Browsing by Author "Xu, Xinfeng"

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    How Do Quasars Impact Their Host Galaxies? From the Studies of Quasar Outflows in Absorption and Emission
    Xu, Xinfeng (Virginia Tech, 2020-05-27)
    "Quasar-mode feedback" occurs when momentum and energy from the environment of accreting supermassive black hole couple to the host galaxy. One mechanism for such a coupling is by high-velocity (up to ~0.2c) quasar-driven ionized outflows, appearing as blue-shifted absorption and emission lines in quasar spectra. Given enough energy and momentum, these outflows are capable of affecting the evolution of their host galaxies. This dissertation presents the studies of emission and absorption quasar outflows from different perspectives. (1). By conducting large broad absorption line (BAL) quasar surveys in both Sloan Digital Sky Survey and Very Large Telescopes (VLT), we determined various physics properties of quasar absorption outflows, e.g., the electron number density ((ne), the distance of outflows to the central quasar (๐‘…), and the kinetic energy carried by the outflow (๐ธฬ‡k). We demonstrated that half of the typical BAL outflows are situated at ๐‘… > 100 pc, i.e., having the potential to affect the host galaxies. (2). Our group carried out a Hubble Space Telescope program (PI: Arav) for studying the outflows in the Extreme-UV, collaborating with Dr. Gerard Kriss from Space Telescope Science Institute (STScI). We developed a novel method to fit the multitude of quasar absorption troughs efficiently and accurately. We have identified the most energetic quasar-driven outflows on record and discovered the largest acceleration and velocity-shift for a quasar absorption outflow. (3). By using the VLT data, Xu led the project to study the relationships between BAL outflows and emission line outflows. We found possible connections between these two types of quasar outflows, e.g., the luminosity of the [๐‘‚III ฮป5007 emission profile decreases with increasing ne derived from the BAL outflow in the same quasar. These findings are consistent with BAL and emission outflows being different manifestations of the same wind, and the observed relationships are likely a reflection of the outflow density distribution.
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    Modeling and Predicting Incidence: Critical Systems Failures and Flu Infection Cases
    Xu, Xinfeng (Virginia Tech, 2019-03-26)
    Given several related critical infrastructure (CI) networks, such as power grid, transportation, and water systems, one crucial question emerges: how to model the propagation of failed facilities and predict their spread over time to the whole system? Given digital surveillance data, can we predict the impact of Influenza-Like Illness (ILI), including the percentage of outpatient doctors visits, the season duration, and peak? These two questions are related to modeling and predicting the incidence of different types of contagions. In the case of CI, the contagions are the failures of facilities. In the case of flu spread, the contagions are the infective ILI. In this thesis, in the case of CI, we give a novel model of failure cascades and use it to identify key facilities in an optimization-based approach, called HotSpots. In the case of flu spread, we develop a deep neural network, EpiDeep, to predict multiple key epidemiology metrics. In both of these applications, we use the dynamics of propagation to develop better approaches. By collaborating with Oak Ridge National Laboratory (ORNL) and working on the real CI networks provided by them, we find that HotSpots helps solve what-if scenarios. By using the digital surveillance data reported by the Centers for Disease Control and Prevention (CDC), we carry on experiments and find that EpiDeep is better than non-trivial baselines and outperforms them by up to 40%. We believe the generality of our approaches, and it can be applied to other propagation-based scenarios in infrastructure and epidemiology.