arXiv subjects

Nastaran Farhang

Publications and source records attributed to Nastaran Farhang.

6 recordsLinked to original sources

arXiv2026

A Convolutional Neural Network-Derived Catalog of Solar Flares from Soft X-Ray Observations

A convolutional neural network (CNN) is used to construct a new catalog for solar flares based on high resolution (1-s cadence) Geostationary Operational Environmental Satellites (GOES) soft X-ray data. The CNN is trained to identify flare rise episodes. From 1 January 2018 to 22 August 2025, the algorithm detects 111,580 flare candidates, compared with 14,612 events in the corresponding GOES catalog. For each candidate, the probability of being a true positive is quantified by Bayesian inference based on the peak flux, rise time, and temporal coincidence with cataloged events where available. The flare size and waiting-time distributions are studied and compared with the GOES catalog. The CNN catalog shows a steeper power-law index for raw peak fluxes (-2.59 -\+ 0.02) than GOES (-2.25 -\+ 0.04), indicating the CNN's higher sensitivity to small events. After background correction, the indices are -1.97 -\+ 0.02 (CNN) and -2.05 -\+ 0.04 (GOES). The CNN catalog extends the power-law distribution of flare peak fluxes by one order of magnitude at the small-flux end compared with the GOES background-subtracted catalog. A Bayesian blocks analysis of the waiting-time distributions from the GOES and CNN catalogs indicates broad consistency with a piecewise Poisson process. We find that previously reported correlations between flare sizes and waiting times are significantly influenced by obscuration, that is, under-counting weaker or overlapping flares during periods of elevated flux. The new CNN catalog provides a foundation for complete and consistent studies of solar flare statistics.

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arXiv2023

Complex Network View of the Sun's Magnetic Patches: I. Identification

Solar and stellar magnetic patches (i.e., magnetic fluxes that reach the surface from the interior) are believed to be the primary sources of a star's atmospheric conditions. Hence, detecting and identifying these features (also known as magnetic elements) are among the essential topics in the community. Here, we apply the complex network approach to recognize the solar magnetic patches. For this purpose, we use the line-of-sight magnetograms provided by the Helioseismic and Magnetic Imager on board the Solar Dynamic Observatory. We construct the magnetic network following a specific visibility graph condition between pairs of pixels with opposite polarities and search for possible links between these regions. The complex network approach also provides the ability to rank the patches based on their connectivity (i.e., degree of nodes) and importance (i.e., PageRank). The use of the developed algorithm in the identification of magnetic patches is examined by tracking the features in consecutive frames, as well as making a comparison with the other approaches to identification. We find that this method could conveniently identify features regardless of their sizes. For small-scale (one or two pixels) features, we estimate the average of 8% false-positive and 1% false-negative errors.

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arXiv2022

Do Cellular Automaton Avalanche Models Simulate the Quasi-Periodic Pulsations of Solar Flares?

Quasi-periodic pulsations (QPPs) with various periods that originate in the underlying magnetohydrodynamic processes of the flaring structures are detected repeatedly in the solar flare emissions. We apply a 2D cellular automaton (CA) avalanche model to simulate QPPs as a result of a repetitive load/unload mechanism. We show that the frequent occurrence of magnetic reconnections in a flaring loop could induce quasi-periodic patterns in the detected emissions. We obtain that among 21070 simulated flares, 813 events endure over 50 seconds, scaled with the temporal resolution of the Yohkoh Hard X-ray Telescope, and about 70 percent of these rather long-lasting events exhibit QPPs. We also illustrate that the applied CA model provides a wide range of periodicities for QPPs. Furthermore, we observe the presence of multiple periods in nearly 50 percent of the cases applying the Lomb-Scargle periodogram. A lognormal distribution is fitted to the unimodal distribution of the periods as a manifestation of an underlying multiplicative mechanism that typifies the effect of the system's independent varying parameters. The global maximum of the periods' lognormal distribution is located at 29.29 seconds. We compare statistics of the simulated QPPs with parameters of the host flares and discuss the impacts of flare properties on QPPs' periods. Considering the intrinsic characteristic of CA models, namely the repetitive load/unload mechanism, and the obtained pieces of evidence, we suggest that CA models may generate QPPs. We also examine the applicability of the autoregressive integrated moving average models to describe the simulated and observational QPPs.

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arXiv2019

Energy Balance in Avalanche Models for Solar Flares

The distributions of solar flare energies and waiting times have not been described simultaneously by a single physical model, yet. In this research, we investigate whether recent avalanche models can describe the distributions for both the released energies and waiting times of flares in an active region. Flaring events are simulated using the modified Lu and Hamilton model (Lu and Hamilton (1991), ApJ, 380, 89) and also the optimized model (Farhang et al. (2018), ApJ, 859, 41). Applying a probability balance equation approach, we study the statistics of the simulated flaring events and investigate the origin of the observed power law in the flare frequency-size distribution. The results indicate that the power law originates in the distribution of transition rates (the distribution of the probabilities of transitions between different energies) rather than the distribution of the energy of the active region. It is also observed that the waiting-time distribution of simulated flaring events follows a q-exponential function which approximates a simple Poisson distribution.

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arXiv2019

Resonant absorption as a damping mechanism for the transverse oscillations of the coronal loops observed by SDO/AIA

Solar coronal loops represent the variety of fast, intermediate, and slow normal mode oscillations. In this study, the transverse oscillations of the loops with a few-minutes period and also with damping caused by the resonant absorption were analyzed using extreme ultraviolet (EUV) images of the Sun. We employed the 171 $Å$ data recorded by Solar Dynamic Observatory (SDO)/Atmospheric Imaging Assembly (AIA) to analyze the parameters of coronal loop oscillations such as period, damping time, loop length, and loop width. For the loop observed on 11 October 2013, the period and the damping of this loop are obtained to be 19 and 70 minutes, respectively. The damping quality, the ratio of the damping time to the period, is computed about 3.6. The period and damping time for the extracted loop recorded on 22 January 2013 are about 81 and 6.79 minutes, respectively. The damping quality is also computed as 12. It can be concluded that the damping of the transverse oscillations of the loops is in the strong damping regime, so resonant absorption would be the main reason for the damping.

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arXiv2018

Principle of Minimum Energy in Magnetic Reconnection in a Self-Organized Critical Model for Solar Flares

Solar flares are an abrupt release of magnetic energy in the Sun's atmosphere due to reconnection of the coronal magnetic field. This occurs in response to turbulent flows at the photosphere which twist the coronal field. Similar to earthquakes, solar flares represent the behavior of a complex system, and expectedly their energy distribution follows a power law. We present a statistical model based on the principle of minimum energy in a coronal loop undergoing magnetic reconnection, which is described as an avalanche process. We show the distribution of peaks for the flaring events in this self-organized critical system is scale-free. The obtained power law index 1.84 $ \pm $ 0.02 for the peaks is in good agreement with satellite observations of soft X-ray flares. The principle of minimum energy can be applied for general avalanche models to describe many other phenomena.

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