Orateur
Description
The neutrino telescopes KM3NeT search for cosmic neutrinos from distant
astrophysical sources such as supernovae, gamma ray bursters or
colliding stars flaring blazars. Once the events are received, they are
rapidly reconstructed online. The online events must be classified to
identify signal neutrinos from atmospheric muon background events.
Dedicated applications will then analyse the neutrino sample to look for
correlation with astrophysical sources and so that to send neutrino alerts
to the astro community.
The initial pipeline was running the reconstruction in C++ and classifying
the events in Python. The classification model has been trained with
LightGBM, a gradient boosting framework. To simplify the pipeline, I
integrated ONNX Runtime in the reconstruction code. The LightGBM model has
been converted in ONNX format. I first compared the results of LightGBM
with ONNX runtime in Python. Then, C++ implementation has been done and
the new pipeline is now running in production.
