  A study on the use of a machine learning algorithm for the level 1 trigger
decision in the JUNO experiment ispresented. JUNO is a medium baseline neutrino
experiment in construction in China, with the main goal of determining the
neutrino mass hierarchy. A large liquid scintillator (LS)volume will detect the
electron antineutrinos issued from nuclear reactors. The LS detector is
instrumented by around 20000 large photomultiplier tubes. The hit information
from each PMT will be collected into a center trigger unit for the level 1
trigger decision. The current trigger algorithm used to select a neutrino
signal event is based on a fast vertex reconstruction. We propose to study an
alternative level 1 (L1) trigger in order to achieve a similar performance as
the vertex fitting trigger but with less logic resources by using firmware
implemented machine learning model at the L1 trigger level. We treat the
trigger decision as a classification problem and train a Multi-Layer Perceptron
(MLP)model to distinguish the signal events with an energy higher than a
certain threshold from noise events. We use JUNO software to generate datasets
which include 100K physics events with noise and 100K pure noise events coming
from PMT dark noise.For events with energy higher than 100 keV, the L1 trigger
based on the converged MLP model can achieve an efficiency higher than 99%.
After the training performed on simulations,we successfully implemented the
trained model into a Kintex 7FPGA. We present the technical details of the
neural network development and training, as well as its implementation in the
hardware with the FPGA programming. Finally the performance of the L1 trigger
MLP implementation is discussed.
