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Markus J. Aschwanden

Publications and source records attributed to Markus J. Aschwanden.

At least 19 recordsLinked to original sources

The Energy-Duration Relationship in Astrophysical Self-Organized Criticality Systems

Scaling laws in astrophysical systems that involve the energy, the geometry, and the spatio-temporal evolution, provide the theoretical framework for physical models of energy dissipation processes. A leading model is the standard fractal-diffusive self-organized criticality (FD-SOC) model, which is built on four fundamental assumptions: (i) the dimensionality $d=3$, (ii) the fractal dimension $D_V=d-1/2=2.5$, (iii) classical diffusion $L \propto T^{(1/2)}$, and (iv) the proportionality of the dissipated energy to the fractal volume $E \propto V$. Based on these assumptions, the FD-SOC model predicts a scaling law of $T \propto E^k \propto E^{(4/5)} = E^{0.8}$. On the observational side, we find empirical scaling laws of $T \propto E^{0.81\pm0.03}$ by Peng et al.~(2023) and $T \propto E^{0.86\pm0.03}$ by Araujo \& Valio (2021) that are self-consistent with the theoretical prediction of the FD-SOC model. However, cases with a small time range $q_T = \log{(T_{max}/T_{min})} \lapprox 2$ have large statistical uncertainties and systematic errors, which produces smaller scaling law exponents ($k \approx 0.3, ..., 0.6$) as a consequence. The close correlation of the scaling exponent $k$ with the truncation bias $q_T$ implies that the dispersion of k-values is an observational effect, rather than a physical property.

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New Trends in Astrophysical Self-Organized Criticality

This review is focused on recent {\sl Self-Organized Criticality (SOC)} literature of astrophysical phenomena, covering the last decade of (2015-2025), while previous SOC literature (1987-2014) is reviewed elsewhere. The selection of literature is mostly based on searches with the NASA-supported {\sl Astrophysics Data System (ADS)}. The discussed astrophysical SOC phenomena are subdivided into solar flares, solar atmosphere (photosphere, chromosphere, corona), heliospheric systems (coronal mass ejections, solar wind, solar energetic particles), planetary systems (asteroids and small bodies, lunar cratering, Saturnian ring systems, magnetospheric systems), stellar flares, and galactic systems (pulsar glitches, gamma ray bursts, soft gamma-ray repeaters, supergiant fast X-ray transients, fast transient radio bursts, magnetars, blazars, black holes).

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How the Solar Dynamics Observatory Revolutionized our Physical Understanding of the Sun

We review solar studies using AIA, HMI, and EVE data from the SDO spacecraft that revolutionized our physical understanding of the Sun. The relevant SDO studies cover the entire 15-year lifetime of SDO, from 2010 May 1 to 2025 May 1. The discussed phenomena and their physical interpretations include (in chronological order): (1) MHD Waves and Oscillations (AIA, HMI); (2) Propagating MHD Waves (AIA); (3) Coronal Loop Cross-Sectional Temperatures (AIA); (4) Size Distributions of Solar Flare Parameters (AIA); (5) Spatio-Temporal Evolution and Diffusion (AIA); (6) The Rosner-Tucker-Vaiana (RTV) Scaling Law (AIA); (7) The Fractal-Diffusive Self-Organized Criticality Model (AIA); (8) Automated Temperature and Emission Measure Maps (AIA); (9) Automated Pattern Recognition Codes (AIA); (10) Kelvin-Helmholtz Instability in Reconnetion Outflows (AIA); (11) Hydrodstatics of Coronal Loops (AIA); (12) Magnetic Energy Dissipation (HMI); (13) Global Energetics of Solar Flares (AIA).

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Power Laws Associated with Self-Organized Criticality: A Comparison of Empirical Data with Model Predictions

We investigate the relevance of {\sl self-organized criticality (SOC)} models in previously published empirical datasets, which includes statistical observations in astrophysics, geophysics, biophysics, sociophysics, and informatics. We study 25 interdisciplinary phenomena with five different event detection and power law fitting methods. The total number of analyzed size distributions amounts to 64 cases, of which 80\% are found to be nearly consistent ($α_s=1.99\pm0.30$) with the SOC model predictions. The fractal-diffusive SOC model predicts power law slopes of $α_F=(9/5)=1.80$ for the flux $F$, $α_E=(5/3)\sim1.67$ for the fluence or energy $E$, and $α_T=2.00$ for the avalanche duration $T$. We find that the phenomena of solar flares, earthquakes, and forest fires are consistent with the theoretical predictions, while the size distributions of other phenomena are not conclusive due to neglected background treatment, inadequacy of power law fitting range, small-number statistics, and finite-system size effects.

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Self-Organized Criticality Across Thirteen Orders of Magnitude in the Solar-Stellar Connection

The observed size distributions of solar and stellar flares is found to be consistent with the predictions of the fractal-diffusive self-organized criticality (FD-SOC) model, which predicts power law slopes with universal constants of $α_F=(9/5)=1.80$ for the flux, and $α_E=(5/3)\approx 1.67$ for the fluence, respectively. In this Letter we explore the solar-stellar connection under this aspect, which extends over an unprecedented dynamic range of 13 orders of magnitude between the smallest detected solar nanoflare event ($E_{min}=10^{24}$ erg) and the largest superflare ($E_{max}=10^{37}$ erg) on solar-like G-type stars, observed with the KEPLER mission. The FD-SOC model predicts a scaling law of $L \propto E^{(2/9)}$ for the length scale $L$ as a function of the flare energy $E$, which limits the largest flare size to $L_{max} \lapprox 0.14 R_\odot$ for solar flares, and $L_{stellar} \lapprox 1.04\ R_{\odot}$ for stellar flares on G-type stars. In the overall we conclude that the universality of power laws (and their slopes) is a consequence of SOC properties (fractality, classical diffusion, scale-freeness, volume-flux proportionality), rather than identical physical processes at different wavelengths.

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The Universality of Power Law Slopes in the Solar Photosphere and Transition Region Observed with HMI and IRIS

We compare the size distributions of self-organized criticality (SOC) systems in the solar photosphere and the transition region, using magnetogram data from Helioseismic and Magnetic Imager (HMI) and Interface Region Imaging Spectrograph (IRIS)} data. For each dataset we fit a combination of a Gaussian and a power law size distribution function, which yields information on four different physical processes: (i) Gaussian random noise in IRIS data; (ii) spicular events in the plages of the transition region (described by power law size distribution in IRIS data); (iii) salt-and-pepper small-scale magnetic structures (described by the random noise in HMI magnetograms); and (iv) magnetic reconnection processes in flares and nanoflares (described by power law size distributions in HMI data). We find a high correlation (CCC=0.90) between IRIS and HMI data. Datasets with magnetic flux balance are generally found to match the SOC-predicted power law slope a_F=1.80 (for mean fluxes F), but exceptions occur due to arbitrary choices of the HMI field-of-view. The matching cases confirm the universality of SOC-inferred flux size distributions, and agree with the results of Parnell et al.~(2009), a_F=1.85 +/- 0.14.

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Interface Region Imaging Spectrograph (IRIS) Observations of the Fractal Dimension in the Solar Atmosphere

While previous work explored the fractality and self-organized criticality (SOC) of flares and nanoflares in wavelengths emitted in the solar corona (such as in hard X-rays, soft X-rays, and EUV wavelenghts), we focus here on impulsive phenomena in the photosphere and transition region, as observed with the {\sl Interface Region Imaging Spectrograph (IRIS)} in the temperature range of $T_e \approx 10^4-10^6$ K. We find the following fractal dimensions (in increasing order): $D_A=1.21 \pm 0.07$ for photospheric granulation, $D_A=1.29 \pm 0.15$ for plages in the transition region, $D_A=1.54 \pm 0.16$ for sunspots in the transition region, $D_A=1.59 \pm 0.08$ for magnetograms in active regions, $D_A=1.56 \pm 0.08$ for EUV nanoflares, $D_A=1.76 \pm 0.14$ for large solar flares, and up to $D_A=1.89 \pm 0.05$ for the largest X-class flares. We interpret low values of the fractal dimension ($1.0 \lapprox D_A \lapprox 1.5$) in terms of sparse curvi-linear flow patterns, while high values of the fractal dimension ($1.5 \lapprox D_A \lapprox 2.0$) indicate near space-filling transport processes, such as chromospheric evaporation. Phenomena in the solar transition region appear to be consistent with SOC models, based on their size distributions of fractal areas $A$ and (radiative) energies $E$, which show power law slopes of $α_A^{obs}=2.51 \pm 0.21$ (with $α_A^{theo}=2.33$ predicted), and $α_E^{obs}=2.03 \pm 0.18$ (with $α_E^{theo}=1.80$ predicted).

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Reconciling Power Law Slopes in Solar Flare and Nanoflare Size Distributions

We unify the power laws of size distributions of solar flare and nanoflare energies. We present three models that predict the power law slopes $α_E$ of flare energies defined in terms of the 2-D and 3-D fractal dimensions ($D_A, D_V$): (i) The spatio-temporal standard SOC model, defined by the power law slope $α_{E1}=1+2/(D_V+2)=(13/9)\approx 1.44$; (ii) the 2-D thermal energy model, $α_{E2}=1+2/D_A=(7/3)\approx 2.33$, and (iii) the 3-D thermal energy model, $α_{E3}=1+2/D_V=(9/5)\approx 1.80$. The theoretical predictions of energies are consistent with the observational values of these three groups, i.e., $α_{E1}=1.47 \pm 0.07$; $α_{E2}=2.38 \pm 0.09$, and $α_{E3}=1.80 \pm 0.18$. These results corroborate that the energy of nanoflares does not diverge at small energies, since $(α_{E1}<2$) and $(α_{E3}<2)$, except for the unphyiscal 2-D model $(α_{E2}>2)$. This conclusion adds an additional argument against the scenario of coronal heating by nanoflares.

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Global energetics of solar flares. XIII. The Neupert effect and acceleration of coronal mass ejections

Our major aim is a height-time model $r(t)$ of the propagation of {\sl Coronal Mass Ejections (CMEs)}, where the lower corona is self-consistently connected to the heliospheric path. We accomplish this task by using the Neupert effect to derive the peak time, duration, and rate of the CME acceleration phase, as obtained from the time derivative of the {\sl soft X-ray (SXR)} light curve. This novel approach offers the advantage to obtain the kinematics of the CME height-time profile $r(t)$, the CME velocity profile $v(t)=dr(t)/dt$, and the CME acceleration profile $a(t)=dv(t)/dt$ from {\sl Geostationary Orbiting Earth Satellite (GOES)} and white-light data, without the need of {\sl hard X-ray (HXR)} data. We apply this technique to a data set of 576 (GOES X and M-class) flare events observed with GOES and the {\sl Large Angle Solar Coronagraph (LASCO)}. Our analysis yields acceleration rates in the range of $a_A = 0.1-13$ km s$^{-2}$, acceleration durations of $τ_A = 1.2-45$ min, and acceleration distances in the range of $d_A = 3-1063$ Mm, with a median of $d_A=39$ Mm, which corresponds to the hydrostatic scale height of a corona with a temperature of $T_e \approx 0.8$ MK. The results are consistent with standard flare/CME models that predict magnetic reconnection and synchronized (primary) acceleration of CMEs in the low corona (at a height of ~0.1 R_sun), while secondary (weaker) acceleration may occur further out at heliospheric distances.

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The Solar Memory From Hours to Decades

Waiting time distributions allow us to distinguish at least three different types of dynamical systems, such as (i) linear random processes (with no memory); (ii) nonlinear, avalanche-type, nonstationary Poisson processes (with memory during the exponential growth of the avalanche rise time); and (iii) chaotic systems in the state of a nonlinear limit cycle (with memory during the oscillatory phase). We describe the temporal evolution of the flare rate $λ(t) \propto t^p$ with a polynomial function, which allows us to distinguish linear ($p \approx 1$) from nonlinear ($p \gapprox 2$) events. The power law slopes $α$ of observed waiting times (with full solar cycle coverage) cover a range of $α=2.1-2.4$, which agrees well with our prediction of $α= 2.0+1/p = 2.3-2.5$. The memory time can also be defined with the time evolution of the logistic equation, for which we find a relationship between the nonlinear growth time $τ_G = τ_{rise}/(4p)$ and the nonlinearity index $p$. We find a nonlinear evolution for most events, in particular for the clustering of solar flares ($p=2.2\pm0.1$), partially occulted flare events ($p=1.8\pm0.2$), and the solar dynamo ($p=2.8\pm0.5$). The Sun exhibits memory on time scales of $\lapprox$2 hours to 3 days (for solar flare clustering), 6 to 23 days (for partially occulted flare events), and 1.5 month to 1 year (for the rise time of the solar dynamo).

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The Poissonian Origin of Power Laws in Solar Flare Waiting Time Distributions

In this study we aim for a deeper understanding of the power law slope, $α$, of waiting time distributions. Statistically independent events with linear behavior can be characterized by binomial, Gaussian, exponential, or Poissonian size distribution functions. In contrast, physical processes with nonlinear behavior exhibit spatio-temporal coherence (or memory) and "fat tails" in their size distributions that fit power law-like functions, as a consequence of the time variability of the mean event rate, as demonstrated by means of Bayesian block decomposition in the work of Wheatland et al.~(1998). In this study we conduct numerical simulations of waiting time distributions $N(τ)$ in a large parameter space for various (polynomial, sinusoidal, Gaussian) event rate functions $λ(t)$, parameterized with an exponent $p$ that expresses the degree of the polynomial function $λ(t) \propto t^p$. We derive an analytical exact solution of the waiting time distribution function in terms of the incomplete gamma function, which is similar to a Pareto type-II function and has a power law slope of $α= 2 + 1/p$, in the asymptotic limit of large waiting times. Numerically simulated random distributions reproduce this theoretical prediction accurately. Numerical simulations in the nonlinear regime ($p \ge 2$) predict power law slopes in the range of $2.0 \le α\le 2.5$. The self-organized criticality model yields a prediction of $α=2$. Observations of solar flares and coronal mass ejections (over at least a half solar cycle) are found in the range of $α_{obs} \approx 2.1-2.4$. Deviations from strict power law functions are expected due to the variability of the flare event rate $λ(t)$, and deviations from theoretically predicted slope values $α$ occur due to the Poissonian weighting bias of power law fits.

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Self-Organized Criticality in Stellar Flares

Power law size distributions are the hallmarks of nonlinear energy dissipation processes governed by self-organized criticality. Here we analyze 75 data sets of stellar flare size distributions, mostly obtained from the {\sl Extreme Ultra-Violet Explorer (EUVE)} and the {\sl Kepler} mission. We aim to answer the following questions for size distributions of stellar flares: (i) What are the values and uncertainties of power law slopes? (ii) Do power law slopes vary with time ? (iii) Do power law slopes depend on the stellar spectral type? (iv) Are they compatible with solar flares? (v) Are they consistent with self-organized criticality (SOC) models? We find that the observed size distributions of stellar flare fluences (or energies) exhibit power law slopes of $α_E=2.09\pm0.24$ for optical data sets observed with Kepler. The observed power law slopes do not show much time variability and do not depend on the stellar spectral type (M, K, G, F, A, Giants). In solar flares we find that background subtraction lowers the uncorrected value of $α_E=2.20\pm0.22$ to $α_E=1.57\pm0.19$. Furthermore, most of the stellar flares are temporally not resolved in low-cadence (30 min) Kepler data, which causes an additional bias. Taking these two biases into account, the stellar flare data sets are consistent with the theoretical prediction $N(x) \propto x^{-α_x}$ of self-organized criticality models, i.e., $α_E=1.5$. Thus, accurate power law fits require automated detection of the inertial range and background subtraction, which can be modeled with the generalized Pareto distribution, finite-system size effects, and extreme event outliers.

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Correlation of the sunspot number and the waiting time distribution of solar flares, coronal mass ejections, and solar wind switchback events observed with the Parker Solar Probe

Waiting time distributions of solar flares and {\sl coronal mass ejections (CMEs)} exhibit power law-like distribution functions with slopes in the range of $α_τ \approx 1.4-3.2$, as observed in annual data sets during 4 solar cycles (1974-2012). We find a close correlation between the waiting time power law slope $α_τ$ and the {\sl sunspot number (SN)}, i.e., $α_τ$ = 1.38 + 0.01 $\times$ SN. The waiting time distribution can be fitted with a Pareto-type function of the form $N(τ) = N_0$ $(τ_0 + τ)^{-α_τ}$, where the offset $τ_0$ depends on the instrumental sensitivity, the detection threshold of events, and pulse pile-up effects. The time-dependent power law slope $α_τ(t)$ of waiting time distributions depends only on the global solar magnetic flux (quantified by the sunspot number) or flaring rate, independent of other physical parameters of {\sl self-organized criticality (SOC)} or {\sl magneto-hydrodynamic (MHD)} turbulence models. Power law slopes of $α_τ\approx 1.2-1.6$ were also found in solar wind switchback events, as observed with the {\sl Parker Solar Probe (PSP)}. We conclude that the annual variability of switchback events in the heliospheric solar wind is modulated by flare and CME rates originating in the photosphere and lower corona.

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Finite System-Size Effects in Self-Organized Criticality Systems

We explore upper limits for the largest avalanches or catastrophes in nonlinear energy dissipation systems governed by self-organized criticality (SOC). We generalize the idealized "straight" power low size distribution and Pareto distribution functions in order to accomodate for incomplete sampling, limited instrumental sensitivity, finite system-size effects, "Black-Swan" and "Dragon-King" extreme events. Our findings are: (i) Solar flares show no finite system-size limits up to L < 200 Mm, but solar flare durations reveal an upper flare duration limit of < 6 hrs; (ii) Stellar flares observed with KEPLER exhibit inertial ranges of $E \approx 10^{34}-10^{37}$ erg, finite system-size ranges at $E \approx 10^{37}-10^{38}$ erg, and extreme events at $E =(1-5) \times 10^{38}$ erg; (iii) The maximum flare energy of different spectral-type stars (M, K, G, F, A, Giants) reveal a positive correlation with the stellar radius, which indicates a finite system-size limit imposed by the stellar surface area. Fitting our finite system-size models to terrestrial data sets (Earth quakes, wildfires, city sizes, blackouts, terrorism, words, surnames, web-links) yields evidence (in half of the cases) for finite system-size limits and extreme events, which can be modeled with dual power law size distributions.

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Global Energetics of Solar Flares. XII. Energy Scaling Laws

In this study we test 30 variants of 5 physical scaling laws that describe different aspects of solar flares. We express scaling laws in terms of the magnetic potential field energy $E_p$, the mean potential field strength $B_p$, the free energy $E_{free}$, the dissipated magnetic flare energy $E_{diss}$, the mean loop length scale $L$, the mean helically twisted flux tube radius $R$, the sunspot radius $r$, the emission measure-weighted flare temperature $T_w$, the electron density $n_e$, and the total emission measure $EM$, measured from a data set of $\lapprox 400$ GOES M- and X-class flare events. The 5 categories of physical scaling laws include (i) a scaling law of the potential-field energy, (ii) a scaling law for helical twisting, (iii) a scaling law for Petschek-type magnetic reconnection, (iv) the Rosner-Tucker-Vaiana scaling law, and (v) the Shibata-Yokoyama scaling law. We test the self-consistency of these theoretical scaling laws with observed parameters by requiring two conditions: a cross-corrleation coefficient of CCC$>$0.5 between the observed and theoretically predicted scaling laws, and a linear regression fit with a slope of $α\approx 1$. With these two criteria we find that 10 out of the 30 tested scaling law variants are consistent with the observed data, which strongly corroborates the existence and validity of the tested flare scaling laws.

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Global Energetics of Solar Flares: X. Petschek Reconnection Rate and Alfven Mach Number of Magnetic Reconnection Outflows

We investigate physical scaling laws for magnetic energy dissipation in solar flares, in the framework of the Sweet-Parker model and the Petschek model. We find that the total dissipated magnetic energy $E_{diss}$ in a flare depends on the mean magnetic field component $B_f$ associated with the free energy $E_f$, the length scale $L$ of the magnetic area, the hydrostatic density scale height $λ$ of the solar corona, the Alfvén Mach number $M_A=v_1/v_A$ (the ratio of the inflow speed $v_1$ to the Alfvénic outflow speed $v_A$), and the flare duration $τ_f$, i.e., $E_{diss} = (1/4π) B_f^2\ L\ λ v_A\ M_A\ τ_f$, where the Alfvén speed depends on the nonpotential field strength $B_{np}$ and the mean electron density $n_e$ in the reconnection outflow. Using MDI/SDO and AIA/SDO observations and 3-D magnetic field solutions obtained with the vertical-current approximation nonlinear force-free field code (VCA-NLFFF) we measure all physical parameters necessary to test scaling laws, which represents a new method to measure Alfvén Mach numbers $M_A$, the reconnection rate, and the total free energy dissipated in solar flares.

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Global Energetics of Solar Flares. XI. Flare Magnitude Predictions of the GOES-Class

In this study we determine scaling relationships of observed solar flares that can be used to predict upper limits of the GOES-class magnitude of solar flares. The flare prediction scheme is based on the scaling of the slowly-varying potential energy $E_p(t)$, which is extrapolated in time over an interval of $Δt \le$ 24 hrs. The observed scaling of the dissipated energy $E_{diss}$ scales with the potential field energy as $E_{diss} \propto E_p^{1.32}$. In addition, the observed scaling relationship of the flare volume, $V \propto E_{diss}^{1.17}$, the multi-thermal energy, $E_{th} \propto V^{0.76}$, the flare emission measure $EM \propto E_{th}^{0.79}$, the EM-weighted temperature $T_{w}$, and the GOES flux, $F_8(t) \propto E_p(t)^{0.92}$, allows us then to predict an upper limit of the GOES-class flare magnitude in the extrapolated time window. We find a good correlation (CCC$\approx 0.7$) between the observed and predicted GOES-class flare magnitudes (in 172 X and M-class events). This is the first algorithm that employs observed scaling laws of physical flare parameters to predict GOES flux upper limits, an important capability that complements previous flare prediction methods based on machine-learning algorithms used in space weather forecasting.

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Torsional Alfvénic Oscillations Discovered in the Magnetic Free Energy During Solar Flares

We report the discovery of torsional Alfvénic oscillations in solar flares, which modulate the time evolution of the magnetic free energy $E_f(t)$, while the magnetic potential energy $E_p(t)$ is uncorrelated, and the nonpotential energy varies as $E_{np}(t) = E_p + E_f(t)$. The mean observed time period of the torsional oscillations is $P_{obs}=15.1 \pm 3.9$ min, the mean field line length is $L=135\pm35$ Mm, and the mean phase speed is $v_{phase} =315 \pm 120$ km s$^{-1}$, which we interpret as torsional Alfvénic waves in flare loops with enhanced electron densities. Most of the torsional oscillations are found to be decay-less, but exhibit a positive or negative trend in the evolution of the free energy, indicating new emerging flux (if positive), magnetic cancellation, or flare energy dissipation (if negative). The time evolution of the free energy has been calculated in this study with the {\sl Vertical-Current Approximation (Version 4) Nonlinear Force-Free Field (VCA4-NLFFF)} code, which incorporates automatically detected coronal loops in the solution and bypasses the non-forcefreeness of the photospheric boundary condition, in contrast to traditional NLFFF codes.

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