Development & Implementation of the Trigger for a Short-baseline Reactor Antineutrino Experiment (SoLid)
This work addresses the problem of efficient data handling and signal enhancement for physicists conducting short-baseline neutrino experiments, though it appears incremental as it applies existing methods to a specific experimental setup.
The paper tackles the challenge of real-time background and noise rejection in the SoLid reactor antineutrino experiment, which is crucial for improving the signal-background ratio and managing data rates, by implementing a firmware trigger on an FPGA using machine learning methods.
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.