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The Laboratory Astrophysics program employing the Lawrence Livermore National Laboratory (LLNL) Electron Beam Ion Trap (EBIT) has been providing useful atomic data in support of the x-ray missions Chandra and XMM-Newton. Major achievements have been made for Fe L-shell ions in hot, collisional plasmas, relevant to stellar coronae, supernova remnants, elliptical galaxies, and galaxy clusters. Measurements for L-shell ions of other cosmiscally important elements are also required, some of which are in the LLNL EBIT pipeline. On the other hand, data for inner-shelll excited lines relevant to photoionized plasmas near accretion sources are largely lacking. Even the wavelengths of these lines are only poorly known, which severely limits their use for diagnostics, despite the great potential.
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We review the available atomic data used for interpreting and modeling X-ray observations. The applications for these data can be divided into several levels of detail, ranging from compilations which can be used with direct inspection of raw data, such as line finding lists, to synthetic spectra which attempt to fit to an entire observed dataset simultaneously. This review covers cosmic sources driven by both electron ionization and photoionization and touches briefly on planetary surfaces and atmospheres. We review all of this, the applications to X-ray astronomy, the available data, recommendations for astronomical users, and attempt to point out the applications where the shortcomings are greatest
The Atmospheric Imaging Assembly (AIA) and the Exteme-ultraviolet Variability Experiment (EVE) onboard the Solar Dynamics Observatory include spectral windows in the X-ray/EUV band. Accuracy and completeness of the atomic data in this wavelength range is essential for interpretation of the spectrum and irradiance of the solar corona, and of SDO observations made with the AIA and EVE instruments. Here we test the X-ray/EUV data in the CHIANTI database to assess their completeness and accuracy in the SDO bands, with particular focus on the 94A and 131A AIA passbands. Given the paucity of solar observations adequate for this purpose, we use high-resolution X-ray spectra of the low-activity solar-like corona of Procyon obtained with the Chandra Low Energy Transmission Grating Spectrometer (LETGS). We find that while spectral models overall can reproduce quite well the observed spectra in the soft X-ray range ll < 50A, and at the EUV wavelengths ll >130A, they significantly underestimate the observed flux in the 50-130A wavelength range. The model underestimates the observed flux by a variable factor ranging from approx 1.5, at short wavelengths below sim50A, up to approx5-7 in the sim 70-125A range. In the AIA bands covered by LETGS, i.e. 94A and 131A, we find that the observed flux can be underestimated by large factors (sim 3 and sim 1.9 respectively, for the case of Procyon presented here). We discuss the consequences for analysis of AIA data and possible empirical corrections to the AIA responses to model more realistically the coronal emission in these passbands.
With their brilliance and temporal structure, X-ray free-electron laser can unveil atomic-scale details of ultrafast phenomena. Recent progress in split-and-delay optics (SDO), which produces two X-ray pulses with time-delays, offers bright prospects for observing dynamics at the atomic-scale. However, their insufficient pulse energy has limited its application either to phenomena with longer correlation length or to measurement with a fixed delay-time. Here we show that the combination of the SDO and self-seeding of X-rays increases the pulse energy and makes it possible to observe the atomic-scale dynamics in a timescale of picoseconds. We show that the speckle contrast in scattering from water depends on the delay-time as expected. Our results demonstrate the capability of measurement using the SDO with seeded X-rays for resolving the dynamics in temporal and spatial scales that are not accessible by other techniques, opening opportunities for studying the atomic-level dynamics.
In this work, we characterize the performance of a deep convolutional neural network designed to detect and quantify chemical elements in experimental X-ray photoelectron spectroscopy data. Given the lack of a reliable database in literature, in order to train the neural network we computed a large ($>$100 k) dataset of synthetic spectra, based on randomly generated materials covered with a layer of adventitious carbon. The trained net performs as good as standard methods on a test set of $approx$ 500 well characterized experimental X-ray photoelectron spectra. Fine details about the net layout, the choice of the loss function and the quality assessment strategies are presented and discussed. Given the synthetic nature of the training set, this approach could be applied to the automatization of any photoelectron spectroscopy system, without the need of experimental reference spectra and with a low computational effort.
I will review the constraints set by X-ray measurements of afterglows on several issues of GRB, with particular regard to the fireball model, the environment, the progenitor and dark GRB.
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