Charged particle identification method, charged particle identification system based on screen grid ionization chamber, computer equipment and storage medium

Abstract

The invention discloses a charged particle identification method, a charged particle identification system based on a screen grid ionization chamber, computer equipment and a storage medium, and belongs to the technical field of radiation detection. The charged particle identification method comprises the following steps: acquiring signals generated by charged particles at a cathode and an anode of a detector and generating characteristic quantities; constructing particle identification parameters by using the obtained characteristic quantity; generating a two-dimensional spectrogram according to the identification parameters and observing distribution characteristics of different particle events; and identifying different particles according to the distribution characteristics of different particle events in the two-dimensional spectrogram. Particle energy is associated with an ionization range during identification, the problem of limitation of a traditional particle identification technology in a high event rate and complex particle product environment is solved, especially the capability of processing a tritium nuclear event superposition phenomenon in a resonance energy region is improved, and therefore the problem that due to the fact that the energy resolution is low, the probability of tritium nuclear event superposition is low can be solved. And the defect problem in the prior art that the detection range is limited due to the fact that complex particle types cannot be accurately identified is solved.

Description

Technical Field

[0001] The invention belongs to the technical field of radiation detection, and in particular relates to a charged particle identification method and a charged particle identification system based on a screen grid ionization chamber, a computer device and a storage medium. Background Art

[0002] With the deepening of particle physics and nuclear physics research, the acquisition of nuclear data has put forward new requirements for the identification technology of charged particles. However, in current particle physics experiments, the Grid Ionization Chamber (GIC), as part of the Light-charged Particle Detector Array (LPDA), faces a series of challenges in particle identification in a neutron source environment. Although GIC has a large solid angle, which helps to receive more particles, its energy resolution is relatively low, which limits its ability in particle identification, especially when the particles have similar energies. It is difficult to distinguish different particles. In addition, the superposition phenomenon of tritium nuclear events is particularly obvious in GIC at high event rates, such as in the resonance energy region, making it difficult for traditional methods to distinguish between single tritium nuclear events and double tritium nuclear events.

[0003] As the neutron energy increases, the types of particles produced become more complex, including high-energy protons, alpha particles, etc., which further increases the difficulty of existing methods in high-energy particle identification. In addition, existing technologies may not be accurate enough in signal processing and parameter extraction, and more effective signal processing methods are needed to improve the accuracy of particle identification. Although there are a variety of detectors for particle identification, their solid angles are relatively small, which limits the detection range. Summary of the invention

[0004] In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to provide a charged particle identification method and a charged particle identification system, computer equipment and storage medium based on a screen grid ionization chamber, so as to solve the problem that the prior art has low energy resolution and cannot accurately identify complex particle types, thereby limiting the detection range.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: The present invention discloses a charged particle identification method, comprising: using a screen grid ionization chamber to acquire signals and generate characteristic quantities; constructing identification parameters related to particle range and energy according to the obtained characteristic quantities; generating a two-dimensional spectrum according to different incident neutron energies and performing multi-parameter analysis; and identifying different particles according to particle distribution in the two-dimensional spectrum.

[0006] Specifically, the method of the present invention comprises the following steps: S1: Acquire the signals generated by charged particles at the cathode and anode of the detector and generate characteristic quantities; S2: Use the obtained characteristic quantities to construct particle identification parameters; S3: Generate a two-dimensional spectrum based on the identification parameters and observe the distribution characteristics of different particle events; S4: Identify different particles based on the distribution characteristics of different particle events in the two-dimensional spectrum.

[0007] Preferably, in step S1, the generated characteristic quantities include: anode signal amplitude, anode signal rise time, and cathode and anode signal start time difference.

[0008] Preferably, in step S2, the obtained characteristic quantity is used to construct a particle identification parameter, which specifically includes: S21: Constructing recognition parameters , at small angles of incidence, Only with particle range The calculation formula is: (1) In formula (1), Δt represents the start time difference between the cathode and anode signals, Δt RISE-A Indicates the anode signal rise time; S22: Construct identification parameters based on the Bragg peak characteristics of charged particles, that is, the relationship between the peak position of particle energy deposition in the medium and the particle range. , and the particle range and energy E The calculation formula is: (2) In formula (2), Q A Indicates the amplitude of the anode signal; S23: Constructing identification parameters for identifying double tritium events k , and its calculation formula is: (3) In formula (3), , are the energy and range of the two tritium nuclei in a double tritium event.

[0009] Preferably, in step S3, by plotting the anode signal amplitude Q A , Anode signal rise time Δt RISE-A The two-dimensional spectrum of the start time difference Δt between the cathode and anode signals is used to observe the distribution characteristics of different particle events.

[0010] Further preferably, in step S4, the method for identifying different particles includes: For low-energy regions with neutron energies less than 0.1 MeV: According to Δt RISE-A -Q A and Δt-Q A Figure,identify tritium nuclei and filter out noise signals by setting threshold conditions; For the resonance energy region between 0.1 and 1 MeV neutron energies: Identify double tritium events, -Q A Draw a straight line connecting the centers of the tritium and ditritium clusters in the figure to identify the parameters k That is, the slope is k. Project each point in the figure onto the straight line, distinguish tritium nuclei and double tritium events by double Gaussian fitting, and filter high-energy tritium nuclei by setting the time difference threshold; For high energy regions with neutron energies greater than 1 MeV: Identify alpha particles based on Δt-Q A Figure 2. The segmentation lines are drawn by visual inspection, and the alpha particle bands are obtained by using quadratic polynomial fitting to distinguish the alpha particles.

[0011] More preferably, the noise signal is filtered by setting a threshold condition, the threshold condition is ∆t<6 µs, ∆t RISE-A <4 µs and Q A >120 ADC.

[0012] More preferably, high-energy tritium nuclei are filtered by setting a time difference threshold, and the time difference threshold is set to ∆t>10µs.

[0013] The present invention also discloses a charged particle identification system based on a screen grid ionization chamber, which can implement the above-mentioned charged particle identification method, including: A signal acquisition module is used to collect signals generated by charged particles at the cathode and anode of the detector through a screen grid ionization chamber; A feature extraction module is used to analyze and generate feature quantities based on the signals collected by the signal collection module; A particle identification parameter construction module is used to construct particle identification parameters according to characteristic quantities; A two-dimensional spectrum generation module is used to generate a two-dimensional spectrum according to the identification parameters and observe the distribution characteristics of different particle events; The particle identification module is used to identify different particles based on the distribution characteristics of different particle events in the two-dimensional spectrum.

[0014] The present invention also discloses a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned charged particle identification method when executing the computer program.

[0015] The present invention also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the charged particle identification method are implemented.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The charged particle identification method based on the screen grid ionization chamber disclosed in the present invention is based on the working principle of GIC, by obtaining the signals generated by the charged particles at the cathode and anode of the detector and generating characteristic quantities, constructing particle identification parameters according to the characteristic quantities, and by combining advanced signal processing technology and multi-parameter analysis, the distribution characteristics of different particle events can be observed, so that alpha particles, tritium nuclei and ditritium nuclei events can be accurately identified, while effectively suppressing proton interference, and associating particle energy with ionization range during identification, solving the limitation problem of traditional particle identification technology in high event rate and complex particle product environment, especially the ability to handle the superposition phenomenon of tritium nuclei events in the resonance energy region, so as to solve the defective problem of the prior art that the detection range is limited due to the low energy resolution and the inability to accurately identify complex particle types. The identification method of the present invention is highly adaptable and can adapt to different neutron energy regions, including low energy, resonance energy and high energy regions.

[0017] Furthermore, by setting a suitable threshold, the interference of high-energy protons is effectively eliminated, thereby improving the accuracy and reliability of the measurement, which is particularly critical in experiments in high-energy regions where proton interference may seriously affect the measurement results. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the process of the charged particle identification method of the present invention; Figure 2 The GIC detector system described in the embodiment of the present invention; Figure 3 is the Δt-Q of different particles described in the embodiments of the present invention A Two-dimensional spectra; Figure 4 is the Δt-Q used for low energy region particle identification in the embodiment of the present invention A Two-dimensional spectra; Figure 5 is Δt used for low energy region particle identification in the embodiment of the present invention RISE-A -Q A Two-dimensional spectra; Figure 6 g[E, R(E)]-Q for identifying particles in the mid-energy region described in the embodiment of the present invention A Two-dimensional spectra; Figure 7It is a double Gaussian distribution fitting diagram of tritium and double tritium events described in an embodiment of the present invention; Figure 8 is the Δt-Q used for high energy region particle identification in the embodiment of the present invention A Two-dimensional spectrum. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0020] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] The present invention is further described in detail below in conjunction with the accompanying drawings: The present invention provides a technical solution: a charged particle identification method based on a screen grid ionization chamber, specifically comprising the following steps: Figure 1 As shown: S1. Signal acquisition and feature generation.

[0022] The signal acquisition and feature quantity generation method in S1 includes: S11. This embodiment uses Figure 2 The GIC detector system shown in the figure is placed in a vacuum chamber with a vacuum degree of 0.01 Pa and an incident angle of 120° for the neutron beam to reduce the collection of scattered protons. The target is a stainless steel substrate with a surface coated with a rich 6The target is rotated 60° to reduce the energy loss of charged particles. The above system is used to obtain the signals generated by charged particles at the cathode and anode of the detector. The signal of the detector is generated by the drift of ionized electron-ion pairs under the action of an external electric field. Due to the shielding effect of the gate, the signal waveforms of the cathode and anode are not the same. Different signal characteristics reflect the different properties of the incident charged particles.

[0023] S12. Analyze the signal obtained in S11 to extract characteristic quantities, including: the amplitude Q of the anode signal A , Anode signal rise time Δt RISE-A , the time difference between the cathode and anode signals Δt. The obtained characteristic quantity is related to the incident particles and the design parameters of the detector. The specific mathematical model is: (1.1) In formula (1.1), is the amplification factor, is the number of ionized electrons, is the shielding ratio of the gate, and is the center of gravity of the electron cluster, is the angle of incidence, is the distance from cathode to grid.

[0024] (1.2) (1.3) In formula (1.2), and Represents the start time of the signal. The cathode signal starts when the particle is incident, while the anode signal starts when the electron cluster reaches the gate. In formula (1.3) and are the drift velocities of electrons in the cathode and anode regions, respectively. is the distance from the gate to the cathode.

[0025] S2. Use the obtained characteristic quantities to construct particle identification parameters.

[0026] The process of constructing particle identification parameters using the obtained characteristic quantities described in S2 includes the following specific steps: S21. Among the above three characteristics, Q A Dependent on the incident particle energy, Δt and Δt Edge-A It is related to the range of the incident particles, so it is selected as the parameter for particle identification.

[0027] S22. Constructing identification parameters , in the case of small angle incidence, it is easy to see from the calculation formula It is only related to the particle range R, and its calculation formula is: (2.1) S23. Construct identification parameters based on the Bragg peak characteristics of charged particles, that is, the relationship between the peak position of particle energy deposition in the medium and the particle range. , which is related to the particle's range R and energy E, and its calculation formula is: (2.2) S24. To identify double tritium events, an identification parameter k is constructed, and its calculation formula is: (2.3) In formula (2.3), , is the energy and range of the two tritium nuclei in the double tritium event. If the two superimposed tritium nuclei have different energies, the time information of the double tritium event is determined by the tritium nucleus with a longer range, because the particle with a longer range will cover the trajectory of the particle with a shorter range, satisfying the calculation of the above formula.

[0028] S3. Generation and analysis of two-dimensional spectra: As the neutron energy increases, the incident neutrons will trigger more complex reaction channels. Neutrons with energy below 0.1 MeV will pass through 6 Li(n, t) 4 The He reaction channel produces particles. When the neutron energy enters the resonance energy region of 0.1 to 1 MeV, the event rate increases significantly, resulting in the coincidence of random events and the generation of double tritium events. When the neutron energy exceeds 1 MeV, the reaction channel becomes more complicated and is affected by high-energy proton noise. Therefore, it is not appropriate to measure the cross section only by counting the number of effective events. It is also necessary to remove the noise signal and perform particle identification.

[0029] In this embodiment, a two-dimensional spectrum is generated by multi-parameter analysis to identify different particles. Due to the strong correlation between the anode signal amplitude and the particle energy, and the relationship between the anode signal time and the particle range, the characteristics of different particle energy and range can be reflected in the two-dimensional spectrum of the anode signal amplitude and time parameters. Figure 3 As shown in the figure, alpha particles and tritium nuclei form clear bands respectively. According to the maximum energy of the incident particles, that is, the signal amplitude, the correspondence between different bands and different particle types can be determined. However, directly analyzing the entire data set is too complicated, so a segmented method is needed to identify particles in different energy regions.

[0030] S4. Identify different particles based on the particle distribution in the two-dimensional spectrum.

[0031] The method for identifying different particles described in S4 comprises the following specific steps: S41. In this embodiment, for the low energy region where the neutron energy is less than 0.1 MeV, according to Δt-Q A and Δt RISE-A -Q A Figure, such as Figure 4 and Figure 5 As shown in the figure, there is only one particle cluster, mainly tritium nucleus events. In order to filter out noise signals, some thresholds need to be set, such as ∆t RISE-A <6 μs, ∆t<4 μs and Q A >120 ADC. After filtering, the proportion of valid events to total events was 96.42%.

[0032] S42. In this embodiment, for the resonance energy region between 0.1 and 1 MeV, tritium nuclei and ditritium events mainly occur. When the neutron energy reaches this energy region, the significantly increased event rate triggers the random coincidence of events, which in turn leads to the generation of ditritium events. In order to filter out most of the noise signals, a threshold of ∆t>10 µs is set in this embodiment to retain the valid data of the main tritium nuclei and ditritium events. In this energy region, tritium nuclei and ditritium events occur within ∆t-Q A and Δt RISE-A -Q A The distributions in the figure are relatively similar, so new parameters are used to construct Q A and The two-dimensional spectrum of Figure 6 As shown, each point in the figure represents a particle incident event.

[0033] First, through visual inspection, -Q A The cluster distribution of tritium nuclei and ditritium events is identified in the figure, and the centers of the above clusters are connected by a straight line with a slope of k as the relationship between particle energy and range. In this embodiment, k=1.5.

[0034] Projecting the data points in the two-dimensional spectrum onto this straight line, both tritium nuclei and ditritium events follow Gaussian distribution. Regardless of rotation or projection, the two-dimensional Gaussian distribution is always consistent with the Gaussian distribution. Therefore, the projection results are fitted using the double Gaussian distribution model to obtain the relationship between the projection distance and counts of the two events, as shown in Figure 7 By calculating the area of ​​the fitted Gaussian peak, the proportion of different events to the total number of events is obtained. In this embodiment, the double tritium events account for 4.65% of the fitted events, and the numbers of tritium nuclei and double tritium events are 47,377 and 2,199, respectively.

[0035] S43. In this embodiment, for the high-energy region with neutron energy greater than 1 MeV, tritium nuclei and alpha particles are mainly identified. As the neutron energy increases, more high-energy tritium nuclei and alpha particle events are incident on the screen grid ionization chamber, while high-energy protons can completely penetrate the sensitive area of ​​the screen grid ionization chamber, resulting in a very short Δt of high-energy proton events. Based on this, the high-energy proton signal can be filtered out first. The threshold condition used in this embodiment is Δt>1 μs, and about 6.4% of the events are screened out, totaling 2,683 events.

[0036] Identification of tritium nuclei and alpha particles. Figure 8 The Δt-Q A Figure 1 shows that the particle distribution is mainly divided into two bands through visual inspection. According to the energy range, the left side is the tritium nucleus band and the right side is the alpha particle band. After fitting the original alpha particle band with a quadratic polynomial, its central distribution line is obtained. By lateral translation of the fitting curve, it is made to pass through the blank area between the alpha particle band and the tritium nucleus band as the dividing line between alpha particles and tritium nuclei. The final statistical results are: 27,464 tritium nucleus events and 11,728 alpha particle events.

[0037] In another embodiment, the present invention also provides a charged particle identification system based on a screen grid ionization chamber, which can implement the above-mentioned charged particle identification method, including: A signal acquisition module is used to collect signals generated by charged particles at the cathode and anode of the detector through a screen grid ionization chamber; A feature extraction module is used to analyze and generate feature quantities based on the signals collected by the signal collection module; A particle identification parameter construction module is used to construct particle identification parameters according to characteristic quantities; A two-dimensional spectrum generation module is used to generate a two-dimensional spectrum according to the identification parameters and observe the distribution characteristics of different particle events; The particle identification module is used to identify different particles based on the distribution characteristics of different particle events in the two-dimensional spectrum.

[0038] In another embodiment of the present invention, a computer device is provided, the computer device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor implements the steps of the above-mentioned charged particle identification method when executing the computer program. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding functions; the processor described in the embodiment of the present invention can be used to implement the steps of the above-mentioned charged particle identification method when the processor executes the computer program.

[0039] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a computer device for storing programs and data. When the computer program in this embodiment is executed by a processor, the steps of the above-mentioned charged particle identification method are implemented. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the steps of the charged particle identification method in the above-mentioned embodiment.

[0040] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0041] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products of the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0042] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0043] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0044] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A charged particle identification method, characterized in that: The following steps are involved: S1: Acquire the signals generated by charged particles at the cathode and anode of the detector and generate characteristic quantities; S2: Use the obtained characteristic quantities to construct particle identification parameters; S3: Generate a two-dimensional spectrum based on the identification parameters and observe the distribution characteristics of different particle events; S4: Identify different particles based on the distribution characteristics of different particle events in the two-dimensional spectrum.

2. The charged particle identification method according to claim 1, characterized in that: In step S1, the generated characteristic quantities include: anode signal amplitude, anode signal rise time, and cathode and anode signal start time difference.

3. The charged particle identification method according to claim 1, characterized in that: In step S2, the obtained characteristic quantity is used to construct particle identification parameters, which specifically includes: S21: Constructing recognition parameters , at small angles of incidence, Only with particle range The calculation formula is: (1) In formula (1), Δt represents the start time difference between the cathode and anode signals, Δt RISE-A Indicates the anode signal rise time; S22: Construct identification parameters based on the Bragg peak characteristics of charged particles, that is, the relationship between the peak position of particle energy deposition in the medium and the particle range. , and the particle range and energy E The calculation formula is: (2) In formula (2), Q A Indicates the amplitude of the anode signal; S23: Constructing identification parameters for identifying double tritium events k , and its calculation formula is: (3) In formula (3), , are the energy and range of the two tritium nuclei in a double tritium event.

4. The charged particle identification method according to claim 1, characterized in that: In step S3, by plotting the anode signal amplitude Q A , Anode signal rise time Δt RISE-A The two-dimensional spectrum of the start time difference Δt between the cathode and anode signals is used to observe the distribution characteristics of different particle events.

5. The charged particle identification method according to claim 4, characterized in that: In step S4, the method of identifying different particles includes: For low-energy regions with neutron energies less than 0.1 MeV: According to Δt RISE-A -Q A and Δt-Q A Figure,identify tritium nuclei and filter out noise signals by setting threshold conditions; For the resonance energy region between 0.1 and 1 MeV neutron energies: Identify double tritium events, -Q A Draw a straight line connecting the centers of the tritium and ditritium clusters in the figure to identify the parameters k That is, the slope is k. Project each point in the figure onto the straight line, distinguish tritium nuclei and double tritium events by double Gaussian fitting, and filter high-energy tritium nuclei by setting the time difference threshold; For high energy regions with neutron energies greater than 1 MeV: Identify alpha particles based on Δt-Q A Figure 2. The segmentation lines are drawn by visual inspection, and the alpha particle bands are obtained by using quadratic polynomial fitting to distinguish the alpha particles.

6. The charged particle identification method according to claim 5, characterized in that: The noise signal is filtered by setting the threshold condition, the threshold condition is ∆t<6 µs, ∆t RISE-A <4 µs and Q A >120 ADC.

7. The charged particle identification method according to claim 5, characterized in that: High energy tritium nuclei were filtered out by setting the time difference threshold, which was set to ∆t>10 µs.

8. A charged particle identification system based on a screen grid ionization chamber, characterized in that: A method for identifying charged particles according to any one of claims 1 to 7 is implemented, comprising: A signal acquisition module is used to collect signals generated by charged particles at the cathode and anode of the detector through a screen grid ionization chamber; A feature extraction module is used to analyze and generate feature quantities based on the signals collected by the signal collection module; A particle identification parameter construction module is used to construct particle identification parameters according to characteristic quantities; A two-dimensional spectrum generation module is used to generate a two-dimensional spectrum according to the identification parameters and observe the distribution characteristics of different particle events; The particle identification module is used to identify different particles based on the distribution characteristics of different particle events in the two-dimensional spectrum.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the charged particle identification method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the charged particle identification method described in any one of claims 1 to 7 are implemented.

Images