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Crosby, O.

doi:10.1002/9783527809080.cataz16494

Cited by 5 publications

(7 citation statements)

References 1 publication

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“…The correlation results for these two intervals clearly support a relaxation-oscillator (BUR) behavior in the "saturation" relationship, and conflict with the negative conclusions about waiting-time correlations by previous authors (Biesecker 1994;Crosby et al 1998;Hudson et al 1998;Wheatland 2000b). The difference may results from the systematic uncertainties in the databases in use or in methodology; as discussed below there are many ways to hide the presence of even a strong correlation such as we see in the two examples of Figures 5 and 6.…”

Section: An Interval-size Relationshipcontrasting

confidence: 89%

“…In spite of these systematic errors and unknowns, this study has found a significant correlation by using only the simplest possible flare data, namely the SolarSoft summaries of GOES soft X-ray event time, magnitude, and location. At least two previous careful searches for interval-size relationships found none, with Crosby et al (1998) stating "No correlation is found between the elapsed time interval between successive flares arising from the same active region and the peak intensity of the flare." That study selected sequences of events from the same active region, but perhaps 16:00 20:00 00:00 04:00 08:00 12:00 16:00 Start Time (06-Dec-06 12:30:00) Table 1).…”

Section: Why Was This Correlation Not Found Earlier?mentioning

confidence: 99%

“…Many proxies for flare energy exist, and we are helped by the tendency of many of the extensive parameters to scale together (one manifestation of the "Big Flare Syndrome"; Kahler, 1982). In previous efforts to establish a BUR correlation, Biesecker et al (1994) used hard X-ray observations from the BATSE experiment (Fishman et al 1992), Hudson et al (1998) used the GOES soft X-ray observations, and both Crosby et al (1998) and Wheatland (2000a) used data from the WATCH monitor of hard Xrays, as described in an early version by Lund (1981). The relationship of any of these proxies to the total flare energy certainly contains some variance, and none represents a large fraction of the total.…”

Section: Introductionmentioning

confidence: 99%

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A Correlation in the Waiting-time Distributions of Solar Flares

Hudson

2019

Monthly Notices of the Royal Astronomical Society

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In isolated solar active regions, we find that the waiting times between flares correlate with flare magnitudes as determined by the GOES soft X-ray fluxes. A "build-up and release" scenario (BUR) for magnetic energy storage in the solar corona suggests the existence of such a relationship, relating the slowly varying subphotospheric energy sources to the sudden coronal energy releases of flares and CMEs. Substantial amounts of research effort had not previously found any obvious observational evidence for such a BUR process. This has posed a puzzle since coronal magnetic energy storage represents the consensus view of the basic flare mechanism. We have revisited the GOES soft X-ray flare statistics for any evidence of correlations, using two isolated active regions, and have found significant evidence for a "saturation" correlation. Rather than a "reset" form of this relaxation, in which the time before a flare correlates with its magnitude, the "saturation" relationship results in the time after the flare showing the correlation. The observed correlation competes with the effect of reduced GOES sensitivity, in which weaker events can be under-reported systematically. This complicates the observed correlation, and we discuss several approaches to remedy this.

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Section: An Interval-size Relationshipcontrasting

confidence: 89%

Section: Why Was This Correlation Not Found Earlier?mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Correlation in the Waiting-time Distributions of Solar Flares

Hudson

2019

Monthly Notices of the Royal Astronomical Society

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“…The core reasons for this may be multiple: first, we only rely on photospheric magnetic field data, but flares occur above the line-tied photosphere in the low solar corona. Second, flares may well be intrinsically stochastic phenomena, as adopted in a long-standing working hypothesis (Rosner and Vaiana 1978), shown conclusively by the flares' time-dependent Poisson waiting times (Crosby et al 1998;Wheatland and Litvinenko 2002) and interpretted physically via the concept of self-organized criticality (Lu and Hamilton 1991;Lu et al 1993;Vlahos et al 1995) -see also Aschwanden et al (2016) for a comprehensive review.…”

Section: Discussionmentioning

confidence: 99%

Feature Ranking of Active Region Source Properties in Solar Flare Forecasting and the Uncompromised Stochasticity of Flare Occurrence

Campi

Benvenuto

Massone

et al. 2019

ApJ

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Solar flares originate from magnetically active regions but not all solar active regions give rise to a flare. Therefore, the challenge of solar flare prediction benefits by an intelligent computational analysis of physics-based properties extracted from active region observables, most commonly line-ofsight or vector magnetograms of the active-region photosphere. For the purpose of flare forecasting, this study utilizes an unprecedented 171 flare-predictive active region properties, mainly inferred by the Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) in the course of the European Union Horizon 2020 FLARECAST project. Using two different supervised machine learning methods that allow feature ranking as a function of predictive capability, we show that: i) an objective training and testing process is paramount for the performance of every supervised machine learning method; ii) most properties include overlapping information and are therefore highly redundant for flare prediction; iii) solar flare prediction is still -and will likely remain -a predominantly probabilistic challenge. Nishizuka et al 2018) within the recent field of space weather forecasting that relies on the availability of two ingredients; one observational and one computational. First, it is well-established that solar active regions (ARs) exclusively host major flares and therefore flare prediction needs experimental data on AR properties, associated to the photospheric and coronal magnetic field; however, coronal information has only recently started being used in the form of EUV images given as input to a deep learning network by Nishizuka et al (2018). Second, this information on AR magnetic properties can be processed for prediction purposes by means of a computational method for data analysis; machine learning has recently offered strong candidates for such methods.Since February 2010, the Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) (Scherrer et al 2012) is providing both line-of-sight and vector magnetograms of the full solar disk at a (vector magnetogram) cadence of 12 minutes. SDO/HMI magnetograms can be used for solar flare prediction according to two different approaches. First, HMI magnetograms are utilized to calculate a variety of properties either from the line-of-sight component only, from the radial component only, or from all three vector components. Various single-valued quantities, hereafter referred to as features, can be calculated from these property images through a variety of techniques (e.g., thresholding, feature recognition, etc), such that calculation of one physical property may provide multiple features as inputs to machine learning (i.e., image maximum, total, and moments). Of course, additional features that are not derived from property images may also contribute to the input dataset. Second, from a deep learning perspective, HMI images can be given as input to Convolutional Neural Networks (CNNs) that automatically perform a probabilistic f...

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“…Simply put, energy is stored in the solar corona with an exponential growth rate, which produces a power law distribution of flare energies, if the time intervals between two subsequent flares are governed by a stochastic (random) waiting time distribution. However, the postulated correlation between the waiting time and the intermittently released flare energies has never been confirmed by observational statistics in solar flares (Crosby et al 1998;Wheatland 2000;Lippiello et al 2010), and time scale arguments were brought forward that indicate that such a storage model is inconsistent with solar flare energy storage in coronal magnetic fields (Lu 1995).…”

Section: Coronal Energy Storagementioning

confidence: 99%

Global Energetics of Solar Flares. IX. Refined Magnetic Modeling

Aschwanden¹

2019

ApJ

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A more accurate analytical solution of the vertical-current approximation nonlinear force-free field (VCA3-NLFFF) model is presented that includes besides the radial (B r ) and the azimuthal (B ϕ ) magnetic field components a poloidal component (B θ = 0) also. This new analytical solution is of second-order accuracy in the divergence-freeness condition, and of third-order accuracy in the force-freeness condition. We re-analyze the sample of 173 GOES M-and X-class flares observed with the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). The new code reproduces helically twisted loops with a low winding number below the kink instability consistently, avoiding unstable, highly-twisted structures of the Gold-Hoyle flux rope type. The magnetic energies agree within E V CA3 /E W = 0.99 ± 0.21 with the Wiegelmann (W-NLFFF) code. The time evolution of the magnetic field reveals multiple, intermittent energy build-up and releases in most flares, contradicting both the Rosner-Vaiana model (with gradual energy storage in the corona) and the principle of time scale separation (τ f lare ≪ τ storage ) postulated in self-organized criticality models. The mean dissipated flare energy is found to amount to 7% ± 3% of the potential energy, or 60% ± 26% of the free energy, a result that can be used for predicting flare magnitudes based on the potential field of active regions.Subject headings: Sun: Corona -Sun: Magnetic Fields 1.

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N , N , N′ , N′ ‐tetrakis(diphenylphosphino)ethylene diamine

Cited by 5 publications

References 1 publication

A Correlation in the Waiting-time Distributions of Solar Flares

A Correlation in the Waiting-time Distributions of Solar Flares

Feature Ranking of Active Region Source Properties in Solar Flare Forecasting and the Uncompromised Stochasticity of Flare Occurrence

Global Energetics of Solar Flares. IX. Refined Magnetic Modeling

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