Browsing by Author "Zhang, Yichao"
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Item FFLO Superfluids in 2d Spin-Orbit Coupled Fermi Gases(2014-10-07) Zheng, Zhen; Gong, Ming; Zhang, Yichao; Zou, Xubo; Zhang, Chuanwei; Guo, Guangcan; Zhang, ChuanweiWe show that the combination of spin-orbit coupling and in-plane Zeeman field in a two-dimensional degenerate Fermi gas can lead to a larger parameter region for Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) phases than that using spin-imbalanced Fermi gases. The resulting FFLO superfluids are also more stable due to the enhanced energy difference between FFLO and conventional Bardeen-Cooper-Schrieffer (BCS) excited states. We clarify the crucial role of the symmetry of Fermi surface on the formation of finite momentum pairing. The phase diagram for FFLO superfluids is obtained in the BCS-BEC crossover region and possible experimental observations of FFLO phases are discussed.Item Providing Wavelength Resolved Irradiance Measurements by Using Machine Learning(2021-12-01T06:00:00.000Z) Zhang, Yichao; Biewer, Michael C.; Lary, David J.; Stoneback, Russell; Lou, Xinchou; Anderson, Phillip C.; Da Silveira Rodrigues, FabianoSunlight incident on the Earth’s atmosphere is essential for life and is the driving force for atmospheric photo-chemistry. Atmospheric photo-chemistry is central to understanding urban air quality and the host of associated human health impacts. In this dissertation, two solutions were proposed to address the current lack of real-time wavelength-resolved solar irradiance data across cities. Our first solution is based on the machine learning calibration of low-cost light sensors. These calibrated sensors have a strong performance and can be readily deployed at scale across dense urban environments to measure the wavelength resolved irradiance on a neighborhood scale. This work has been published in MDPI (Zhang et al., 2021). Our second solution is based on the comprehensive dataset from public environmental sensors. We developed another machine learning model to estimate the wavelength resolved solar irradiance from solar zenith angle, earth distance, and multiple environmental dataset, such as relative humidity, total column ozone, earth surface reflectance, and radar reflectivities in the sky. All these factors can be accessed from the public datasets of weather stations and remote sensing systems. Using this solution, wavelength resolved solar irradiance can be estimated in a neighborhood scale, without implementing any additional sensors.