  SoLid, located at SCK-CEN in Mol, Belgium, is a reactor antineutrino
experiment at a very short baseline of 5.5 - 10m aiming at the search for
sterile neutrinos and for high precision measurement of the neutrino energy
spectrum of Uranium-235. It uses a novel approach using Lithium-6 sheets and
PVT cubes as scintillators for tagging the Inverse Beta-Decay products (neutron
and positron). Being located overground and close to the BR2 research reactor,
the experiment faces a large amount of backgrounds. Efficient real-time
background and noise rejection is essential in order to increase the
signal-background ratio for precise oscillation measurement and decrease data
production to a rate which can be handled by the online software. Therefore, a
reliable distinction between the neutrons and background signals is crucial.
This can be performed online with a dedicated firmware trigger. A peak counting
algorithm and an algorithm measuring time over threshold have been identified
as performing well both in terms of efficiency and fake rate, and have been
implemented onto an FPGA. After having introduced the experimental and
theoretical background of neutrino oscillation physics, as well as SoLid's
detector technology, read-out system and trigger scheme, the thesis presents
the design of the firmware neutron trigger implemented by applying machine
learning methods.
