Improving Depth, Energy and Timing Estimation in PET Detectors with Deconvolution and Maximum Likelihood Pulse Shape Discrimination

In a scintillation detector, the light generated in the scintillator by a gamma interaction is converted to photoelectrons by a photodetector and produces a time-dependent waveform, the shape of which depends on the scintillator properties and the photodetector response. Several depth-of-interaction (DOI) encoding strategies have been developed that manipulate the scintillator's temporal response along the crystal length and therefore require pulse shape discrimination techniques to differentiate waveform shapes. In this work, we demonstrate how maximum likelihood (ML) estimation methods can be applied to pulse shape discrimination to better estimate deposited energy, DOI and interaction time (for time-of-flight (TOF) PET) of a gamma ray in a scintillation detector. We developed likelihood models based on either the estimated detection times of individual photoelectrons or the number of photoelectrons in discrete time bins, and applied to two phosphor-coated crystals (LFS and LYSO) used in a previously developed TOF-DOI detector concept. Compared with conventional analytical methods, ML pulse shape discrimination improved DOI encoding by 27% for both crystals. Using the ML DOI estimate, we were able to counter depth-dependent changes in light collection inherent to long scintillator crystals and recover the energy resolution measured with fixed depth irradiation (~11.5% for both crystals). Lastly, we demonstrated how the Richardson-Lucy algorithm, an iterative, ML-based deconvolution technique, can be applied to the digitized waveforms to deconvolve the photodetector's single photoelectron response and produce waveforms with a faster rising edge. After deconvolution and applying DOI and time-walk corrections, we demonstrated a 13% improvement in coincidence timing resolution (from 290 to 254 ps) with the LFS crystal and an 8% improvement (323 to 297 ps) with the LYSO crystal.

Sensitized Triplet-Triplet Annihilation in Nanostructured Polymeric Scintillators Allows for Pulse Shape Discrimination

Scintillating materials emit light when exposed to ionizing radiation or particles and are used for the detection of nuclear threats, medical imaging, high-energy physics, and other usages. For some of these applications, it is vital to distinguish neutrons and charged particles from γ-rays. This is achievable by pulse shape discrimination (PSD), a time-gated technique, which exploits that the scintillation kinetics can depend on the nature of the incident radiation. However, it proves difficult to realize efficient PSD with plastic scintillators, which have several advantages over liquid or crystalline scintillating materials, including mechanical robustness and shapeability. It is shown here that sensitive and rapid PSD is possible with nanostructured polymer scintillators that consist of a solid polymer matrix and liquid nanodomains in which an organic dye capable of triplet-triplet annihilation (TTA) is dissolved. The liquid nature of the nanodomains renders TTA highly efficient so that delayed fluorescence can occur at low energy density. The nanostructured polymer scintillators allow discriminating α particles, neutrons, and γ-rays with a time response that is better than that of commercial scintillators. Exploiting that the liquid nanodomains can facilitate energy transfer processes otherwise difficult to realize in solid polymers, an auxiliary triplet sensitizer is incorporated. This approach further increases the scintillator's sensitivity toward α particles and neutrons and other high-energy processes where localized interactions are involved.

Ratio-Based Pulse Shape Discrimination: Analytic Results for Gaussian and Poisson Noise Models

In experiments in a range of fields including fast neutron spectroscopy and astroparticle physics, one can discriminate events of interest from background events based on the shapes of electronic pulses produced by energy deposits in a detector. Here, I focus on a well-known pulse shape discrimination method based on the ratio of the temporal integral of the pulse over an early interval Xp and the temporal integral over the entire pulse Xt. For both event classes, for both a Gaussian noise model and a Poisson noise model, I present analytic expressions for the conditional distribution of Xp given knowledge of the observed value of Xt and a scaled energy deposit corresponding to the product of the full energy deposit and a relative yield factor. I assume that the energy-dependent theoretical prompt fraction for both classes are known exactly. With a Bayesian approach that accounts for imperfect knowledge of the scaled energy deposit, I determine the posterior mean background acceptance probability given the target signal acceptance probability as a function of the observed value of Xt. My method enables one to determine receiver-operating-characteristic curves by numerical integration rather than by Monte Carlo simulation for these two noise models.

Pulse Shape Discrimination of n/γ in Liquid Scintillator at PMT Nonlinear Region Using Artificial Neural Network Technique

Reactor-emitted electron antineutrinos can be detected via the inverse beta decay reaction, which produces a characteristic signal: a two-fold coincidence between a prompt positron event and a delayed neutron capture event within a specific time frame. While liquid scintillators are widely used for detecting neutrinos reacting with matter, detection is difficult because of the low interaction of neutrinos. In particular, it is important to distinguish between neutron (n) and gamma (γ) signals. The principle of the interaction of neutrons with matter differs from that of gamma rays with matter, and hence the detection signal's waveform is different. Conventionally, pulse shape discrimination (PSD) is used for n/γ separation. This study developed a machine learning method to see if it is more efficient than the traditional PSD method. The possibility of n/γ discrimination in the region beyond the linear response limits was also examined, by using 10- and 2-inch photomultiplier tubes (PMTs) simultaneously. To the best of our knowledge, no study has attempted PSD in a PMT nonlinear region using artificial neural networks. Our results indicate that the proposed method has the potential to distinguish between n and γ signals in a nonlinear region.

An Optimized SVR Algorithm for Pulse Pile-Up Correction in Pulse Shape Discrimination

Pulse pile-up presents a significant challenge in nuclear radiation measurements, particularly in neutron-gamma pulse shape discrimination, as it causes pulse distortion and diminishes identification accuracy. To address this, we propose an optimized Support Vector Regression (SVR) algorithm for correcting pulse pile-up. Initially, the Dung Beetle Optimizer (DBO) and Whale Optimization Algorithm (WOA) are integrated to refine the correction process, with performance evaluated using charge comparison methods (CCM) for pulse shape discrimination. Leveraging prior knowledge from simulated data, we further analyze the relationships between various types of pulse pile-ups, including their combinations, inter-peak distances, and the accuracy of corrections. Extensive experiments conducted in a mixed neutron-gamma radiation field using plastic scintillators demonstrate that the proposed method effectively corrects pulse pile-up and accurately discriminates between neutron and gamma. Moreover, our approach significantly improves the fidelity of pulse shape discrimination and enhances the overall reliability of radiation detection systems in high-interference environments.

Optimum lithium loading of a liquid scintillator for neutron and neutrino detection

Neutral particle detection in high-background environments is greatly aided by the ability to easily load ⁶Li into liquid scintillators. We describe a readily available and inexpensive liquid scintillation cocktail stably loaded with a Li mass fraction up to 1 %. Compositions that give thermodynamically stable microemulsions (reverse-micellar systems) were explored, using a Compton spectrum quenching technique to distinguish these from unstable emulsions. Scintillation light yield and transmittance were characterized. Pulse shape discrimination (PSD) was measured using a ²⁵²Cf source, showing that electron-like and proton-like recoil events are well-resolved even for Li loading up to 1 %, providing a means of background suppression in neutron/neutrino detectors. While samples in this work were prepared with ^nat Li (7.59 % ⁶Li), the neutron capture peak was clearly visible in the PSD spectrum; this implies that while extremely high capture efficiency could be achieved with ⁶Li-enriched material, a very inexpensive neutron-sensitive detector can be prepared with ^nat Li.