Deep learning for analysing synchrotron data streams

Boyu Wang, Ziqiao Guan, Shun Yao, Hong Qin, Minh Hoai Nguyen, Kevin Yager, Dantong Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

The National Synchrotron Light Source II (NSLS-II) at Brookhaven National Laboratory (BNL) is now providing some of the world's brightest X-ray beams. A suite of imaging and diffraction methods, exploiting megapixel detectors with kilohertz frame-rates at NSLS-II beamlines, generate a variety of image streams in unprecedented velocities and volumes. A complete understanding of a complex material system often requires a cluster of X-ray characterization tools that can reveal its elemental, structural, chemical and physical properties at different length-scales and time-scales. The flourish and continuing refinement of X-ray probes enable that the same sample may be studied with different perspectives and granularities, and at different time and locations; these powerful tools generate a correspondingly daunting big data challenge, with multiple image streams that outpaces any manual efforts and traditional data analysis practice. In this paper, we applied deep learning methods, in particular, deep convolutional neural network (CNN) to automatically recognize image features from image streams from NSLS-II, and integrated our deep-learning methods into the Google Tensorflow to cluster and label both real and synthetic 2-D scattering image patterns. These methods would empower scientists by providing timely insights, allowing them to steer experiments efficiently during their precious X-ray beamtime allocation. Experiment shows that the CNN-based image labeling attains a 10% improvement over traditional K-mean and Support Vector Machine.

Original languageEnglish (US)
Title of host publication2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467390514
DOIs
StatePublished - Nov 17 2016
Externally publishedYes
Event2016 New York Scientific Data Summit, NYSDS 2016 - New York, United States
Duration: Aug 14 2016Aug 17 2016

Publication series

Name2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings

Other

Other2016 New York Scientific Data Summit, NYSDS 2016
CountryUnited States
CityNew York
Period8/14/168/17/16

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications
  • Computer Science Applications

Keywords

  • CNN
  • Deep learning
  • X Ray Image Classification

Fingerprint Dive into the research topics of 'Deep learning for analysing synchrotron data streams'. Together they form a unique fingerprint.

Cite this