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

Applying Deep Learning in MicroBooNE

Aug 1, 2017, 1:55 PM
1 East (Fermi National Accelerator Laboratory)

1 East

Fermi National Accelerator Laboratory

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


Taritree Wongjirad (MIT)


Deep learning algorithms, which have emerged over the last decade, are opening up new ways to analyze data for many particle physics experiments. The MicroBooNE experiment, which is a neutrino experiment at Fermilab, has been exploring the use of such algorithms, in particular convolutional neural networks (CNNS). CNNs are the state-of-the-art method for a large class of problems requiring the analysis of image data. This makes CNNs an attractive approach as the MicroBooNE detector is a liquid argon time projection chamber, which produces high-resolution images of particle interactions. In this talk, I will discuss the ways CNNs can be applied to tasks like neutrino interaction detection and particle identification in MicroBooNE.

Primary author


Dr Kazuhiro Terao (Nevis Laboratories, Columbia University)

Presentation materials