Published January 1, 2017
| Version v1
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Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber
Creators
- 1. Fermilab Natl Accelerator Lab, Batavia, IL 60510 USA
- 2. Yale Univ, New Haven, CT 06520 USA
- 3. IIT, Chicago, IL 60616 USA
- 4. Univ Texas Arlington, Arlington, TX 76019 USA
- 5. Univ Bern, CH-3012 Bern, Switzerland
- 6. Univ Oxford, Oxford OX1 3RH, England
- 7. TUBITAK Space Technol Res Inst, METU Campus, TR-06800 Ankara, Turkey
- 8. Brookhaven Natl Lab, Upton, NY 11973 USA
- 9. Univ Lancaster, Lancaster LA1 4YW, England
- 10. Kansas State Univ, Manhattan, KS 66506 USA
- 11. MIT, Cambridge, MA 02139 USA
- 12. Columbia Univ, New York, NY 10027 USA
Description
We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.
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