Identification of Neutrons Using Digitized Waveforms

Abstract

The advantages of performing neutron-tagging measurements using a waveform digitizer are explored. An existing analog setup consisting of modular crate electronics at the Source-Testing Facility at the Division of Nuclear Physics in Lund, Sweden has been digitally replicated. Neutrons are detected using an organic liquid-scintillator detector while the corresponding 4.44 MeV gamma-rays are detected using inorganic scintillation crystals. The performance of the digitizer-based setup is compared to that of the modular analog setup in terms of neutron and gamma-ray pulse-shape discrimination and time-of-flight. The results obtained using the digitizer-based approach are superior to those obtained using the modular analog approach in all aspects. The digitizer-based approach is then successfully employed both to distinguish between neutrons and gamma-rays via a convolutional neural network and to relate neutron deposition energy to neutron kinetic energy via time-of-flight.

Popular Abstract

Detecting Neutrons: What Information Should We record?
Well why not use it all?
Traditional modular electronics components are limited in flexibility and discard a lot of information. Waveform digitizers can be used to extract more information from signals and offer a greater degree of flexibility to the users.

Many physics related experiments use modular electronics components to process detector signals. Each module performs a specific task and much like LEGOs these components can be combined in a variety of ways to for example select pulses that fulfil certain criteria and then extract parameters such as charge in the pulse or time differences between pulses. This approach offers a great deal of flexibility but also comes with certain drawbacks.
At the Source-Testing Facility at the Division of Nuclear Physics in Lund, Sweden there is an experimental setup build out of such devices. The specific constellation of detectors and electronics makes it possible to measure the speed of neutrons produced by a radioactive source. Unfortunately it is cumbersome to optimize the setup for new detectors, since many hardware components need to be fine tuned. Aditionaly only a fraction of the information in the form of information on the timing and strength of signals is saved.
An alternative approach is to use a device called a waveform digitizer. When a particle interacts in one of the detectors it produce a pulse of current (moving electrons). The digitizer then takes snapshots of the strength of this pulse at regular time intervals. The figure below illustrates how the rate at which the pulse is sampled affects how well it can be reconstructed. Modern digitizers can take hundreds of millions or even billions of such snapshots per second, which means highly accurate representations of signals are available for analysis.
In this thesis such digitized snapshots of pulses are used to both replicate what can be done with the established modular crate electronics and to go beyond by training a neural network to distinguish between neutrons and highly energetic photons. The Digitizer is found to outperform the established experimental setup and to open up new possibilities for distinguishing particle species.

• author: Kjær Høier, Rasmus
• supervisor: Kevin Fissum; Hanno Perrey
• organization: Nuclear physics; Department of Physics
• course: FYSM60 20191
• year: 2019
• type: H2 - Master's Degree (Two Years)
• subject: Physics and Astronomy
• keywords: Digitizer, neutron, gamma-ray, pulse-shape discrimination, CNN, convolutional neural network, charge comparison, time of flight, source-testing facility, STF
• language: English
• id: 8986889
• date added to LUP: 2019-06-26 09:34:37
• date last changed: 2019-06-26 09:34:37