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- W4226314231 abstract "A key assumption in quasar absorption line studies of the circumgalactic medium (CGM) is that each absorption component maps to a spatially isolated cloud structure that has single valued properties (e.g. density, temperature, metallicity). We aim to assess and quantify the degree of accuracy underlying this assumption. We used adaptive mesh refinement hydrodynamic cosmological simulations of two $z=1$ dwarf galaxies and generated synthetic quasar absorption-line spectra of their CGM. For the SiII $lambda 1260$ transition, and the CIV $lambdalambda1548, 1550$ and OVI $lambdalambda1031, 1037$ fine-structure doublets, we objectively determined which gas cells along a line-of-sight (LOS) contribute to detected absorption. We implemented a fast, efficient, and objective method to define individual absorption components in each absorption profile. For each absorption component, we quantified the spatial distribution of the absorbing gas. We studied a total of 1,302 absorption systems containing a total of 7,755 absorption components. 48% of SiII, 68% of CIV, and 72% of OVI absorption components arise from two or more spatially isolated cloud structures along the LOS. Spatially isolated cloud structures were most likely to have cloud-cloud LOS separations of 0.03$R_{vir}$, 0.11$R_{vir}$, and 0.13$R_{vir}$ for SiII, CIV, and OVI, respectively. There can be very little overlap between multi-phase gas structures giving rise to absorption components. If our results reflect the underlying reality of how absorption lines record CGM gas, they place tension on current observational analysis methods as they suggest that component-by-component absorption line formation is more complex than is assumed and applied for chemical-ionisation modelling." @default.
- W4226314231 created "2022-05-05" @default.
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- W4226314231 date "2022-02-24" @default.
- W4226314231 modified "2023-09-25" @default.
- W4226314231 title "Examining quasar absorption-line analysis methods: the tension between simulations and observational assumptions key to modelling clouds" @default.
- W4226314231 doi "https://doi.org/10.48550/arxiv.2202.12228" @default.
- W4226314231 hasPublicationYear "2022" @default.
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