Abstract
Neutrinos are one of the least known elementary particles. The detection of neutrinos is an extremely difficult task since they are affected only by weak subatomic force or gravity. Therefore, large detectors are constructed to reveal neutrino’s properties. Among them the Liquid Argon Time Projection Chamber (LAr-TPC) detectors provide excellent imaging and particle identification ability for studying neutrinos. The computerized methods for automatic reconstruction and identification of particles are needed to fully exploit the potential of the LAr-TPC technique. Herein, the novel method for electron neutrino classification is presented. The method constructs a feature descriptor from images of observed event. It characterizes the signal distribution propagated from vertex of interest, where the particle interacts with the detector medium. The classifier is learned with a constructed feature descriptor to decide whether the images represent the electron neutrino or cascade produced by photons. The proposed approach assumes that the position of primary interaction vertex is known. The method’s performance in dependency to the noise in a primary vertex position and deposited energy of particles is studied.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
CERN—European Organization for Nuclear Research.
- 2.
Coordinate system labeling is given for reference.
References
Anderson, C., et al.: The ArgoNeuT detector in the NuMI low-energy beam line at Fermilab. J. Instrum. 7, P10019 (2012)
Autiero, D., et al.: Large underground, liquid based detectors for astro-particle physics in Europe: scientific case and prospects. J. Cosmol. Astropart. Phys. 11, 011 (2007)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Ferrari, A., Sala, P.R., Fasso, A., Ranft, J.: FLUKA: a multi-particle transport code, CERN-2005-10, INFN TC 05 11, SLAC-R- 773 (2005)
Hastie, T., Friedman, J., Tibshirani, R.: The Elements of Statistical Learning. Springer, New York (2009)
Morgan, B.: Interest point detection for reconstruction in high granularity tracking detectors. J. Instrum. 5, P07006 (2010)
Płoński, P., Stefan, D., Sulej, R., Zaremba, K.: Image Segmentation in Liquid Argon Time Projection Chamber Detector. Lecture Notes in Artificial Intelligence, vol. 9119, pp. 606–615. Springer, Heidelberg (2015)
Rubbia, C.: The liquid-argon time projection chamber: a new concept for neutrino detectors. CERN report (1977)
Sulej, R.: Sterile neutrino search with the ICARUS T600 in the CNGS beam. XV Workshop on Neutrino Telescopes, PoS Neutel (2013)
The ICARUS Collaboration, Design, construction and tests of the ICARUS T600 detector, Nuclear Instruments and Methods in Physics Research, vol. A527 (2004)
The LBNE Collaboration, The long-baseline neutrino experiment–exploring fundamental symmetries of the universe, FERMILAB-PUB-14-022 (2014). arXiv:1307.7335
The MicroBooNE Collaboration, Proposal for a new experiment using the booster and NuMI neutrino beamlines: MicroBooNE, FERMILAB-PROPOSAL-0974 (2007)
Acknowledgments
PP and KZ acknowledge the support of the National Science Center (Harmonia 2012/04/M/ST2/00775). Authors are grateful to the ICARUS Collaboration and Polish Neutrino Group for useful suggestions and constructive discussions during a preliminary part of this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Płoński, P., Stefan, D., Sulej, R., Zaremba, K. (2016). Electron Neutrino Classification in Liquid Argon Time Projection Chamber Detector. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-26227-7_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26225-3
Online ISBN: 978-3-319-26227-7
eBook Packages: EngineeringEngineering (R0)