Pulse Shape Discrimination Algorithms Research Articles

Development of indigenous pulse-shape discrimination algorithm for organic scintillation detectors

In this study, we present the development of an indigenous pulse shape discrimination (PSD) algorithm grounded in tail area and total area analysis, effectively obviating the necessity for specialized programmable hardware. Pulse data was collected utilizing a BC501 detector paired with a Pu-Be source and digitized in oscilloscope mode during experiments conducted at IIT-Kanpur. The algorithm developed in this work encompasses essential functions such as pulse normalization, shaping, peak identification and removal, and threshold determination. Notably, the algorithm offers the capability to derive neutron and γ-ray counts, generate scatter plots, and calculate the figure-of-Merit (FoM). It is essential to emphasize that the development of this indigenous Tail-to-Total Area (TtoT) PSD algorithm was a direct response to the unavailability of dedicated hardware for PSD in the experiment conducted at IIT-Kanpur. The introduction of the TtoT algorithm played a pivotal role in enabling offline PSD analysis at IIT-Kanpur, effectively overcoming infrastructure limitations. Importantly, the efficacy of our proposed algorithm was tested by applying it to pulse data from a distinct source-detector arrangement featuring a BC501A detector and an Cf-252 source. This comparative analysis also included the Charge Integration (CI) method and was conducted at IIT-Roorkee. The results obtained from our algorithm and the CI method exhibited a strong concurrence concerning the identification of neutrons and γ-rays, along with FoM metrics.

Wavelet-based digital pulse-shape method for discrimination between neutron and gamma-rays with organic scintillation detectors

Organic scintillator detectors are central instruments in many applications involving fast neutrons. Many organic scintillator detectors exhibit pulse-shape discrimination (PSD) properties and are widely used to discriminate between neutron events and background gamma rays. PSD methods have been traditionally implemented using analog electronic circuits; however, in recent years, digital techniques have proven to outperform analog methods in areas such as count rate capability. Many digital PSD algorithms with various levels of achievement have been proposed, and those based on the wavelet transform of digitized scintillation pulses have shown great promise for operation at high event rates, where the pulse pile-up effect limits the performance of PSD methods. However, the proposed wavelet-based PSD methods involve intricate calculations that limit their practical use. In this work, we describe a modified version of the wavelet-based PSD methods that offers significant simplification of the PSD process while still producing excellent PSD performance. The method employs the Haar wavelet transform, which is the simplest available wavelet function and is easily implemented on digital hardware, such as field-programmable gate arrays (FPGA). We describe the details of the method, and different aspects of its performance are experimentally demonstrated using an experimental setup comprising a NE213 liquid scintillation detector. A figure-of-merit (FOM) of 1.47 ± 0.07 is achieved with an energy threshold of 500 keVee (electron equivalent energy). An excellent FOM value (1.32) is achieved with a short pulse processing window of only 26 ns, indicating the resilience of the method against the pulse pile-up effect.

Optimization of a BEGe Detector Setup for Testing Quantum Foundations in the Underground LNGS Laboratory

In this work, we report on tests performed with an experimental apparatus prototype based on a broad-energy germanium detector aimed at investigating topical, foundational issues in quantum mechanics: i.e., possible violations of the spin-statistics connection and models of dynamical wave function collapse. Our recent phenomenological analyses demonstrated the importance of pushing the research of new physics signal, predicted in these fields, to an energy range below 10 keV. We describe the development of the dedicated data acquisition system and of the pulse shape discrimination algorithm, which have already allowed us to get a factor two improvement in the lower energy threshold. Future plans are discussed to further improve the lower energy threshold to the level of a few keV.

Labeling strategy to improve neutron/gamma discrimination with organic scintillator

Organic scintillators are widely used for neutron/gamma detection. Pulse shape discrimination algorithms have been commonly used to discriminate the detected radiations. These algorithms have several limits, in particular with plastic scintillator which has lower discrimination ability, compared to liquid scintillator. Recently, machine learning (ML) models have been explored to enhance discrimination performance. Nevertheless, obtaining an accurate ML model or evaluating any discrimination approach requires a reference neutron dataset. The preparation of this is challenging because neutron sources are also gamma-ray emitters. Therefore, this paper proposes a pipeline to prepare clean labeled neutron/gamma datasets acquired by an organic scintillator. The method is mainly based on a Time of Flight setup and Tail-to-Total integral ratio (TTTratio) discrimination algorithm. In the presented case, EJ276 plastic scintillator and 252Cf source were used to implement the acquisition chain. The results showed that this process can identify and remove mislabeled samples in the entire ToF spectrum, including those that contribute to peak values. Furthermore, the process cleans ToF dataset from pile-up events, which can significantly impact experimental results and the conclusions extracted from them.

Improved fast neutron detection using CNN-based pulse shape discrimination

The importance of fast neutron detection for nuclear safeguards purposes has increased due to its potential advantages such as reasonable cost and higher precision for larger sample masses of nuclear materials. Pulse-shape discrimination (PSD) is inevitably used to discriminate neutron- and gamma-ray- induced signals from organic scintillators of very high gamma sensitivity. The light output (LO) threshold corresponding to several MeV of recoiled proton energy could be necessary to achieve fine PSD performance. However, this leads to neutron count losses and possible distortion of results obtained by neutron multiplicity counting (NMC)-based nuclear material accountancy (NMA). Moreover, conventional PSD techniques are not effective for counting of neutrons in a high-gamma-ray environment, even under a sufficiently high LO threshold. In the present work, PSD performance (figure-of-merit, FOM) according to LO bands was confirmed using a conventional charge comparison method (CCM) and compared with results obtained by convolution neural network (CNN)-based PSD algorithms. Also, it was attempted, for the first time ever, to reject fake neutron signals from distorted PSD regions where neutron-induced signals are normally detected. The overall results indicated that higher neutron detection efficiency with better accuracy could be achieved via CNN-based PSD algorithms.

Heterogeneous Quasi-Continuous Spiking Cortical Model for Pulse Shape Discrimination

The present study introduces the heterogeneous quasi-continuous spiking cortical model (HQC-SCM) method as a novel approach for neutron and gamma-ray pulse shape discrimination. The method utilizes specific neural responses to extract features in the falling edge and delayed fluorescence parts of radiation pulse signals. In addition, the study investigates the contributions of HQC-SCM’s parameters to its discrimination performance, leading to the development of an automatic parameter selection strategy. As HQC-SCM is a chaotic system, a genetic algorithm-based parameter optimization method was proposed to locate local optima of HQC-SCM’s parameter solutions efficiently and robustly in just a few iterations of evolution. The experimental results of this study demonstrate that the HQC-SCM method outperforms traditional and state-of-the-art pulse shape discrimination algorithms, including falling edge percentage slope, zero crossing, charge comparison, frequency gradient analysis, pulse-coupled neural network, and ladder gradient methods. The outstanding discrimination performance of HQC-SCM enables plastic scintillators to compete with liquid and crystal scintillators’ neutron and gamma-ray pulse shape discrimination ability. Additionally, the HQC-SCM method outperforms other methods when dealing with noisy radiation pulse signals. Therefore, it is an effective and robust approach that can be applied in radiation detection systems across various fields.

Systematic evaluation of fast neutron sensing with Cesium Hafnium Chloride

Cesium Hafnium Chloride (CHC) is a promising new scintillator for dual mode sensing of both neutrons and gamma-rays. The high chlorine content allows for both 35Cl(n,p)35S and 35Cl(n, α)32P reaction channels leading to reliable detection of fast neutrons. By utilizing pulse shape discrimination (PSD), these neutron interactions may be reliably separated from gamma-ray signals with a high figure-of-merit (FOM) after optimization of the PSD algorithm. In this study, the PSD algorithm settings for CHC were systematically investigated using a bare 252Cf and a lead-shielded plutonium beryllium neutron source. It was found that the PSD algorithm settings affects both the FOM and the neutron detection efficiency, but a FOM as high as 4.5 for alpha particles was observed in one data processing scenario. Further, an intrinsic 5 parts per million alpha-emitting contamination was observed in the sample, which we attribute to natural uranium.

Model-Based Deep Learning Algorithm for Pulse Shape Discrimination in High Event Rates

Pulse shape discrimination (PSD) is at the core of radioactive particles monitoring. Conventional PSD methods are geared towards low event rates, and struggle in the presence of pileups resulting from high rate. In this work we develop a PSD algorithm that combines classic approaches with deep learning techniques, that is highly suitable for coping with the dramatic challenges associated with classifying pulses in high event rates. Common PSD algorithms for high event rates limit their research to two piled-up pulses. Our algorithm is designed and tested under severe pileup conditions, where three or more pulses were piled-up. We tested the algorithm on simulated data based on Cs2LiYCl6:Ce (CLYC) based detector pulse shapes and compare its performance to both traditional PSD algorithms and data-driven deep neural network (DNN) based algorithms. In high event rates, ranging up to 10 Mcps, the algorithm demonstrates up to 8 times fewer miss-classifications than the traditional normalized cross-correlation (NCC) approach, and up to 1.7 times fewer miss-classifications than a purely data-driven DNN-aided method.

Robust pulse shape discrimination algorithm using canonical correlation analysis of gamma and neutron pulses

In this paper, a robust pulse shape discrimination algorithm is proposed to classify neutron and gamma pulses in radiation detectors. Scalogram of the induced pulses, which represent the absolute value of the Continuous Wavelet Transform (CWT) coefficients in the time-frequency domain, is used as feature descriptors. A two-dimensional principal component analysis (2D PCA) is employed to achieve dimensionality reduction of the obtained features and remove the redundancy in the given data. Accordingly, dominant features are selected and stacked together to construct two feature sets representing the two classes (neutron and gamma). Canonical Correlation Analysis is performed between the testing and training feature sets to find the basis vectors on which the new projected set pairs are highly correlated. Experimental results showed that the proposed method outperforms the other state-of-the art PSD methods in terms the discrimination accuracy.

Real-time pulse shape discrimination for neutron scattering science using EJ-270

A detector for neutron scattering applications utilizing the 6Li loaded pulse shape discrimination scintillator EJ-270 was developed. The detector utilises a pulse shape discrimination algorithm developed for high-count rate applications implemented in FPGA to enable real time operation. The implemented algorithm we call Tail Sum was specifically developed for high-rate thermal neutron detection and implemented into a 2 channel 500 MSps 14-bit ADC with a Xilinx Kintex 7 K325T FPGA. The detector consisted of a 48 mm diameter × 15 mm thick cylinder of EJ-270 mounted on a Hamamatsu R6231-100 PMT and an E1198-2 base at -1000 V. The performance of the system was evaluated using a moderated 241Am-Be mixed radionuclide source, a figure of merit of 1.605 ± 0.007 was measured. The γ-ray sensitivity of the system was measured using a 60Co source and found to be 3.23 × 10-6 ± 7 × 10-8. The high neutron count rate capability of the system was tested on the Emma instrument at the ISIS pulsed neutron and muon source based at the Rutherford Appleton Laboratory, a peak count rate of 5 × 105 neutrons per second was measured on a single channel and was found to have no statistically significant deviation from the expected count-rate.

Investigation of CLYC-6 for thermal neutron detection and CLYC-7 for fast neutron spectrometry

Highly enriched 6Li and 7Li Cs2LiYCl 6 (CLYC) scintillators were investigated in view of their application as thermal neutron counter (CLYC-6) and as fast neutron spectrometer (CLYC-7) in the recently developed B-RAD radiation survey metre, an instrument designed to work in regions of high magnetic fields, currently equipped with a probe for photon dose rate measurements and γ-spectrometry. Both crystals were irradiated with a 2.5 MeV mono-energetic neutron beam from a Deuterium–Deuterium (D–D) generator and characterised in terms of neutron/γ-ray (n/γ) discrimination capability by the Pulse Shape Discrimination (PSD) method, energy resolution and quenching factor. Both crystals exhibit an excellent n/γ discrimination, quantified by a Figure of Merit higher than 2. The measured thermal energy resolution of CLYC-6 is 6%. The quenching factor for the 6Li(n,t)α thermal neutron reaction is 0.65 and for the 35Cl(n,p)35S reaction is 0.91. The measured neutron detection efficiency per unit volume of CLYC-6 is 60% higher than that of a 3He proportional counter. CLYC-7 was tested as a fast neutron spectrometer below 10 MeV, when irradiated with continuum neutron spectra from Am–Be and 252Cf sources. A proton/α-particle (p/α) discrimination was performed exploiting the PSD technique, without any unfolding procedure. The resulting spectra match the corresponding ISO spectra better than the spectra without the p/α discrimination, especially in the range 3 MeV to 5 MeV. A more sensitive PSD algorithm might help to improve particle discrimination at lower energies, while different reaction channels arise at higher energies preventing a spectrometry analysis.

Novel Model for Analysis and Optimization of Silicon Photomultiplier-Based Scintillation Systems

Nowadays, silicon photomultipliers (SiPMs) are extensively used for absorption of scintillation light in all types of scintillators in high-energy physics. Fast spread of SiPMs resulted in a rapid development of both analytical and Monte Carlo models. Models describe the response of these silicon integrated circuits. We introduce a novel Monte Carlo model of SiPM with a scintillator module that enables modeling the response of SiPM to dynamic scintillation processes. The model introduces several improvements over other models. This article focuses on the analysis of pulse shape discrimination (PSD) performance of SiPM-based scintillation systems since such techniques are often used to discriminate between incident particles of ionizing radiation. The algorithms for PSD are sensitive to the shape of the pulse and SiPMs have several mechanisms that influence the shape of the output pulse, such as bandwidth of the system, the presence of fast decay components, and the recovery time of individual microcells. Some of these mechanisms are not present in a classical photomultiplier, for instance, a recovery time or the fast decay component. We have analyzed the performance of three different PSD algorithms with three SiPMs (MicroFC-30020, MicroFC-30035, and MicroFC-30050) coupled with scintillators EJ-301 and EJ-276. Several conclusions are drawn from the analysis. The two most important ones are that optimized systems need to finetune their bandwidth and that scintillators with fast decay signals are better suited for photomultipliers with lower recovery time and vice versa. It is also shown that the classical charge comparison algorithm does not reach the performance of modern algorithms, for instance, frequency gradient analysis.

Characterization of a medium-sized CLLB scintillator: single neutron/gamma detector for radiation monitoring

The use of a single neutron/gamma detector is an interesting solution to detect and identify gamma emitters and also special nuclear materials (SNM), being able to discriminate between the two kinds of particles and also to perform good-resolution gamma spectroscopy.In this framework, we present a comprehensive characterization of a medium sized (2" × 2") CLLB (Cs2LiLaBr6:Ce) scintillation detector, in order to give the necessary information to assess its deployment in applications regarding homeland security and radiation monitoring. In particular, the parameters studied are: energy resolution, full-energy peak gamma efficiency, time resolution, thermal neutron/gamma discrimination capability, decay time of the signals, high counting rate performance and minimum detectable activities (of 137Cs and 252Cf sources). We employed digital nuclear electronics combined with a pulse shape discrimination algorithm to acquire and analyze the data. We compared our results with reported data for smaller CLLB scintillators, finding good agreement. Experiments were combined with Monte Carlo simulations (using GEANT4 v10.6.0 and MCNP5 v1.60) in order to complement the characterization. The obtained results suggest that the 2” × 2” CLLB detector offers better performance with respect to other scintillators of the same size such as NaI(Tl), CsI, CeBr, etc. which are commonly used in a radiation monitoring systems.

Pulse-shape discrimination against low-energy Ar-39 beta decays in liquid argon with 4.5 tonne-years of DEAP-3600 data

The DEAP-3600 detector searches for the scintillation signal from dark matter particles scattering on a 3.3 tonne liquid argon target. The largest background comes from ^{39}text{ Ar } beta decays and is suppressed using pulse-shape discrimination (PSD). We use two types of PSD estimator: the prompt-fraction, which considers the fraction of the scintillation signal in a narrow and a wide time window around the event peak, and the log-likelihood-ratio, which compares the observed photon arrival times to a signal and a background model. We furthermore use two algorithms to determine the number of photons detected at a given time: (1) simply dividing the charge of each PMT pulse by the mean single-photoelectron charge, and (2) a likelihood analysis that considers the probability to detect a certain number of photons at a given time, based on a model for the scintillation pulse shape and for afterpulsing in the light detectors. The prompt-fraction performs approximately as well as the log-likelihood-ratio PSD algorithm if the photon detection times are not biased by detector effects. We explain this result using a model for the information carried by scintillation photons as a function of the time when they are detected.

ResNet and CycleGAN for pulse shape discrimination of He-4 detector pulses: Recovering pulses conventional algorithms fail to label unanimously

Pulse shape discrimination (PSD) capable detectors, such as He-4, that respond to neutron and gamma-ray interactions have a threshold deposited energy value below which n/γ discrimination vanishes when using conventional PSD algorithms. Recent attempts in applying supervised learning based artificial neural networks for PSD use the pulses in the separated regions to train the networks so they can be used to classify another set of separated pulses. In doing so, pulses previously indistinguishable are not recovered for classification, which would have increased the number of neutron and gamma-ray pulses that could be used for further analysis. Assuming the reason why conventional PSD algorithms have unseparated regions is because the parameter space of the algorithms fail to capture the intrinsic (but subtle) distinguishing behavior of some of the neutron and gamma-ray pulses, a cycle-consistent generative adversarial network (CycleGAN) was trained to amplify those differences and extract well separated neutron and gamma-ray clusters. Results show that, once the network is trained with pulses from separated and unseparated regions, it was able to transform the pulses in the unseparated region to improve the PSD. Subsequent n/γ classification was performed using deep residual network (ResNet) that takes pulses with 512 data points as an input. Two different ResNets were explored – simple ResNet and modified ResNet which takes segmented pulse inputs in the first layer and the corresponding time axis values in the last hidden layer. The later approach enables the network to extract time correlated pulse features to enhance its ability to capture the pulse behaviors relevant for PSD. Although it achieves slightly lower accuracy, 99.41% versus 99.89%, based on simply counting the number of correct n/γ labels assigned, compared to the simple ResNet, the modified ResNets architecture was able to decreases the cross-entropy loss function by half, which implies that the correct n/γ labels assigned are less likely to be accidental. PSD parameter distributions based on n/γ classification by ResNet before and after transforming unseparated pulses using CycleGAN show that by enhancing the separation between neutrons and gamma-rays, the transformation helps improve the performance of classifier networks that are trained using labeled dataset. The enhancement of neutron and gamma-ray separation by the CycleGAN increased the PSD figure of merit (FOM) by up to 70% in some regions. The results show that, if a given detector achieves clear separation between neutron and gamma-ray pulses in any energy region, such neural network approaches can help lower the energy threshold for the separation and increasing the number of neutron and gamma-ray pulses that can be used for further analysis.

A 2D pixelated stilbene scintillator detector array for simultaneous radiography with fast neutrons and gammas

For radiography applications using fast neutrons simultaneously with gammas we have developed a detector with 16 stilbene crystals in a 4×4 2D array with a 5 mm pitch and a depth of 25 mm. The crystal array is read out by Silicon photomultipliers and custom signal processing electronics. The detector prototype was tested using a custom D-D fast neutron generator at the Paul Scherrer Institute. By applying a pulse shape discrimination algorithm the detector is able to detect and distinguish fast neutrons and gammas simultaneously. Various attenuating samples placed between the source and detector with different materials and thicknesses were tested and the measured macroscopic fast neutron cross sections were compared to what was expected. Deviations were studied with the help of detailed Geant4 simulations. The detection efficiency for D-D fast neutrons was measured to be around 10%.

High count rate pulse shape discrimination algorithms for neutron scattering facilities

The performance of EJ-270, a 6Li loaded pulse shape discriminating plastic scintillator was tested for use in applications with high thermal and epithermal neutron fluxes such as neutron scattering facilities. The short decay time of EJ-270 make it of interest for high count rate applications. To realize this, 4 PSD algorithms were tested. The algorithms were selected based on the potential for high rate applications and simplicity. These algorithms were the charge integration method with and without the addition of a digital low pass filter, a measurement of the time to 10% of peak amplitude and a method we call the “tail sum” which utilizes a digital low pass filter and sums a small number of samples in the tail of each pulse. These algorithms were benchmarked using the figure of merit, the γ-sensitivity and potential rate capability. The charge integration method gave the highest figure of merit of 1.37 using a long window of 272.5 ns but had a γ-sensitivity of 2×10−6 which was poorer than the tail sum algorithm. The tail sum algorithm was able to achieve a figure of merit of 1.36 with a window of 250 ns and a γ-sensitivity on the order of 10−7. Reducing the integration windows to match the fastest algorithm of time to 10% resulted in the tail sum outperforming the other algorithms with a figure of merit of 1.26 and a γ-sensitivity of 6×10−7. The short charge integration method and tail-sum were compared on the EMMA beamline at the ISIS pulsed neutron and muon source. The tails sum demonstrated better separation between the γ-rays and thermal neutron at an incident peak neutron rate of 9.6×105 neutrons per second.

Performance of linear classification algorithms on α/γ discrimination for LaBr3:Ce scintillation detectors with various pulse digitizer properties

With the development of high-speed readout electronics, the digital pulse shape discrimination (PSD) methods have attracted the attention of more researchers, especially in the field of high energy physics and neutron detection. How to choose a PSD algorithm and corresponding data acquisition system (DAQ) naturally becomes a critical problem to settle down for the detection system designers. In this paper, the relationship between the classification performance of different PSD algorithms and digitizers' sampling properties (including sampling rate and the effective number of bits) has been researched based on LaBr3:Ce scintillation detectors. A self-developed integrated digitizer configured with five different ADCs and a WavePro 404HD oscilloscope were deployed to digitize the waveforms from LaBr3:Ce scintillators. Moreover, three PSD methods, charge comparison method (CCM), least square for classification method (LS) and Fisher's linear discriminant analysis (LDA), based on linear model were applied to discriminate the alpha signals from the intrinsic isotope 227Ac. With the LS method and a 125 MSPS 14-Bit ADC, the FoM value was 1.424±0.042, which is similar to the result from LDA but 31% better than the result of CCM. The discrimination results showed that the performances of LS and LDA are less affected by the sampling rate with respect to the CCM method, which reflects in a better PSD capability. The results of this paper can help the developers of detector systems to make a trade-off among sampling properties, desirable discrimination results and the cost of systems.

A Novel Portable Device For Gamma And Neutron Spectroscopy With Special Nuclear Material Identification

The development of new types of detectors and the increased performance of the electronics have paved the way for developing advanced systems for the measurement and identification of radioactive material that can be involved, for example, in illicit trafficking. Radioactive isotope identifiers are today commercially available. Nowadays those systems make use of inorganic scintillators as Sodium Iodide (NaI(Tl)) or, for enhanced resolution, Lanthanum Bromide (LaBr3) to identify the gamma emitters through their characteristic gamma lines. The most complete systems usually include an additional 3He proportional counter for neutron detection and counting. The performances of such devices are compliant with standards as the IEC 62327 Hand Held Instruments for the Detection and Identification of Radionuclides. This paper presents a new type of portable radioactive isotope identifier. This device, based on an organic liquid scintillator with excellent Pulse Shape Discrimination (PSD) proprieties for the simultaneous detection of gamma rays and neutrons, detects radioactive source as Special Nuclear Material (SNM), medical, industrial and Naturally Occurring Radioactive Material. The exclusive feature of this instrument is the identification of neutron sources with discrimination between fission sources (like Californium 252Cf) and alpha-n type sources (like Americium Beryllium Am-Be) from Plutonium and Uranium through an innovative dedicated algorithm. Individual thresholds for neutron and gamma counts are calculated to allow detection with 95% detection probability for a dose rate on the front face of the scintillator of at least 50 nSv/h. Alarms are triggered separately when the respective rate exceed these thresholds. The neutron source detection has also been proved in a gamma ray field up to 100 μSv/h. The electronics is equipped with two analog inputs and two high voltage power supplies in a small form factor thus becoming an enabling technology for higher performance yet portable radioactive isotope identifier devices, which can include more detectors and perform data fusion analysis. The addition of a second detector allows to detect a masked neutron source through the PSD algorithm performed by the liquid scintillator detector while the added inorganic scintillator identifies the masking gamma emitters. The inorganic scintillator allows also the calculation of Pu and U enrichment grade through characteristic gamma emission line.

Digital pulse shape discrimination technique for liquid scintillation detector EJ-301

A digital pulse shape discrimination (PSD) system has been built using a high speed digital oscilloscope NI-5772 and tested with an EJ-301 liquid scintillation detector. The sampling frequency of this system is 1.6 Gs/s. In this work, the charge comparison method, the rise time method and the zero-crossing time method are used to perform neutron-gamma discrimination. Experimental results show that the studied digital PSD algorithms can discriminate neutron and gamma rays in mixed radiation fields. The digital rise time method shows the optimal neutron-gamma discrimination with integration parameter RC of 10000 ns. Moreover, the zero-crossing time method is investigated through CR-R1C1 and CR-R1C1-R2C2 shaping networks. It is found that for the same pair of values for the two kinds of network, the CR-R1C1-R2C2 shaping network presents better neutron-gamma discrimination with R2C2 of 5 ns. In addition, through the investigation of the distribution of neutron and gamma peaks for different energy regions, the relationship between the ionization density of liquid scintillation detector and the contribution of slow component in the light outputs is demonstrated.