Characterizing charge state distributions in Fe photoionized plasma to test high density effects in astrophysical code XSTAR
ORAL
Abstract
Astrophysical plasma codes predict unreasonably high iron abundances in the photoionized plasmas found in about a dozen black hole accretion disks. It has been suggested that this discrepancy may stem from an underestimate of electron densities and subsequent neglect of high-density effects. One astrophysical model, XSTAR, was recently updated to incorporate physical mechanisms like continuum lowering which could significantly affect the emergent spectra at high densities. However, photoionized plasma codes have not been validated against observation due to the difficulty to produce photoionized plasmas in the lab. Using the Z-machine at Sandia National Laboratories, we developed a platform to collect emission and absorption spectra from photoionized Fe plasmas. Ultimately, our goal is to evaluate XSTAR's ability to accurately predict x-ray emission and absorption spectra. This will inform the accuracy of revisions to iron abundances inferred in black hole accretion disks. In addition to comparing model spectra against observations, one key intermediate component of the code we can test is the predicted charge state distribution. We perform fits to the observed absorption features using opacity cross sections calculated using the collisional-radiative code PrismSPECT to infer ionization fractions of the charge states achieved in the plasma. This method allows us to establish a measurement of the ionization fractions that is independent of the model assumptions. We will describe the motivation, approach, and preliminary results comparing observed and predicted charge state distributions.
SNL is managed and operated by NTESS under DOE NNSA contract DE-NA000352
SNL is managed and operated by NTESS under DOE NNSA contract DE-NA000352
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Presenters
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Isaac D Huegel
University of Texas at Austin
Authors
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Isaac D Huegel
University of Texas at Austin
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Patricia B Cho
University of Texas at Austin
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Daniel C Mayes
University of Nevada, Reno, University of Texas at Austin
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Guillaume P Loisel
Sandia National Laboratories