotypes to predict various phenotypes. Recently, these models have focused not only on an accurate prediction but mechanistic interpretation, in that they attempt to describe the hierarchy of biological systems underlying the predicted phenotype. Such models still face major challenges, however, including how to robustly quantify the importance of systems mediating phenotypic outcomes and how to represent the bidirectional flow of information among genes, systems, phenotypes, and cell environment. Results: To address these challenges we introduce G2PT, a general Genotype-to-Phenotype Transformer, which we apply here to the classic G2P problem of modeling tumor cell sensitivity to drugs. We show that the framework of hierarchical transformers can represent biological systems spanning a wide range of physical scales and provide a quantitative assessment of the interplay between biological components important for phenotype prediction. When used to analyze a high-throughput cell screen of 436 drugs exposed to 1,097 cell types, we show that the model achieves good performance in predicting cell sensitivity for 83% of drugs (⍴ &amp;gt; 0.5 in cross-validation). G2PT provides a robust and interpretable model with potential application to many challenges in genotype-phenotype translation. Citation Format: Ingoo Lee, Sungjoon Park, Hojung Nam, Trey Ideker. G2PT: Mechanistic genotype-phenotype translation using hierarchical transformers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7383.

Cloud properties across spatial scales in simulations of the interstellar medium

Molecular clouds (MCs) are structures of dense gas in the interstellar medium (ISM) that extend from ten to a few hundred parsecs and form the main gas reservoir available for star formation. Hydrodynamical simulations of a varying complexity are a promising way to investigate MCs evolution and their properties. However, each simulation typically has a limited range in resolution and different cloud extraction algorithms are used, which complicates the comparison between simulations. In this work, we aim to extract clouds from different simulations covering a wide range of spatial scales. We compare their properties, such as size, shape, mass, internal velocity dispersion, and virial state. We applied the Hop cloud detection algorithm on (M)HD numerical simulations of stratified ISM boxes and isolated galactic disk simulations that were produced using Flash Ramses and Arepo We find that the extracted clouds are complex in shape, ranging from round objects to complex filamentary networks in all setups. Despite the wide range of scales, resolution, and sub-grid physics, we observe surprisingly robust trends in the investigated metrics. The mass spectrum matches in the overlap between simulations without rescaling and with a high-mass power-law index of -1 for logarithmic bins of mass, in accordance with theoretical predictions. The internal velocity dispersion scales with the size of the cloud as $ $ for large clouds ($R pc $). For small clouds we find larger sigma compared to the power-law scaling, as seen in observations, which is due to supernova-driven turbulence. Almost all clouds are gravitationally unbound with the virial parameter scaling as $ vir $, which is slightly flatter compared to observed scaling but in agreement given the large scatter. We note that the cloud distribution towards the low-mass end is only complete if the more dilute gas is also refined, rather than only the collapsing regions.

Inelastic neutron scattering and spectroscopy methods to characterize dynamics in colloidal and soft matter systems

Colloidal systems and soft materials are well suited to neutron scattering, and the community has readily adopted elastic scattering techniques to investigate their structure. Due to their unique properties, neutrons may also be used to characterize the dynamics of soft materials over a wide range of length and time scales in situ. Both static structures and an understanding of how molecules move about their equilibrium positions is essential if we are to deliver on the promise of rationally designing soft materials. In this review we introduce the basics of neutron spectroscopy and explore the ways in which inelastic neutron scattering can be used to study colloidal and soft materials. Illustrative examples are chosen that highlight the phenomena suitable for investigation using this suite of techniques.

Cosmic-Eν: An- emulator for the non-linear neutrino power spectrum

ABSTRACT Cosmology is poised to measure the neutrino mass sum Mν and has identified several smaller-scale observables sensitive to neutrinos, necessitating accurate predictions of neutrino clustering over a wide range of length scales. The FlowsForTheMasses non-linear perturbation theory for the the massive neutrino power spectrum, $\Delta ^2_\nu (k)$, agrees with its companion N-body simulation at the $10~{{\ \rm per\ cent}}-15~{{\ \rm per\ cent}}$ level for k ≤ 1 h Mpc−1. Building upon the Mira-Titan IV emulator for the cold matter, we use FlowsForTheMasses to construct an emulator for $\Delta ^2_\nu (k)$, Cosmic-Eν, which covers a large range of cosmological parameters and neutrino fractions Ων, 0h2 ≤ 0.01 (Mν ≤ 0.93 eV). Consistent with FlowsForTheMasses at the 3.5 per cent level, it returns a power spectrum in milliseconds. Ranking the neutrinos by initial momenta, we also emulate the power spectra of momentum deciles, providing information about their perturbed distribution function. Comparing a Mν = 0.15 eV model to a wide range of N-body simulation methods, we find agreement to 3 per cent for k ≤ 3kFS = 0.17 h Mpc−1 and to 19 per cent for k ≤ 0.4 h Mpc−1. We find that the enhancement factor, the ratio of $\Delta ^2_\nu (k)$ to its linear-response equivalent, is most strongly correlated with Ων, 0h2, and also with the clustering amplitude σ8. Furthermore, non-linearities enhance the free-streaming-limit scaling $\partial \log (\Delta ^2_\nu /\Delta ^2_{\rm m}) / \partial \log (M_\nu)$ beyond its linear value of 4, increasing the Mν-sensitivity of the small-scale neutrino density.

Exploring Host-Guest Interactions within a 600 kDa DegP Protease Cage Complex Using Hydrodynamics Measurements and Methyl-TROSY NMR.

The DegP protease-chaperone operates within the periplasm of Gram-negative bacteria, where it assists in the regulation of protein homeostasis, promotes virulence, and is essential to survival under stress. To carry out these tasks, DegP forms a network of preorganized apo oligomers that facilitate the capture of substrates within distributions of cage-like complexes which expand to encapsulate clients of various sizes. Although the architectures of DegP cage complexes are well understood, little is known about the structures, dynamics, and interactions of client proteins within DegP cages and the relationship between client structural dynamics and function. Here, we probe host-guest interactions within a 600 kDa DegP cage complex throughout the DegP activation cycle using a model α-helical client protein through a combination of hydrodynamics measurements, methyl-transverse relaxation optimized spectroscopy-based solution nuclear magnetic resonance studies, and proteolytic activity assays. We find that in the presence of the client, DegP cages assemble cooperatively with few intermediates. Our data further show that the N-terminal half of the bound client, which projects into the interior of the cages, is predominantly unfolded and flexible, and exchanges between multiple conformational states over a wide range of time scales. Finally, we show that a concerted structural transition of the protease domains of DegP occurs upon client engagement, leading to activation. Together, our findings support a model of DegP as a highly cooperative and dynamic molecular machine that stabilizes unfolded states of clients, primarily via interactions with their C-termini, giving rise to efficient cleavage.

Non-extensive (Tsallis) q-statistics and auroral glow

It is well-known that the auroral region of the magnetosphere–ionosphere interaction is an open, nonlinear dissipative system far from the equilibrium state. It is in this region that auroras are regularly observed, demonstrating not only a wide variety of dynamic forms but also a wide range of temporal and spatial scales. Due to the memory effects and fractal properties of auroral plasma, as well as strong correlations between its individual parts, the dynamics of the high-latitude system are primarily determined by long-range electromagnetic forces. Therefore, it is expected that non-additive and non-extensive thermodynamic principles may characterize their macroscopic behavior. In the presented study, it is shown that pulsating auroras exhibit non-extensive properties and can be described by q-statistics. The correlation of the parameter q with traditional indicators of nonlinear dynamics, such as the flatness coefficient, standard deviation, and scaling index, has been investigated. Small-scale changes are superimposed on large-scale processes characterized by a low correlation dimension, and this leads to an increase in the correlation dimension during geomagnetic activity, which approximately coincides with increased values of q. It is concluded that the q-statistics (Tsallis distribution) can be used to analyze optical phenomena in the auroral region.