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Register a new userThe Solar Flare Iron Abundance
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The abundance of iron is measured from emission line complexes at 6.65 keV (Fe line) and 8 keV (Fe/Ni line) in {em RHESSI} X-ray spectra during solar flares. Spectra during long-duration flares with steady declines were selected, with an isothermal assumption and improved data analysis methods over previous work. Two spectral fitting models give comparable results, viz. an iron abundance that is lower than previous coronal values but higher than photospheric values. In the preferred method, the estimated Fe abundance is $A({rm Fe}) = 7.91 pm 0.10$ (on a logarithmic scale, with $A({rm H}) = 12$), or $2.6 pm 0.6$ times the photospheric Fe abundance. Our estimate is based on a detailed analysis of 1,898 spectra taken during 20 flares. No variation from flare to flare is indicated. This argues for a fractionation mechanism similar to quiet-Sun plasma. The new value of $A({rm Fe})$ has important implications for radiation loss curves, which are estimated.
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The RESIK instrument on the CORONAS-F spacecraft obtained solar flare and active region X-ray spectra in four channels covering the wavelength range 3.8 -- 6.1 AA in its operational period between 2001 and 2003. Several highly ionized silicon lines were observed within the range of the long-wavelength channel (5.00 -- 6.05 AA). The fluxes of the sixiv Ly-$beta$ line (5.217 AA) and the sixiii $1s^2 - 1s3p$ line (5.688 AA) during 21 flares with optimized pulse-height analyzer settings on RESIK have been analyzed to obtain the silicon abundance relative to hydrogen in flare plasmas. As in previous work, the emitting plasma for each spectrum is assumed to be characterized by a single temperature and emission measure given by the ratio of emission in the two channels of GOES. The silicon abundance is determined to be $A({rm Si}) = 7.93 pm .21$ (sixiv) and $7.89 pm .13$ (sixiii) on a logarithmic scale with H = 12. These values, which vary by only very small amounts from flare to flare and times within flares, are $2.6 pm 1.3$ and $2.4 pm 0.7$ times the photospheric abundance, and are about a factor of three higher than RESIK measurements during a period of very low activity. There is a suggestion that the Si/S abundance ratio increases from active regions to flares.
Motivated by the controversy over the surface metallicity of the Sun, we present a re-analysis of the solar photospheric oxygen (O) abundance. New atomic models of O and Ni are used to perform Non-Local Thermodynamic Equilibrium (NLTE) calculations with 1D hydrostatic (MARCS) and 3D hydrodynamical (Stagger and Bifrost) models. The Bifrost 3D MHD simulations are used to quantify the influence of the chromosphere. We compare the 3D NLTE line profiles with new high-resolution, R = 700 000, spatially-resolved spectra of the Sun obtained using the IAG FTS instrument. We find that the O I lines at 777 nm yield the abundance of log A(O) = 8.74 +/- 0.03 dex, which depends on the choice of the H-impact collisional data and oscillator strengths. The forbidden [O I] line at 630 nm is less model-dependent, as it forms nearly in LTE and is only weakly sensitive to convection. However, the oscillator strength for this transition is more uncertain than for the 777 nm lines. Modelled in 3D NLTE with the Ni I blend, the 630 nm line yields an abundance of log A(O) = 8.77 +/- 0.05 dex. We compare our results with previous estimates in the literature and draw a conclusion on the most likely value of the solar photospheric O abundance, which we estimate at log A(O) = 8.75 +/- 0.03 dex.
The metal abundance of the hot plasma that permeates galaxy clusters represents the accumulation of heavy elements produced by billions of supernovae. Therefore, X-ray spectroscopy of the intracluster medium provides an opportunity to investigate the nature of supernova explosions integrated over cosmic time. In particular, the abundance of the iron-peak elements (chromium, manganese, iron and nickel) is key to understanding how the progenitors of typical type Ia supernovae evolve and explode. Recent X-ray studies of the intracluster medium found that the abundance ratios of these elements differ substantially from those seen in the Sun, suggesting differences between the nature of type Ia supernovae in the clusters and in the Milky Way. However, because the K-shell transition lines of chromium and manganese are weak and those of iron and nickel are very close in photon energy, high-resolution spectroscopy is required for an accurate determination of the abundances of these elements. Here we report observations of the Perseus cluster, with statistically significant detections of the resonance emission from chromium, manganese and nickel. Our measurements, combined with the latest atomic models, reveal that these elements have near-solar abundance ratios with respect to iron, in contrast to previous claims. Comparison between our results and modern nucleosynthesis calculations disfavours the hypothesis that type Ia supernova progenitors are exclusively white dwarfs with masses well below the Chandrasekhar limit (about 1.4 times the mass of the Sun). The observed abundance pattern of the iron-peak elements can be explained by taking into account a combination of near- and sub-Chandrasekhar-mass type Ia supernova systems, adding to the mounting evidence that both progenitor types make a substantial contribution to cosmic chemical enrichment.
We developed a solar flare prediction model using a deep neural network (DNN), named Deep Flare Net (DeFN). The model can calculate the probability of flares occurring in the following 24 h in each active region, which is used to determine the most likely maximum classes of flares via a binary classification (e.g., >=M class versus <M class or >=C class versus <C class). From 3x10^5 observation images taken during 2010-2015 by Solar Dynamic Observatory, we automatically detected sunspots and calculated 79 features for each region, to which flare occurrence labels of X-, M-, and C-class were attached. We adopted the features used in Nishizuka et al. (2017) and added some features for operational prediction: coronal hot brightening at 131 A (T>=10^7 K) and the X-ray and 131 A intensity data 1 and 2 h before an image. For operational evaluation, we divided the database into two for training and testing: the dataset in 2010-2014 for training and the one in 2015 for testing. The DeFN model consists of deep multilayer neural networks, formed by adapting skip connections and batch normalizations. To statistically predict flares, the DeFN model was trained to optimize the skill score, i.e., the true skill statistic (TSS). As a result, we succeeded in predicting flares with TSS=0.80 for >=M-class flares and TSS=0.63 for >=C-class flares. Note that in usual DNN models, the prediction process is a black box. However, in the DeFN model, the features are manually selected, and it is possible to analyze which features are effective for prediction after evaluation.
We study the solar wind helium-to-hydrogen abundances ($A_mathrm{He}$) relationship to solar cycle onset. Using OMNI/Lo data, we show that $A_mathrm{He}$ increases prior to sunspot number (SSN) minima. We also identify a rapid depletion and recovery in $A_mathrm{He}$ that occurs directly prior to cycle onset. This $A_mathrm{He}$ Shutoff happens at approximately the same time across solar wind speeds ($v_mathrm{sw}$), implying that it is formed by a mechanism distinct from the one that drives $A_mathrm{He}$s solar cycle scale variation and $v_mathrm{sw}$-dependent phase offset with respect to SSN. The time between successive $A_mathrm{He}$ shutoffs is typically on the order of the corresponding solar cycle length. Using Brightpoint (BP) measurements to provide context, we infer that this shutoff is likely related to the overlap of adjacent solar cycles and the equatorial flux cancelation of the older, extended solar cycle during Solar Minima.
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