New astronomy in the age of Big Data

是什么神秘的暗能量驱动着宇宙的明显加速？***宇宙中命名相似但截然不同的暗物质是什么？它如何改变我们周围宇宙中的星系、星系团和整体结构？***在天空中发出的明亮但转瞬即逝的无线电爆发的本质是什么？我们如何将这些信号与本地人造无线电信号区分开来，这些信号在天体物理学上并不有趣？******这些看似毫无关联的话题由于它们对大（宇宙学）数据的需求而联系在一起。我的工作使用令人惊讶的解决方案和技术应用于这些数据来回答这些关于宇宙的问题。******我们关于宇宙的标准宇宙学模型很简单。然而，被认为推动宇宙加速的暗能量仍然是一个谜。探测暗能量的一种方法是通过观察Ia型超新星（SNe Ia），这是一种明亮的恒星，在整个宇宙中以大约标准的亮度爆炸，使它们成为宇宙的信标。我们使用超新星Ia来测量距离，揭示宇宙的加速膨胀，这是由于神秘的暗能量成分的负压而发生的。利用LSST，我们可以探测宇宙时间内暗能量的特性。LSST将产生的惊人数据量也将包含其他闯入者：在宇宙学上没有用处的明亮物体。我开创了对这些物体进行分类和分离的关键技术，然后使用统计（概率）置信度，即该物体是有用的超新星Ia，来衡量我们的宇宙学约束。******一个相关的问题是暗物质究竟是什么。我从理论上模拟轴子，一个有希望的暗物质候选者，物理上受弦理论的启发。轴子实际上可以模仿暗物质或暗能量，这取决于它们的质量（以电子伏特或eV的单位表示）。轴子通过在小尺度上抑制结构的形成来“洗掉”宇宙中的结构。随着对CMB的测量在小尺度上变得更加精确，我们的轴子约束几乎增加了十倍。我的团队致力于约束轴子，并以此约束宇宙的“暗区”。******除了暗物质和暗能量的关键宇宙学问题之外，我正在解决理解“快速射电暴”这一新的神秘现象背后的物理学这一关键挑战。我们不知道它们是合并的中子星，还是像宇宙超辐射这样的外来物质。将它们分类到不同的统计组中对于揭示它们的潜在属性和性质至关重要。通过将新的分类技术直接应用于ARO数据的稳定流，我们将为下一代仪器构建尖端的统计分类器。******我的工作汇集了理论，数据和统计工具，以开发新的数据驱动的宇宙学时代。

What is the mysterious dark energy that drives the apparent acceleration of the universe?***What could the similarly named but distinct dark matter in the universe be, and how does it change the galaxies, clusters of galaxies and overall structure in the universe around us?***What is the nature of the bright-but-fleeting radio bursts that go off in the sky, and how can we separate those signals from local man-made radio signals that are not astrophysically interesting?******These seemingly unconnected topics are linked by their requirement for big (cosmological) data. My work uses surprising solutions and techniques applied to these data to answer these questions about the cosmos. ******Our standard cosmological model of the universe is a simple one. However, the dark energy that is believed to drive cosmic acceleration remains a mystery. One way to probe dark energy is through observations of Type Ia Supernovae (SNe Ia), brilliant stars that explode with a roughly standard brightness throughout the universe, allowing them to act as cosmic beacons. We use SNe Ia to measure distances, revealing the universe's accelerating expansion, which occurs due to the negative pressure of the mysterious dark energy component. Using LSST, we can probe dark energy properties over cosmic time. The spectacular amount of data that LSST will generate will also contain other interlopers: bright objects that are not useful cosmologically. I am pioneering critical techniques to classify and separate these objects, and then to use the statistical (probabilistic) confidence that the object is a useful SNe Ia, to weight our cosmological constraints.******A related problem is the riddle of what the dark matter could be. I theoretically model the axion, one promising dark matter candidate, physically motivated by string theory. Axions can actually mimic dark matter or dark energy, depending on their mass (expressed in units of electron volt or eV). Axions`wash out' structure in the universe by suppressing structure formation on small scales. As measurements of the CMB become more precise on small scales, our axion constraints increase by almost ten-fold. My team works to constrain axions and in so doing, constrain the 'dark sector' of the universe.******In addition to the key cosmological problems of dark matter and dark energy, I am tackling the critical challenge of understanding the physics behind the new mysterious phenomenon of "fast radio bursts". We don't know if they are merging neutron stars, or something exotic like cosmic superradiance. Classifying them into different statistical groups is critical to uncover their underlying properties and nature. By applying novel classification techniques approaches directly to the steady stream of ARO data, we will build cutting-edge statistical classifiers for next-generation instruments.******My work brings together theory, data and statistical tools to exploit the new data-driven cosmological epoch.