Bulletin of the American Physical Society
APS April Meeting 2018
Volume 63, Number 4
Saturday–Tuesday, April 14–17, 2018; Columbus, Ohio
Session R05: The Bold New Era of Big Data & Machine LearningInvited
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Sponsoring Units: DAP DCOMP Chair: Alexei Bazavov, Michigan State University Room: A123-125 |
Monday, April 16, 2018 10:45AM - 11:21AM |
R05.00001: Exploring the universe with artificial intelligence Invited Speaker: Kevin Schawinski Astrophysics suffers from an extreme case of time scale mismatch between human lives (1e2 years) to galaxies (1e8-9 years), making experiments impractical. In order to build a physics-based account of how galaxies formed and evolved, we need to be able to forward model the processes involved. I will present some of the ways in which my group attempts to do this using generative models. These models allow us to explore how astrophysical objects change in a data-driven way. I will also show how we can use techniques from machine learning to extract more information from existing data, and how we can use reinforcement learning to better run and exploit observational facilities. [Preview Abstract] |
Monday, April 16, 2018 11:21AM - 11:57AM |
R05.00002: Machine Learning in the LIGO-Virgo Era Invited Speaker: Kai Staats As LIGO-Virgo moved from the first to the second observation run, 2015-17, the rapid maturation of Machine Learning (ML) algorithms industry-wide enabled an increasing number of researchers to engage in a diversity of applied ML projects at the LIGO Scientific and Virgo Collaborations. Furthermore, multiple detection events have enabled a transition from simulated signals to a more robust landscape of real data analysis and note-worthy results. Currently several areas of ML research are being pursued by LV researchers, including: a means to both classify and locate the source of transient artifacts known as glitches; tested localization of desired signal as produced by coalescing binary black holes and neutron stars; as a single-detector case for supernovae; and as a potential, future means to lock an interferometer. The algorithms employed include Random Forest, Genetic Programming (GP), Convolutional Neural Networks, RNN Auto-encoders, Deep Filtering and Deep Regression. This talk will provide a comprehensive overview of the diverse applications of ML in the LIGO Scientific and Virgo Collaborations, the opportunity for engaging citizen scientists, and a deeper discussion of the application of GP to understand the origin of mechanical couplings in the detectors. [Preview Abstract] |
Monday, April 16, 2018 11:57AM - 12:33PM |
R05.00003: AI in the Sky: The Application of Artificial Intelligence to Cosmological Questions Invited Speaker: Brian Nord The increased availability of large data sets and advancements in artificial intelligence algorithms have revolutionized the role of data across industry, society, and the sciences. In the last few years, it has had substantial impact on molecular chemistry, particle physics, and more recently astronomy. AI (e.g., machine learning) is more than likely here to stay, and it has the potential to transform our approach to modeling cosmological and astrophysical data. But, what are these algorithms doing, and what are the critical barriers to enabling their highest impact on science? We'll discuss these topics in the context of modern astronomical surveys, which provide data sets that are unprecedented in size, precision, and complexity. In particular, recent work on the application of convolutional neural networks to strong gravitational lensing, the cosmic microwave background, and cosmological simulations point to the long-term promise for deep learning and its utility in answering fundamental questions about the universe. [Preview Abstract] |
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