A method for distinguishing neutron gamma pulse waveform stacks under high counting rate conditions

Abstract

The present invention relates to a neutron gamma stacking waveform identification method, and specifically to a neutron gamma pulse waveform stacking identification method under high counting rate conditions. The method solves the technical problem that existing neutron gamma waveform identification methods do not consider the change of the neutron gamma waveform trailing edge, resulting in a low accuracy rate. The neutron gamma pulse waveform stacking identification method under high counting rate conditions includes the following steps: 1) collecting and storing neutron gamma single waveform mixed signals; 2) obtaining a neutron data set and a gamma data set S1; 3) constructing a test set P1 of neutron gamma stacking waveforms; 4) obtaining a neutron data set and a gamma data set S2; 5) constructing a feature parameter set T; 6) constructing a training set sample library P2; 7) constructing a training data set P3; 8) training a neural network, using the trained neural network to identify all waveforms in the test set P1 and output their predicted labels, thereby completing neutron gamma pulse waveform stacking identification under high counting rate conditions.

Description

Technical Field

[0001] The present invention relates to a neutron gamma stack waveform discrimination method, in particular to a neutron gamma pulse waveform discrimination method under high counting rate conditions. Background Art

[0002] Pulse shape discrimination (PSD) of high-energy neutron and gamma-ray emissions using organic scintillation detectors (OSDs) is a key requirement for applications in nuclear safety, fusion neutron diagnostics, and neutron-induced fission cross-section measurement experiments. When the detector operates at very high count rates, multiple radiation particles arrive at the detector within a single pulse window, resulting in two or more pulses partially or completely overlapping. This significantly alters the pulse structure, rendering traditional single-event waveform discrimination methods ineffective. Although piled-up pulses can be identified and rejected, the resulting event loss limits the ability to analyze features in the presence of high gamma-ray backgrounds. To reduce the number of lost events, various methods have been proposed to extract PSD information from piled-up pulses.

[0003] F. Belli et al. reported a method for identifying the effect of a double-exponential decay function (Belli F, Esposito B, Marocco D, et al. A method for digital processing of pile-up events inorganic scintillators [J]. Nuclear Instruments and Methods in Physics Research, Section A. Accelerators, Spectrometers, Detectors and Associated Equipment, 2008 (2): 595). This method uses an average decay constant and the original "good" data corresponding to the first pulse for pulse fitting to obtain sample data for the stacked portion of the first pulse trailing edge. The average decay constant used in the fitting has a standard deviation of 25%, and the fitting process relies on multiple trials and errors, making it difficult to converge to the correct solution. XLLuo reported a method for distinguishing between neutrons using a standard gamma waveform (Pulse pile-up identification and reconstruction for liquid scintillator based neutron detectors [J]. Nuclear Instruments and Methods in Physics Research, Section A. Accelerators, Spectrometers, Detectors and Associated Equipment, 2018, 897: 59-65.), which is used for stacked waveform discrimination of BC-501A liquid scintillation detectors. The standard waveform is obtained by averaging all known neutron gamma waveforms, and then the stacked waveform constructed by stacking pulses is compared with the stacked waveform constructed by the standard waveform to achieve discrimination and reconstruction of the stacked pulses. This method can achieve stacked waveform discrimination with an interval of 20ns between the two pulses.

[0004] However, neutron gammas of different energies produce fluorescence with different decay constants in the scintillator, resulting in varying fluctuations in the trailing edge of the neutron gamma waveform. For example, the normalized neutron waveforms do not completely overlap, and the rise times obtained by integrating the entire pulse vary. The aforementioned stacked waveform discrimination methods do not consider the details of trailing edge variations. Therefore, when discriminating stacked waveforms, instead of simply using a standard waveform or average time constant, the variation in the trailing edge of the pulse waveform is considered, which is expected to further improve discrimination accuracy. Summary of the Invention

[0005] The purpose of the present invention is to solve the technical problem that the existing neutron gamma waveform discrimination methods do not consider the change of the neutron gamma waveform trailing edge, resulting in low accuracy, and to provide a neutron gamma pulse waveform stacking discrimination method under high counting rate conditions.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A neutron gamma pulse waveform stacking discrimination method under high counting rate conditions is characterized in that it includes the following steps:

[0008] 1] Collect and store neutron gamma single waveform mixed signals;

[0009] 2] Identify the neutron gamma single waveform signal and obtain the neutron data set and gamma data set S1;

[0010] 3] Using the neutron dataset and gamma dataset S1, construct the test set P1 of neutron gamma stack waveforms;

[0011] 4] Select the neutron waveforms and gamma waveforms of different amplitudes in the neutron data set S1 and the gamma data set S2 according to the rise time and count;

[0012] 5] Extract the characteristic parameters of all stacked waveforms in the test set P1 and construct a characteristic parameter set T corresponding to the test set P1;

[0013] 6] Based on the test set P1, the neutron data set and the gamma data set S2 and the feature parameter set T, the training set sample library P2 is constructed;

[0014] 7] Select the training samples corresponding to the test set P1 from the training set sample library P2 to construct the training data set P3;

[0015] 8] The training data set P3 is used as the training set of the neural network. The neural network is trained and used to identify all waveforms in the test set P1 and output their predicted labels to complete the stacking identification of neutron gamma pulse waveforms under high counting rate conditions.

[0016] Furthermore, step 1 is specifically as follows:

[0017] A neutron tube is used as the neutron source, and a pulsed neutron detection system is used to collect and store more than or equal to 50,000 neutron gamma single waveform mixed signals.

[0018] Furthermore, step 2 is specifically as follows:

[0019] The rise time method is used to identify the neutron and gamma single waveform mixed signals to obtain neutron waveforms and gamma waveforms. The peak positions of all neutron waveforms and gamma waveforms are aligned to form the neutron data set and gamma data set S1.

[0020] Furthermore, step 3 is specifically as follows:

[0021] Neutron waveforms and gamma waveforms are randomly extracted from the neutron dataset and gamma dataset S1, and a test set P1 of neutron-gamma stacking waveforms is constructed according to four stacking methods: neutron + neutron, neutron + gamma, gamma + neutron, and gamma + gamma.

[0022] Furthermore, step 5 is specifically as follows:

[0023] According to the peak value F1, peak value F2 and peak distance D of the stacked waveforms, the characteristic parameters of each waveform in the test set P1 are extracted, and a characteristic parameter set T corresponding to the test set P1 is constructed.

[0024] Furthermore, step 6 is specifically as follows:

[0025] 6.1. Construct a training sample library of neutron + neutron stacking for each test waveform in the test set P1;

[0026] 6.2. Extracting a first neutron waveform and a second neutron waveform from the neutron data set and the gamma data set S2;

[0027] 6.3. The peak value F of the i-th test waveform in the characteristic parameter set T i1 The ratio of the peak value f1 of the first neutron waveform is recorded as the peak factor r i1 ; The peak value F of the i-th test waveform in the characteristic parameter set T i2 The ratio of the peak value f2 of the second neutron waveform is recorded as the peak factor r i2 ; i=1,2,...,N, N is the number of test waveforms in the test set P1, N≥2;

[0028] 6.4. Multiply the first neutron waveform by the peak factor r i1 , the second neutron waveform multiplied by the peak factor r i2 , so that the two neutron waveforms are respectively related to their corresponding peak values ​​F i1 , peak F i2 equal;

[0029] 6.5. Shift the second neutron waveform back by the peak distance D of the i-th test waveform i Then, after adding the first neutron waveform and the second neutron waveform, the peak value f1 of the stacked waveform is extracted. * , peak value f2 * and peak spacing d;

[0030] 6.6. Calculate the new peak factor r1 according to the following formula: * 、r2 * and peak spacing d * :

[0031] r1 * =(F i1 +f1 * ) / 2f1

[0032] r2 * =(F i2 +f2 * ) / 2f2

[0033] d * =D i -(dD i );

[0034] 6.7. Return to step 6.2 and take out the second neutron waveform and the third neutron waveform from the neutron dataset and gamma dataset S2 in order. i1 =r1 * 、r i2 =r2 * and peak spacing D i =d * , reprocess the waveform until the peak value f1 * With peak F i1 and peak f2 * With peak F i2 The peak error is less than 1%, the peak distance d and the peak distance D i The error is less than 2 sampling points, and all neutron waveforms in the neutron data set and the gamma data set S2 are traversed to complete the training set sample library of the neutron + neutron stacking method, which is recorded as the first training sample library;

[0035] 6.8. According to steps 6.1 to 6.7, a training set sample library constructed for the neutron + gamma stacking method is recorded as the second training sample library, a training set sample library constructed for the gamma + neutron stacking method is recorded as the third training sample library, and a training set sample library constructed for the gamma + gamma stacking method is recorded as the fourth training sample library; and the first training sample library, the second training sample library, the third training sample library, and the fourth training sample library are referred to as training set sample library P2.

[0036] Furthermore, step 7 is specifically as follows:

[0037] The K-nearest neighbor method is used to calculate the similarity. Based on the similarity, training samples corresponding to the test set P1 are selected from the training set sample library P2 to construct the training data set P3.

[0038] Furthermore, step 8 is specifically as follows:

[0039] 8.1. Use the training dataset P3 as the input of the neural network, train the neural network, and define each row as a training sample, with the number of rows being the number of training samples;

[0040] 8.2. Use the trained neural network to identify all test waveforms in the test set P1 and output their predicted labels to complete the stacked identification of neutron gamma pulse waveforms under high counting rate conditions.

[0041] Furthermore, in step 3], the stacked waveform is obtained by shifting any one of the two waveforms backward by 100 to 400 sampling points and then adding the two waveforms together to obtain the stacked waveform.

[0042] Furthermore, in step 8.1, the neural network is a CNN convolutional neural network.

[0043] Beneficial effects of the present invention:

[0044] 1. The present invention provides a method for distinguishing neutron gamma pulse waveform stacks under high counting rate conditions. This method changes the previous method of randomly extracting samples as a data set. Based on the different rise times of each single-particle waveform (i.e., neutron waveform or gamma waveform), the waveform characteristics are refined, and samples are extracted using the rise time as a parameter, making the samples more representative. When processing the training set (i.e., the training set sample library P2), the two peak values ​​and peak spacing of the processed stacked waveform are inconsistent with the waveform to be distinguished. An iterative method is used to continuously approximate the characteristic parameters of the stacked waveform to be distinguished. Training samples similar to the waveform to be distinguished are selected as the input of the neural network, avoiding overfitting caused by the input of a large number of irrelevant training sets, and improving computational efficiency and accuracy.

[0045] 2. This invention proposes a method for distinguishing stacked neutron gamma pulse waveforms under high count rate conditions. It combines rise time, the K-nearest neighbor method, and an artificial neural network. Samples are extracted using rise time as a parameter, making them more representative. The K-nearest neighbor method calculates the similarity with the waveform to be distinguished and selects training samples similar to the waveform to be distinguished as input to the neural network, providing a new approach for distinguishing stacked waveforms.

[0046] 3. The stacked waveform in the neutron gamma pulse waveform stacking identification method under high counting rate conditions of the present invention has three significant characteristic parameters, namely the first peak value, the second peak value and the peak spacing of the stacked waveform. The extracted characteristic parameters can effectively determine the main characteristics of the test waveform.

[0047] 4. The present invention provides a neutron gamma pulse waveform stacking and discrimination method under high counting rate conditions. When the selected neutron waveform and gamma waveform are processed according to four stacking methods, the two peak values ​​and peak spacing of the processed waveform will change, which will be different from the waveform characteristics of the test set. After modifying the characteristic parameters, multiple iterations are performed to narrow the gap between the characteristic parameters of the training set (i.e., the training set sample library P2) and the test waveform. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of an embodiment of a method for distinguishing neutron gamma pulse waveform stacks under high counting rate conditions according to the present invention;

[0049] Figure 2 This is a schematic diagram of a method for distinguishing neutron gamma pulse waveform stacks under high counting rate conditions according to the present invention, in which a rise time method is used to distinguish neutron gamma single waveform mixed signals. Figure 2 The left side of the red line represents the gamma single wave signal, and the right side represents the neutron single wave signal;

[0050] Figure 3 Schematic diagram of two neutron waveforms in an embodiment of a neutron gamma pulse waveform stacking discrimination method under high counting rate conditions of the present invention, wherein the horizontal axis represents time and the vertical axis represents amplitude;

[0051] Figure 4 Schematic diagram of a method for stacking neutron gamma pulse waveforms under high count rate conditions according to an embodiment of the present invention, wherein the neutron waveform is shifted back by 100 sampling points, wherein the horizontal axis represents time and the vertical axis represents amplitude;

[0052] Figure 5 1 is a schematic diagram of a stacked waveform obtained by adding two neutron waveforms in an embodiment of a neutron gamma pulse waveform stacking discrimination method under high counting rate conditions of the present invention, wherein the horizontal axis represents time and the vertical axis represents amplitude;

[0053] Figure 6 3 is a schematic diagram showing the relative positions of unstacked neutron waveforms (black and red) and stacked waveforms (green) in an embodiment of a method for stacking neutron gamma pulse waveforms for discrimination under high count rate conditions of the present invention, wherein the horizontal axis represents time and the vertical axis represents amplitude;

[0054] Figure 7 It is a schematic diagram of a CNN neural network in an embodiment of a neutron gamma pulse waveform stacking discrimination method under high counting rate conditions of the present invention. DETAILED DESCRIPTION

[0055] like Figure 1 As shown, a neutron gamma pulse waveform stacking discrimination method under high counting rate conditions includes the following steps:

[0056] Step 1: Collect and store neutron gamma single waveform mixed signals:

[0057] Using a neutron tube as the neutron source, a pulsed neutron detection system is used to collect and store more than 50,000 neutron gamma single waveform mixed signals. The pulsed neutron detection system consists of a stilbene crystal, an ETL-9815 photomultiplier tube, and an oscilloscope.

[0058] Step 2: Identify the neutron gamma single waveform signal:

[0059] like Figure 2 As shown, the rise time method is used to identify the neutron and gamma single waveform mixed signals, and the neutron waveform and gamma waveform can be obtained. The peak positions of all neutron waveforms and gamma waveforms are aligned to form the neutron data set and gamma data set S1;

[0060] Step 3: Construct the test set P1 of neutron gamma stack waveform:

[0061] Neutron waveforms and gamma waveforms are randomly extracted from the neutron data set S1 and the gamma data set S1, and neutron-gamma stacked waveforms are constructed according to four stacking methods: neutron + neutron, neutron + gamma, gamma + neutron, and gamma + gamma. The four stacking methods are labeled. In this embodiment, the labels are recorded as 1, 2, 3, and 4 respectively, and 1000 stacked waveforms are processed for each stacking method, resulting in a total of 4000 stacked waveforms to construct the test set P1.

[0062] When processing the stacked waveform of two neutron waveforms, one of the neutron waveforms is moved back by 100 sampling points (the sampling points vary randomly between 100 and 400, and the sampling points are the distance of the backward shift). Figure 3 As shown in, these are the original waveforms of the two neutron waveforms; Figure 4 As shown in , the second neutron waveform is shifted back by 100 sampling points; Figure 5 As shown in , the neutron waveform after 100 sampling points is added to the first neutron waveform to obtain the stacked waveform of the neutron waveform; Figure 6 Figure 2 shows the relative positions of unstacked neutron waveforms (black and red) and stacked waveforms (green). When waveforms are stacked, the peak value and peak position of the second waveform change.

[0063] Step 4: Create neutron dataset and gamma dataset S2:

[0064] like Figure 2As shown, the rise times and counts of the neutron and gamma waveforms follow a Gaussian distribution. Neutron and gamma waveforms of varying amplitudes were selected from the neutron and gamma datasets S1 based on their rise times and counts. The distribution of these waveforms was then thinned to make the selected waveforms more representative. A total of 200 neutron and gamma waveforms were selected to create the neutron and gamma datasets S2, preparing the training sample library.

[0065] Step 5: Extract the stacked waveform feature parameters in the test set P1:

[0066] The characteristic parameters of each waveform in the test set P1 are constructed into a characteristic parameter set T corresponding to the test set P1 according to the peak value F1, peak value F2 and peak distance D of the stacked waveforms; the characteristic parameter set T can effectively determine the main characteristics of the test waveform;

[0067] Step 6: Build the training set sample library P 2i :

[0068] 6.1. Construct a training sample library of neutron + neutron stacking for the i-th test waveform in the test set P1, i∈4000;

[0069] 6.2. Extracting a first neutron waveform and a second neutron waveform from the neutron data set and the gamma data set S2;

[0070] 6.3. The peak value F of the i-th test waveform in the characteristic parameter set T i1 The ratio of the peak value f1 of the first neutron waveform is recorded as the peak factor r i1 ; The peak value F of the i-th test waveform in the characteristic parameter set T i2 The ratio of the peak value f2 of the second neutron waveform is recorded as the peak factor r i2 ; i = 1, 2, ..., N, N is the number of test waveforms in the test set P1, N = 4000;

[0071] 6.4. Multiply the first neutron waveform by the peak factor r i1 , the second neutron waveform multiplied by the peak factor r i2 , so that the two neutron waveforms are respectively related to their corresponding peak values ​​F i1 , peak F i2 equal;

[0072] 6.5. Shift the second neutron waveform back by the peak distance D of the i-th test waveform i Then, after adding the first neutron waveform and the second neutron waveform, the peak value f1 of the stacked waveform is extracted. * , peak value f2 * and peak spacing d;

[0073] 6.6. Calculate the new peak factor r1 according to the following formula: * 、r2 * and peak spacing d * :

[0074] r1 * =(F i1 +f1 * ) / 2f1

[0075] r2 * =(F i2 +f2 * ) / 2f2

[0076] d * =D i -(dD i )

[0077] The characteristic parameters of the processed waveform will change, resulting in a gap with the characteristic parameters of the test waveform. Therefore, further iterative calculation is required to narrow the gap.

[0078] 6.7. Return to step 6.2 and take out the second and third neutron waveforms from the neutron dataset and gamma dataset S2 in order, and set the peak factor r i1 =r1 * 、r i2 =r2 * and peak spacing D i =d * , reprocess the waveform until the peak value f1 * With peak F i1 and peak f2 * With peak F i2 The peak error is less than 1%, the peak distance d and the peak distance D i The error is less than 2 sampling points, and all neutron waveforms in the neutron data set and the gamma data set S2 are traversed to complete the training set sample library of the neutron + neutron stacking method, which is recorded as the first training sample library; in this embodiment, the first waveform traverses the neutron waveform once, and the second waveform traverses the neutron waveform once, and 40,000 neutron + neutron stacking waveforms can be constructed.

[0079] 6.8. According to steps 6.1 to 6.7, construct a training set sample library for the neutron + gamma stacking method, record it as the second training sample library, construct a training set sample library for the gamma + neutron stacking method, record it as the third training sample library, and construct a training set sample library for the gamma + gamma stacking method, record it as the fourth training sample library; and the first training sample library, the second training sample library, the third training sample library, and the fourth training sample library are collectively referred to as training set sample library P2;

[0080] Similarly, a training set sample library of 40,000 neutron + gamma stacking methods, a training set sample library of 40,000 gamma + neutron stacking methods, and a training set sample library of 40,000 gamma + gamma stacking methods are constructed, and a training set sample library P2 containing four stacking methods is established. The labels are marked according to the four stacking methods of neutron + neutron, neutron + gamma, gamma + neutron, and gamma + gamma, which are 1, 2, 3, and 4 respectively.

[0081] Step 7: Construct training dataset P3:

[0082] The K-nearest neighbor method is used to calculate the similarity. According to the similarity, the training samples corresponding to the test set P1 are selected from the training set sample library P2 to construct the training data set P3. The training data set P3 contains 300 samples of each of the four training sets: neutron + neutron, neutron + gamma, gamma + neutron, and gamma + gamma. As the input of the neural network, it avoids overfitting caused by the input of a large number of irrelevant training sets, and improves the calculation efficiency and accuracy.

[0083] Step 8: Use the training data set P3 as the input of the neural network, train the neural network, use the trained neural network to identify the test set P1, and output its predicted label to complete the neutron gamma pulse waveform stack identification under high counting rate conditions:

[0084] like Figure 7 As shown, the neural network uses a convolutional neural network (CNN). Each row of the CNN is defined as a training example, and the number of rows is the number of training examples. The one-dimensional vector of the training dataset P3 is reshaped using the reshape(train,M,N,D,SN) function in the CNN convolutional neural network. train is the training dataset P3, M is the length of a single training example, N is the width of a single training example, D is the number of channels, and SN is the number of training examples.

[0085] The CNN convolutional neural network was trained using the corresponding training dataset P3. Because the training samples are one-dimensional vectors, N = 1 and D = 1 were set. After the training set was input into the CNN convolutional neural network, it passed through the convolutional layer, batch normalization layer, activation layer, and pooling layer. Six convolutional layers and pooling layers were set, and finally through the fully connected layer, sofmax layer, and output layer to output the predicted label. Among them, the activation function was re lu, the gradient descent algorithm was Adam, the regularization parameter was L2, the convolution kernel size was 5×1, and the pooling layer size was 3×1. The CNN convolutional neural network predicted all test waveforms in the test set P1 and output their predicted labels, completing the full waveform identification of the test set P1.

Claims

1. A method for distinguishing neutron gamma pulse waveform stacks under high counting rate conditions, characterized in that: The following steps are involved: 1] Collect and store neutron gamma single waveform mixed signals; 2] Identify the neutron gamma single waveform signal and obtain the neutron data set and gamma data set S1; 3] Using the neutron dataset and gamma dataset S1, construct the test set P1 of neutron gamma stack waveforms; 4] Select the neutron waveforms and gamma waveforms of different amplitudes in the neutron data set S1 and the gamma data set S2 according to the rise time and count; 5] Extract the characteristic parameters of all stacked waveforms in the test set P1 and construct a characteristic parameter set T corresponding to the test set P1; 6] Based on the test set P1, the neutron data set and the gamma data set S2 and the feature parameter set T, the training set sample library P2 is constructed; 6.

1. Construct a training sample library of neutron + neutron stacking for each test waveform in the test set P1; 6.

2. Extracting a first neutron waveform and a second neutron waveform from the neutron data set and the gamma data set S2; 6.

3. The peak value F of the i-th test waveform in the characteristic parameter set T i1 The ratio of the peak value f1 of the first neutron waveform is recorded as the peak factor r i1 ; The peak value F of the i-th test waveform in the characteristic parameter set T is i2 The ratio of the peak value f2 of the second neutron waveform is recorded as the peak factor r i2 ; i=1,2,...,N, N is the number of test waveforms in the test set P1, N≥2; 6.

4. Multiply the first neutron waveform by the peak factor r i1 , the second neutron waveform multiplied by the peak factor r i2 , so that the two neutron waveforms are respectively related to their corresponding peak values ​​F i1 , peak F i2 equal; 6.

5. Shift the second neutron waveform back by the peak distance D of the i-th test waveform i Then, after adding the first neutron waveform and the second neutron waveform, the peak value f1 of the stacked waveform is extracted. * , peak value f2 * and peak spacing d; 6.

6. Calculate the new peak factor r1 according to the following formula: * 、r2 * and peak spacing d * : r1 * =(F i1 +f1 * ) / 2f1 <h2 style=";text-align:left;direction:ltr">r2<h2 style=";text-align:left;direction:ltr"> * <h2 style=";text-align:left;direction:ltr"> =(F<h2 style=";text-align:left;direction:ltr"> i2 <h2 style=";text-align:left;direction:ltr"> +f2<h2 style=";text-align:left;direction:ltr"> * <h2 style=";text-align:left;direction:ltr"> ) / 2f2 d * =D i -(d-D i ); 6.

7. Return to step 6.2 and take out the second and third neutron waveforms from the neutron dataset and gamma dataset S2 in order, and set the peak factor r i1 =r1 * 、r i2 =r2 * and peak spacing D i =d * , reprocess the waveform until the peak value f1 * With peak F i1 and peak f2 * With peak F i2 The peak error is less than 1%, the peak distance d and the peak distance D i The error is less than 2 sampling points, and all neutron waveforms in the neutron data set and the gamma data set S2 are traversed to complete the training set sample library of the neutron + neutron stacking method, which is recorded as the first training sample library; 6.

8. According to steps 6.1 to 6.7, a training set sample library for the neutron + gamma stacking method is constructed as the second training sample library, a training set sample library for the gamma + neutron stacking method is constructed as the third training sample library, and a training set sample library for the gamma + gamma stacking method is constructed as the fourth training sample library; The first training sample library, the second training sample library, the third training sample library and the fourth training sample library are used as the training set sample library P2; 7] Select the training samples corresponding to the test set P1 from the training set sample library P2 to construct the training data set P3; 8] The training data set P3 is used as the training set of the neural network. The neural network is trained and used to identify all waveforms in the test set P1 and output their predicted labels to complete the stacking identification of neutron gamma pulse waveforms under high counting rate conditions.

2. The method for distinguishing neutron gamma pulse waveform stacks under high counting rate conditions according to claim 1, characterized in that: Step 1] Specifically: A neutron tube is used as the neutron source, and a pulsed neutron detection system is used to collect and store more than or equal to 50,000 neutron gamma single waveform mixed signals.

3. The method for distinguishing neutron gamma pulse waveform stacks under high counting rate conditions according to claim 2, characterized in that: Step 2] Specifically: The rise time method is used to identify the neutron and gamma single waveform mixed signals to obtain neutron waveforms and gamma waveforms. The peak positions of all neutron waveforms and gamma waveforms are aligned to form the neutron data set and gamma data set S1.

4. The method for distinguishing neutron gamma pulse waveform stacks under high counting rate conditions according to claim 3, characterized in that: Step 3] Specifically: Neutron waveforms and gamma waveforms are randomly extracted from the neutron dataset and gamma dataset S1, and a test set P1 of neutron-gamma stacking waveforms is constructed according to four stacking methods: neutron + neutron, neutron + gamma, gamma + neutron, and gamma + gamma.

5. The method for distinguishing neutron gamma pulse waveform stacks under high counting rate conditions according to claim 4, characterized in that: Step 5] Specifically: According to the peak value F1, peak value F2 and peak distance D of the stacked waveforms, the characteristic parameters of each waveform in the test set P1 are extracted, and a characteristic parameter set T corresponding to the test set P1 is constructed.

6. The method for distinguishing neutron gamma pulse waveform stacks under high counting rate conditions according to claim 5, characterized in that: Step 7] Specifically: The K-nearest neighbor method is used to calculate the similarity. Based on the similarity, training samples corresponding to the test set P1 are selected from the training set sample library P2 to construct the training data set P3.

7. The method for distinguishing neutron gamma pulse waveform stacks under high counting rate conditions according to claim 6, characterized in that: Step 8] Specifically: 8.

1. Use the training dataset P3 as the input of the neural network, train the neural network, and define each row as a training sample, with the number of rows being the number of training samples; 8.

2. Use the trained neural network to identify all test waveforms in the test set P1 and output their predicted labels to complete the stacked identification of neutron gamma pulse waveforms under high counting rate conditions.

8. The method for distinguishing neutron gamma pulse waveform stacks under high count rate conditions according to claim 7, characterized in that: In step 3], the stacked waveform is obtained by shifting any one of the two waveforms backward by 100 to 400 sampling points and then adding the two waveforms together.

9. The method for distinguishing neutron gamma pulse waveform stacks under high count rate conditions according to claim 8, characterized in that: In step 8.1, the neural network is a CNN convolutional neural network.

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