Anti-interference data processing method and system for space particle detection

Abstract

This invention provides an anti-interference data processing method for space particle detection. It involves establishing a simulation model of a semiconductor telescope detector; uniformly distributing a plurality of test particles on a virtual spherical shell centered on and surrounding the simulation model; statistically analyzing the energy deposited by each test particle in simulated collisions with the semiconductor material of the simulation model; performing energy band inversion on the energy to obtain the inverted energy band corresponding to the test particle; establishing a relationship matrix between the actual energy bands of the test particles and their corresponding inverted energy bands; and obtaining the actual energy distribution result of the test particles through data post-processing of the relationship matrix. Therefore, this invention enables the use of only the necessary semiconductor detector, eliminating the need for additional sensors to detect particles in non-detection directions, thus simplifying the design of the semiconductor telescope and reducing its size, weight, and power consumption.

Description

Technical Field

[0001] This invention relates to the field of space environment detection technology, and in particular to an anti-interference data processing method and system for space particle detection. Background Technology

[0002] Currently, the main international methods for measuring high-energy charged particles include magnetic deflection, aperture imaging, and semiconductor telescope methods.

[0003] The magnetic deflection method emerged in the 1990s. Its principle is that when a charged particle moves in a magnetic field, it continuously changes direction due to the Lorentz force, tracing a circular path. Using Newton's second law, Bvq = mv² / R (where B is the magnetic field strength and q is the charge of the particle), the deflection radius of the charged particle can be calculated as R = mv / (Bq). Since the mass of an electron is less than one-thousandth that of other ions, its deflection radius is much smaller than that of other particles, meaning electrons are more easily deflected in a magnetic field. However, this method is not suitable for high-energy particles.

[0004] Aperture imaging uses position-sensitive semiconductor detectors, which can enhance the spatial resolution of measurements. To ensure the aperture angle, the detector can only have a tiny aperture, on the order of millimeters, and cannot be fitted with a light-blocking layer, placing high demands on thermal control. The detector's ability to distinguish between protons and electrons is not strong. This approach is more suitable for spin-stabilized satellites. For example, the EPS (Energy Power Precipitation) detectors on the CLUSTER and POLAR satellites employ aperture imaging.

[0005] The semiconductor telescope method is currently the mainstream method for measuring high-energy particles. It uses silicon semiconductor sensors to form a telescope, measuring energy particles through amplitude analysis and coincidence / anti-coincidence logic. Energy channel segmentation is achieved through amplitude analysis, while proton and electron identification is achieved through coincidence / anti-coincidence circuits. Because protons have a short range and high energy loss per unit distance, while electrons have a long range and low energy loss per unit distance, energy measurement and composition identification can be performed by the detectors triggered by the incident particles and the amplitudes triggered in each detector. The advantages of the telescope method are its simple instrument design and particle circuitry, and its straightforward principle. As long as the instrument manufacturing technology meets the requirements, it can well meet the needs of energy segmentation and measurement. Therefore, the semiconductor telescope method is currently widely used in the detection of high-energy charged particles.

[0006] When using semiconductor telescopes, it's crucial to address the issue of eliminating interference from particles outside the detection direction. Simply using structural materials for shielding is insufficient to completely eliminate high-energy particles (on the order of hundreds of megaelectron volts). To address this, a common approach is to install particle-sensitive sensors, such as scintillators, along the particle's incident path outside the detection direction. Signal processing then checks for signals from these sensors to eliminate the influence of interfering particles. This method effectively solves the problem of interference from particles in other directions, but it also increases the overall structural complexity, leading to increased size, weight, and power consumption.

[0007] In summary, the existing methods have many problems in practical use, so it is necessary to improve them. Summary of the Invention

[0008] To address the aforementioned shortcomings, the present invention aims to provide an anti-interference data processing method and apparatus for space particle detection, which can achieve miniaturization of high-energy particle detection using semiconductor telescopes.

[0009] To achieve the above objectives, the present invention provides an anti-interference data processing method for space particle detection, comprising the following steps: Establish a simulation model for the semiconductor telescope detector; A plurality of test particles are uniformly distributed at a specified spatial location; wherein, the specified spatial location is a virtual spherical shell with the simulation model as the center and surrounding the simulation model, and the plurality of test particles have a uniform energy distribution within the detection energy range of the semiconductor telescope detector; The energy deposited by the particle under test in the simulated collision with the semiconductor material of the simulation model is statistically analyzed. Energy range inversion is performed on the energy to obtain the inversion energy range corresponding to the particle under test; Establish a relationship matrix between the actual energy ranges of several particles to be tested and the corresponding inversion energy ranges; By performing data post-processing on the relation matrix, the true energy distribution of the particle under test is obtained.

[0010] Optionally, the step of establishing a simulation model of the semiconductor telescope detector includes: A simulation model of a semiconductor telescope detector is built in the Monte Carlo program. The simulation model includes a simulated semiconductor sensor and a support structure.

[0011] Optionally, the step of performing energy range inversion on the energy to obtain the inversion energy range corresponding to the particle to be tested includes: Based on the energy identification method under undisturbed particle state, the energy is inverted and measured to obtain the inversion energy range corresponding to the particle to be measured.

[0012] Optionally, the step of establishing the relationship matrix between the true energy ranges of the plurality of particles to be tested and the corresponding inversion energy ranges includes: The actual energy range of the particle under test is segmented and marked; The number of particles in each real energy range that the test particle falls into the inversion energy range is counted. Establish a relational matrix with the horizontal axis representing the true energy range, the vertical axis representing the inverted energy range, and the matrix value representing the number of particles.

[0013] Optionally, the step of obtaining the true energy distribution of the particle under test by performing data post-processing on the relation matrix includes: By normalizing and correcting the matrix values ​​in the relation matrix, the true energy distribution of the particle under test can be obtained.

[0014] Optionally, the step of normalizing and correcting the matrix values ​​in the relation matrix to obtain the true energy distribution of the particle under test includes: Divide each of the matrix values ​​by the total number of particles identified as belonging to the same inversion energy range to obtain a normalized first proportional matrix. The relationship matrix is ​​corrected based on the first proportional coefficient in the first proportional matrix; Divide the modified relation matrix value by the total number of particles identified as belonging to the same real energy range to obtain a normalized second proportional matrix. Based on the second proportional coefficient of the second proportional matrix, the relationship is corrected to obtain the true energy distribution result of the particle under test.

[0015] Optionally, let the first correction function of the relation matrix, based on the first proportional matrix, be: ; in, The matrix value is the same as the actual energy range in the first scaling matrix and the inverted energy range. C i To be identified as an energy segment i The total number of particles, R These are the matrix values ​​in the first proportional matrix. i Represented as a real energy segment, j This is represented as the inversion energy segment.

[0016] Optionally, let the second correction function of the relation matrix, based on the second proportional matrix, be: ; in, N real，i For energy segment i The true number of particles to be tested. S i The actual energy segment is i The number of the test particles identified. M The total number of segments, R The matrix values ​​are those in the second proportional matrix. i Represented as a real energy segment, j This is represented as the inversion energy segment.

[0017] Optionally, the semiconductor telescope detector consists of three semiconductor sensors.

[0018] An anti-interference data processing system for space particle detection is also provided, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that the processor, when executing the computer program, implements any of the aforementioned anti-interference data processing methods for space particle detection.

[0019] The anti-interference data processing method and system for space particle detection described in this invention involves: establishing a simulation model of a semiconductor telescope detector; uniformly distributing a plurality of test particles on a virtual spherical shell centered on and surrounding the simulation model; statistically analyzing the energy deposited by each test particle in a simulated collision with the semiconductor material of the simulation model; performing energy band inversion on the energy to obtain the inverted energy band corresponding to the test particle; establishing a relationship matrix between the actual energy bands of the test particles and their corresponding inverted energy bands; and obtaining the actual energy distribution result of the test particles through data post-processing of the relationship matrix. Therefore, this invention enables the use of only the necessary semiconductor detector, eliminating the need for additional sensors to detect particles in non-detection directions, thus simplifying the design of the semiconductor telescope and reducing its size, weight, and power consumption. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the steps of the anti-interference data processing method for space particle detection provided in an embodiment of the present invention. Figure 2 A schematic diagram of the simulation model established by the anti-interference data processing method for space particle detection provided in an embodiment of the present invention; Figure 3 This is a simulation diagram of the trajectory of an isotropic particle under test on the upper part of the virtual spherical shell in the anti-interference data processing method for space particle detection provided in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] It should be noted that references to "an embodiment," "embodiment," "example embodiment," etc., in this specification refer to the described embodiment including specific features, structures, or characteristics, but not every embodiment must include these specific features, structures, or characteristics. Furthermore, such expressions do not refer to the same embodiment. Moreover, when describing specific features, structures, or characteristics in conjunction with embodiments, whether or not explicitly described, it is indicated that incorporating such features, structures, or characteristics into other embodiments is within the knowledge of those skilled in the art.

[0023] Furthermore, certain terms are used in the specification and subsequent claims to refer to specific components or parts. Those skilled in the art will understand that manufacturers may use different names or terms to refer to the same component or part. This specification and subsequent claims do not distinguish components or parts by differences in name, but rather by differences in function. The terms "comprising" and "including" used throughout the specification and subsequent claims are open-ended and should be interpreted as "including but not limited to." Additionally, the term "connection" here includes any direct and indirect electrical connection means. Indirect electrical connection means include connections made through other means.

[0024] Figure 1 This invention illustrates an anti-interference data processing method for space particle detection according to an embodiment of the present invention. The method is specifically applied to eliminate interference from particles originating from directions other than the detection direction, and includes the following steps: S101: Establish a simulation model of the semiconductor telescope detector. Specifically, step S101 includes: establishing a simulation model of the semiconductor telescope detector in a Monte Carlo program. The simulation model includes simulated semiconductor sensors and support structures. The Monte Carlo program is specifically Monte Carlo particle radiation software; that is, the simulation model of the semiconductor telescope detector is established using Monte Carlo particle radiation software. The simulation model includes the necessary semiconductor sensors and support structures required for detection. In this embodiment, the semiconductor telescope detector consists of three semiconductor sensors, such as… Figure 2 As shown.

[0025] S102: A plurality of test particles are uniformly distributed at a specified spatial location; wherein, the specified spatial location is a virtual spherical shell centered on and surrounding the simulation model, the virtual spherical shell being a spatial structure that encloses the simulation model to be analyzed and is used to generate particles during simulation, serving as the particle source; the plurality of test particles have a uniform energy distribution within the detection energy range of the semiconductor telescope detector, such as... Figure 3 As shown.

[0026] S103: Calculate the energy deposited by each test particle in the simulated collision with the semiconductor material of the simulation model. After the model is constructed and the particles are set, simulation software is used to simulate the interaction between the particles and the semiconductor material. The specific simulation method can be the Monte Carlo method commonly used in radiation simulation, which will not be elaborated here. After the particle-material simulation interaction, calculate the energy deposited by each test particle in each semiconductor of the simulation model.

[0027] S104: Perform energy segment inversion on the energy to obtain the inversion energy segment corresponding to the particle under test. Inversion refers to solving for some input information or certain unknown structural features of the system by reverse engineering based on some output information of the system, after understanding the development and interaction laws of the system; that is, inversion is a method of finding the cause from the effect. After obtaining the energy deposited by each particle under test through simulation, the statistical energy is used as the "effect" of inversion to reverse analyze the "cause"—the energy segment of the particle under test, which is the inversion energy segment. In specific implementation, step S104 includes: performing inversion measurement on the energy based on the energy identification method under the interference-free particle state to obtain the inversion energy segment corresponding to the particle under test; that is, inverting the energy segment of the incident particle according to the energy identification method under the interference-free particle state, the result of this inversion energy segment has been mixed with interference particle information.

[0028] S105: Establish a relationship matrix between the actual energy ranges of several particles to be tested and their corresponding inversion energy ranges. In the simulation experiment, a particle generated by a program will have its energy preset; the energy range at which this energy is located is the actual energy range of the particle to be tested. The actual energy range and the inversion energy range are opposites; the inversion is based on the measurement results and may not correspond to the actual energy range.

[0029] In one optional implementation, step S105 specifically includes: dividing and marking the true energy range of the particle to be tested; counting the number of particles in each true energy range that fall into the inversion energy range; and establishing a relational matrix with the horizontal axis of the matrix representing the true energy range, the vertical axis of the matrix representing the inversion energy range, and the matrix value representing the number of particles.

[0030] For example, the actual energy segment is iThe results of particle detection are j The number of particles is recorded in ( i , j In the matrix shown in Table 1, M represents the total number of energy segments. Table 1 shows two total calculations, where S in the total S... i Indicates the actual energy segment is i How many particles were identified in total; the total C in C i This indicates that it has been identified as an energy segment. i The total number of particles; C i That is, the initial (uncorrected) instrument measurement results.

[0031] Table 1:

[0032] Based on the principle of the silicon semiconductor telescope method, it is known that most low-energy interfering particles are unlikely to produce charge signals similar to those of high-energy particles in different semiconductors. Therefore, they cannot generate inversion energy results higher than their own energy range during the inversion process. When the particle energy increases, particles incident from other directions may produce interference signals similar to those of particles with higher energies than themselves. Therefore, in the table above, most of the lower triangular portion is close to 0, but a small portion is not close to 0, as shown in Table 2.

[0033] Table 2:

[0034] Since Table 2 is not a standard upper triangular matrix, it is not possible to perform a complete inversion based on it. It is necessary to process the coefficients in the lower triangular part of the matrix that cannot be approximated. The processing of the data in the table is achieved by the following step S106.

[0035] S106: By performing data post-processing on the relation matrix, the true energy distribution result of the particle to be tested is obtained. That is, this embodiment uses the above-mentioned simulation method to obtain the influence of interfering particles on the original detection results of the semiconductor telescope, and uses data post-processing methods to eliminate them. In specific implementation, step S106 includes: normalizing and correcting the matrix values ​​in the relation matrix to obtain the true energy distribution result of the particle to be tested.

[0036] In an optional implementation, the step of obtaining the true energy distribution of the particle under test by normalizing and correcting the matrix values ​​in the relation matrix includes: Divide each of the matrix values ​​by the total number of particles identified as belonging to the same inversion energy band to obtain the normalized first proportional matrix. In this embodiment, the particle energy distribution used in establishing the relationship matrix is ​​based on the particle energy spectrum model of the orbit, and will not deviate significantly from the actual detection results. Referring to Table 2 above, the method for processing the particle count in the lower triangular portion of the table that cannot be approximated is as follows: count N particles in Table 2... i,j Divide by the total number of particles in each row (i.e., C) i By doing so, we can obtain another normalized first proportional matrix, as shown in Table 3.

[0037] Table 3:

[0038] Based on the results in Table 3, if there is a non-zero term in the lower triangular matrix in a row, it indicates the inverted particle count (C). i The matrix contains interference from other energy ranges. Based on the scaling factors in Table 3, this interference can be eliminated, yielding an approximate particle count on the diagonal of the matrix, with the count in the lower triangle set to 0. That is, the relationship matrix is ​​corrected based on the first scaling factor in the first scaling matrix. Let the first correction function for the relationship matrix, based on the first scaling matrix, be: ; in, The matrix value is the same as the actual energy range in the first scaling matrix and the inverted energy range. C i To be identified as an energy segment i The total number of particles, R These are the matrix values ​​in the first proportional matrix. i Represented as a real energy segment, j This is represented as the inversion energy range. After this correction, Table 2 can be transformed into Table 4.

[0039] Table 4:

[0040] Table 4 shows the particle count statistics matrix between the actual and inverted energy ranges after ignoring minor and approximate values. Then, the matrix values ​​after correcting the relationship matrix are divided by the total number of particles identified as belonging to the same actual energy range to obtain a normalized second proportional matrix; that is, the particle counts N in Table 4... i,j Divide by the total number of particles in each column (i.e., S) i By doing so, we can obtain the normalized second proportional matrix, as shown in Table 5.

[0041] Table 5:

[0042] Based on the results in Table 5, the actual energy distribution of particles in each energy range can be obtained. The correction method is as follows: based on the second proportional coefficient of the second proportional matrix, the relationship is corrected to obtain the actual energy distribution of the particles to be tested.

[0043] Let the second correction function of the relation matrix, based on the second proportional matrix, be: ; in, N real，i For energy segment i The true number of particles to be tested. S i The actual energy segment is i The number of the test particles identified. M The total number of segments, R The matrix values ​​are those in the second proportional matrix. i Represented as a real energy segment, j This is represented as the inversion energy segment.

[0044] In this way, the true particle energy distribution can be obtained from the inversion results mixed with interfering particle information through data post-processing.

[0045] In a specific application example, the particle type to be measured is set as a proton. The semiconductor telescope detector consists of three semiconductor sensors. The detection energy range of the semiconductor telescope detector is 5~200 MeV, divided into four energy bands for detection: 5~10 MeV, 10~30 MeV, 30~100 MeV, and 100~200 MeV. Based on the method described in the above embodiment, after establishing a simulation model, a proton source with an energy range of 5~200 MeV is used to emit uniformly distributed protons on a spherical surface that can surround the semiconductor telescope. In particular, it is assumed that the particle energy distribution follows a logarithmic distribution with a coefficient of -2.0. The actual incident protons in energy bands 1~4 are statistically analyzed. After the detector judges the results, the response distribution in energy bands 1~4, including interference information, is obtained, resulting in a 4th order matrix, as shown in Table 6. Table 6:

[0046] Based on the detection results including interference information, and combined with the matrix shown in Table 6, the actual incident particle quantity distribution in energy bands 1-4 can be calculated. Table 7 shows the normalized particle quantity matrices for the actual and inverted energy bands obtained statistically.

[0047] Table 7:

[0048] Furthermore, regarding the above processing results, the relevant test calculations are as follows: Another simulation was performed. The energy distribution of the incident particles was basically consistent with the distribution when the matrix was established, but the number of incident particles was different. This simulation simulated the actual detection process, meaning it could only obtain the particle energy segment count information after the instrument's own inversion, as shown in Table 8. Substituting the total values ​​in Table 8 into the correction calculation process, the corrected results for each energy segment were: 589.2, 428.4, 47.4, and 6.3. The values ​​on the diagonal of the matrix in Table 8 are the actual results obtained from the simulation, which are 563, 419, 44, and 5, respectively. It can be seen that the corrected results are very close to the actual results.

[0049] Table 8:

[0050] An anti-interference data processing system for space particle detection is also provided, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the computer program to implement any of the aforementioned anti-interference data processing methods for space particle detection.

[0051] In summary, the anti-interference data processing method and system for space particle detection described in this invention involves: establishing a simulation model of a semiconductor telescope detector; uniformly distributing a plurality of test particles on a virtual spherical shell centered on and surrounding the simulation model; statistically analyzing the energy deposited by the test particles in simulated collisions with the semiconductor material of the simulation model; performing energy band inversion on the energy to obtain the inverted energy band corresponding to the test particle; establishing a relationship matrix between the actual energy bands of the test particles and their corresponding inverted energy bands; and obtaining the actual energy distribution result of the test particles through data post-processing of the relationship matrix. Therefore, this invention enables the use of only the necessary semiconductor detector, eliminating the need for additional sensors to detect particles in non-detection directions, thus simplifying the design of the semiconductor telescope and reducing its size, weight, and power consumption.

[0052] Of course, the present invention may have other various embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and modifications according to the present invention, but these corresponding changes and modifications should all fall within the protection scope of the appended claims.

Claims

1. An anti-jamming data processing method for spatial particle detection, characterized in that, Including the following steps: A simulation model of a semiconductor telescope detector is established; the semiconductor telescope detector consists of three semiconductor sensors; the simulation model does not include additional sensors for excluding particles in non-detection directions; A plurality of test particles are uniformly distributed at a specified spatial location; wherein, the specified spatial location is a virtual spherical shell with the simulation model as the center and surrounding the simulation model, and the plurality of test particles have a uniform energy distribution within the detection energy range of the semiconductor telescope detector; The energy deposited by the particle under test in the simulated collision with the semiconductor material of the simulation model is calculated separately. Energy range inversion is performed on the energy to obtain the inversion energy range corresponding to the particle under test; Establish a relationship matrix between the actual energy ranges of several particles to be tested and the corresponding inversion energy ranges; By performing data post-processing on the relation matrix, the true energy distribution of the particle under test can be obtained. The step of normalizing and correcting the matrix values ​​in the relation matrix to obtain the true energy distribution of the particle under test includes: Divide each of the matrix values ​​by the total number of particles identified as belonging to the same inversion energy range to obtain a normalized first proportional matrix. The relationship matrix is ​​corrected based on the first proportional coefficient in the first proportional matrix; Divide the corrected value of the relation matrix by the total number of particles identified as belonging to the same real energy range to obtain a normalized second proportional matrix. The relationship matrix is ​​corrected based on the second proportional coefficient of the second proportional matrix to obtain the true energy distribution result of the particle under test.

2. The anti-interference data processing method for space particle detection according to claim 1, characterized in that, The steps for establishing a simulation model of the semiconductor telescope detector include: A simulation model of a semiconductor telescope detector is built in the Monte Carlo program. The simulation model includes a simulated semiconductor sensor and a support structure.

3. The anti-interference data processing method for space particle detection according to claim 1, characterized in that, The step of performing energy range inversion on the energy to obtain the inversion energy range corresponding to the particle to be tested includes: Based on the energy identification method under undisturbed particle state, the energy is inverted and measured to obtain the inversion energy range corresponding to the particle to be measured.

4. The anti-interference data processing method for space particle detection according to claim 1, characterized in that, The step of establishing the relationship matrix between the actual energy ranges of the plurality of particles to be tested and the corresponding inversion energy ranges includes: The actual energy range of the particle under test is segmented and marked; The number of particles in each real energy range that the test particle falls into the inversion energy range is counted. Establish a relational matrix with the horizontal axis representing the true energy range, the vertical axis representing the inverted energy range, and the matrix value representing the number of particles.

5. The anti-interference data processing method for space particle detection according to claim 1, characterized in that, Let the first correction function of the relation matrix, based on the first proportional matrix, be: ; in, The matrix value is the same as the actual energy range in the first scaling matrix and the inverted energy range. C i To be identified as an energy segment i The total number of particles, R The matrix value is in the first proportional matrix. i Represented as the actual energy segment, j This is represented as the inversion energy segment.

6. The anti-interference data processing method for space particle detection according to claim 1, characterized in that, Let the second correction function of the relation matrix, based on the second proportional matrix, be: ; in, N real，i For energy segment i The true number of particles to be tested. S i The actual energy segment is i The number of the test particles identified. M The total number of segments, R The matrix values ​​are those in the second proportional matrix. i Represented as the actual energy segment, j This is represented as the inversion energy segment.

7. An anti-interference data processing system for space particle detection, characterized in that, The invention includes a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, when the processor executes the computer program, it implements the anti-interference data processing method for space particle detection as described in any one of claims 1 to 6.

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