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Volume 357 - The New Era of Multi-Messenger Astrophysics (Asterics2019) - Main session
pLISA: a parallel Library for Identification and Study of Astroparticles and its application to KM3NeT
C. Bozza,* C. De Sio, R. Coniglione
*corresponding author
Full text: pdf
Pre-published on: 2019 August 08
Published on:
Abstract
INFN has produced a Machine Learning library in Python that applies Convolutional Neural
Networks to various common problems in the field of astroparticle identification and study in
suitable detectors. The library itself makes few assumptions and has few requirements that are
easily met in most astroparticle detectors. The Parallel Library for Identification and Study of
Astroparticles (pLISA) has been tested against simulated events for the ARCA detector of the
KM3NeT Collaboration. Interesting preliminary results have been obtained for up/down-going
particle classification, muon/electron neutrino classification, Z component of the direction and
energy estimation. Already with very little optimization work and using limited hardware
resources (one NVidia GTX GPU), pLISA was shown to compete with traditional algorithms.
The approach allows improvements and also portability to other detectors. pLISA is based on
commonly used open source frameworks, which helps ensuring portability and scalability.
Open Access
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