July 31, 2017 to August 4, 2017
Fermi National Accelerator Laboratory
US/Central timezone

Exploring Computing Methods for Improved Cosmic Background Rejection in NOvA's Sterile Neutrino Searches

Aug 2, 2017, 11:35 AM
Hornets Nest (Fermi National Accelerator Laboratory)

Hornets Nest

Fermi National Accelerator Laboratory

Presentation Computing, Analysis Tools and Data Handling Computing, Analysis Tools, and Data Handling


Mr Shaokai Yang (university of Cincinnati)


We are witnessing a revolution happening in experimental HEP based on the current innovations in deep machine learning technologies. For example, Convolutional neural networks (CNNs) have been introduced to identify particle interactions in particle tracking detectors based on their topology without the need for detailed reconstruction and outperforms currently used algorithms. We are trying to further improve the performance of CNNs applied to the NOvA experiment by: training the algorithms to separate a single particle interaction from others (image/particle segmentation phase), classifying the specific interaction type for the separated interaction (image/particle classification phase), and finally, estimating the classification uncertainty. We are developing a new method to select particle interactions based on TensorFlow, a CNN framework released and supported by Google. In this talk, we will detail the algorithm and its performance when applied to NOvA simulation.

Primary author

Mr Shaokai Yang (university of Cincinnati)


Prof. Alexandre Sousa (University of Cincinnati) Enhao Song (University of Virginia) Dr Louise Suter (FNAL)

Presentation materials