Browsing by Author "Doughty, Caitlin"

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    Aligned metal absorbers and the ultraviolet background at the end of reionization
    (Oxford University Press, 2018) Doughty, Caitlin; Finlator, Kristian; Oppenheimer, Benjamin D.; Dave, Romeel; Zackrisson, Erik
    We use observations of spatially-aligned C ii, C iv, Si ii, Si iv, and O i absorbers to probe the slope and intensity of the ultraviolet background (UVB) at z ∼ 6. We accom- plish this by comparing observations with predictions from a cosmological hydrody- namic simulation using three trial UVBs applied in post-processing: a spectrally soft, fluctuating UVB calculated using multi-frequency radiative transfer; a soft, spatially- uniform UVB; and a hard, spatially-uniform “quasars-only” model. When considering our paired high-ionization absorbers (Civ/Siiv), the observed statistics strongly prefer the hard, spatially-uniform UVB. This echoes recent findings that cosmological sim- ulations generically underproduce strong C iv absorbers at z > 5. A single low/high ionization pair (Si ii/Si iv), by contrast, shows a preference for the HM12 UVB, while two more (C ii/C iv and O i/C iv) show no preference for any of the three UVBs. Despite this, future observations of specific absorbers, particularly Si iv/C iv, with next-generation telescopes probing to lower column densities should yield tighter con- ts on the UVB.
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    Constraining the astrophysics and cosmology from 21 cm tomography using deep learning with the SKA
    (Oxford University Press, 2020) Hassan, Sultan; Andrianomena, Sambatra; Doughty, Caitlin
    Future Square Kilometre Array (SKA) surveys are expected to generate huge data sets of 21 cm maps on cosmological scales from the Epoch of Reionization. We assess the viability of exploiting machine learning techniques, namely, convolutional neural networks (CNNs), to simultaneously estimate the astrophysical and cosmological parameters from 21 cm maps from seminumerical simulations. We further convert the simulated 21 cm maps into SKA-like mock maps using the detailed SKA antennae distribution, thermal noise, and a recipe for foreground cleaning. We successfully design two CNN architectures (VGGNet-like and ResNet-like) that are both efficiently able to extract simultaneously three astrophysical parameters, namely the photon escape fraction (fesc), the ionizing emissivity power dependence on halo mass (Cion), and the ionizing emissivity redshift evolution index (Dion), and three cosmological parameters, namely the matter density parameter (Ωm), the dimensionless Hubble constant (h), and the matter fluctuation amplitude (σ8), from 21 cm maps at several redshifts.