Bulletin of the American Physical Society
2017 Fall Meeting of the APS Division of Nuclear Physics
Wednesday–Saturday, October 25–28, 2017; Pittsburgh, Pennsylvania
Session 1WA: Modern Machine Learning Methods in Data Analysis
9:00 AM–12:30 PM,
Wednesday, October 25, 2017
Room: Marquis A
Chair: Curtis Meyer, Carnegie Mellon University
Abstract: 1WA.00007 : Bridging the Particle Physics and Big Data Worlds
12:00 PM–12:30 PM
For decades, particle physicists have developed custom software because the scale and complexity of our problems were unique. In recent years, however, the "big data" industry has begun to tackle similar problems, and has developed some novel solutions. Incorporating scientific Python libraries, Spark, TensorFlow, and machine learning tools into the physics software stack can improve abstraction, reliability, and in some cases performance. Perhaps more importantly, it can free physicists to concentrate on domain-specific problems. Building bridges isn't always easy, however. Physics software and open-source software from industry differ in many incidental ways and a few fundamental ways. I will show work from the DIANA-HEP project to streamline data flow from ROOT to Numpy and Spark, to incorporate ideas of functional programming into histogram aggregation, and to develop real-time, query-style manipulations of particle data.
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