New astronomy in the age of Big Data

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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.