An optimization method and system for a detection device based on gamma geometric source decomposition.

Abstract

This invention discloses a method and system for optimizing a detection device based on gamma geometric source decomposition, belonging to the field of neutron activation detection technology. The method establishes a geometric model of the neutron activation detection device to obtain the total response spectrum of all gamma rays in the detector crystal; it categorizes sources according to the geometric components that generate gamma rays, obtaining the response spectrum of each source separately; it verifies the decomposition results by the closure relationship between the total response spectrum and the sum of the source response spectra; it calculates the contribution ratio of each source at the target energy or target energy region, and adjusts the positions of the moderator, reflector, shield, detector, or sample region structure accordingly. This method can distinguish between sample signals and structural background sources, improving the ability to identify target spectral peaks and the efficiency of device optimization.

Description

Technical Field

[0001] This invention relates to the field of neutron activation detection technology, and more specifically, to an optimization method and system for a detection device based on gamma geometric source decomposition. Background Technology

[0002] Neutron activation detection is an analytical technique that utilizes the interaction of neutrons with atomic nuclei in a sample to release characteristic gamma rays. During detection, neutrons emitted from a neutron source enter the sample region and undergo capture, inelastic scattering, or other nuclear reactions with nuclides in the sample, exciting the nuclides. When these excited nuclides de-excite, they release gamma rays with specific energies. The detector then records the gamma energy spectrum to analyze the types or abundance of elements in the sample. For transient gamma neutron activation analysis, the intensity of the target characteristic peak, the background level, and the degree of spectral interference directly affect the accuracy of element identification.

[0003] Practical neutron activation detection devices typically include not only a neutron source and sample region, but also a moderator, reflector, shield, detector crystal, and detector housing. Neutrons have strong penetrating power; after entering the sample region, they interact not only with the sample but also with the moderator, shield, reflector, detector housing, and neutron source structural components, thereby generating gamma rays. Therefore, the spectral peak recorded by the detector at a target energy level does not necessarily originate entirely from the sample; it may also include background contributions from the device's structural materials. Existing optimization methods for neutron activation detection devices usually focus on macroscopic indicators such as total count rate, overall signal-to-noise ratio, moderator thickness, shield thickness, or detector distance. These methods can reflect the overall performance of the detection system, but they struggle to determine the specific source of a target energy peak and cannot distinguish between the effective sample signal and the structural background contribution. When the characteristic peaks of a target element overlap with the gamma responses generated by the moderator, shield, reflector, or detector materials, it is difficult to determine how structural parameters should be adjusted based solely on the total energy spectrum. This leads to device optimization relying on repeated experiments or empirical judgment, resulting in low optimization efficiency and insufficient reliability in peak identification.

[0004] Therefore, there is a need for an optimization method and system for neutron activation detection devices that can decompose the total gamma response in the detector according to its geometric source and provide the contribution ratio of each source at the target energy or target energy region. This is to identify the source of the sample signal and structural background and guide the directional optimization of the positions of the moderator, reflector, shield, detector, and sample region. Summary of the Invention

[0005] The present invention aims to solve the problems of existing neutron activation detection devices, such as difficulty in determining the source of the target energy peak, difficulty in distinguishing the effective signal of the sample from the structural background, and difficulty in performing targeted structural optimization based on the specific source of interference.

[0006] To address the aforementioned problems, this invention provides an optimization method for a detection device based on gamma geometric source decomposition, applied to a neutron activation detection device. The neutron activation detection device includes a neutron source, a moderator, a sample region, a reflector, a shield, a detector crystal, and a detector housing, comprising the following steps: S1: Establish the geometric model of the neutron activation detection device. The geometric model includes the geometric dimensions, spatial position, and material composition of the neutron source, moderator, sample region, reflector, shield, detector crystal, and detector housing. S2: Perform neutron-photon coupling transport calculations in the geometric model and statistically analyze the total response spectrum Total(E) generated by all gamma rays in the detector crystal; S3: According to the geometric location of gamma ray generation, the gamma generation region in the geometric model is divided into multiple source categories G_i, and the source category G_i includes at least the sample area source, the moderator source, the reflector source, the shielding source, and the detector-related component source; S4: Obtain the source response spectrum Origin_i(E) of the gamma rays generated by each source category G_i in the detector crystal. For source category G_i other than the detector crystal, record the state parameters of the gamma rays generated in the corresponding geometric component and leaving the boundary of the geometric component. Use the state parameters as source surface data for back-reading and transport in the complete geometric model, and statistically analyze the response spectrum generated by them in the detector crystal. For detector crystal sources, directly analyze the response spectrum formed by the gamma rays generated in the detector crystal. S5: Calculate the sum of the response spectra of each source, OriginSum(E)=ΣOrigin_i(E), and calculate the residual between the total response spectrum Total(E) and the sum of the source response spectra, OriginSum(E), Residual(E)=Total(E)-OriginSum(E); S6: When the sum of the total response spectrum Total(E) and the source response spectrum OriginSum(E) satisfies the preset closure condition, calculate the contribution value and contribution ratio of each source category G_i at the target energy or target energy region; S7: Adjust the geometric parameters of the neutron activation detection device according to the contribution ratio to reduce the contribution of non-target sources to the response of the target energy or target energy region.

[0007] The present invention provides an optimization method for a detection device based on gamma geometric source decomposition, which, compared with the prior art, has, but is not limited to, the following beneficial effects: To address the challenges of existing neutron activation detection devices in determining the source of target energy peaks, distinguishing between effective sample signals and structural background, and optimizing the structure based on specific interference sources, this optimization method utilizes a neutron activation detection device comprising a neutron source, a moderator, a sample region, a reflector, a shield, a detector crystal, and a detector housing. The neutron source is a 2.45 MeV neutron source; the moderator is made of polyethylene; the reflector is made of graphite; the shield includes a boron-containing polyethylene shield and a lead shield; the detector crystal is a LaBr3 crystal; and the detector housing is an aluminum alloy shell. The data processing module first establishes a complete geometric model based on the shape, size, spatial position, and material composition of each component, and then performs neutron-photon coupling transport calculations within this model. In this complete geometric model, neutrons emitted from the neutron source enter the sample region and interact with the materials in the sample region, moderator, reflector, shield, detector components, and the neutron source structure. The gamma rays generated by these interactions propagate within the device, with some entering the detector crystal and forming a response. The data processing module does not distinguish between gamma-ray sources, but statistically analyzes the responses formed by all gamma rays within the detector crystal to obtain the total response spectrum Total(E). Subsequently, the data processing module classifies the sources according to their geometric positions and obtains the source response spectrum Origin_i(E) for each source category. After obtaining all source response spectra, it calculates OriginSum(E) = ΣOrigin_i(E) and Residual(E) = Total(E) - OriginSum(E). When OriginSum(E) and Total(E) satisfy the closure condition, the contribution ratio of each source is further calculated at the target energy or target energy region. If the contribution ratio of the sample region source is higher than that of the non-sample source source, it indicates that the target peak mainly comes from the sample under test. If the contribution ratio of the moderator, shield, reflector, or detector-related components is high, the device geometric parameters are adjusted according to the corresponding source to reduce the contribution of non-target sources to the target energy response.

[0008] Furthermore, the target energy or target energy region includes the characteristic gamma energy of the nuclide in the sample to be tested, a preset energy region near the characteristic gamma energy of the nuclide in the sample to be tested, the background characteristic gamma energy of the structural material, or a preset interference energy region used to evaluate spectral peak interference.

[0009] Furthermore, the source category G_i includes sample area source, moderator source, reflector source, shielding source, and detector-related component source; wherein, the detector-related component source includes at least one of detector crystal source and detector housing source, and the shielding source includes at least one of boron-containing polyethylene shielding source and lead shielding source.

[0010] Furthermore, the state parameters include the position, direction of motion, energy, statistical weight, and source category identifier of the gamma ray when it leaves the boundary of the corresponding geometric component.

[0011] Furthermore, when the source surface data is read back and transported in the complete geometric model, the geometric structure, material composition, detector crystal position, energy binning method, and detector response aperture of the geometric model are kept consistent with those when the total response spectrum Total(E) is obtained.

[0012] Furthermore, the detection response aperture includes at least one of the photon track length flux response aperture within the detector crystal and the pulse height response aperture within the detector crystal.

[0013] Furthermore, the preset closure condition includes: |TO| / T≤ε; Where T is the integral value or response value of the total response spectrum Total(E) within the full spectrum, target energy region, or specified energy bin, O is the integral value or response value of the sum of source response spectra OriginSum(E) within the same energy range, and ε is the preset closure threshold.

[0014] Furthermore, within the target energy region ΔE, the contribution value C_i of the i-th source category G_i is the integral value of Origin_i(E) within the target energy region ΔE; the proportion of the i-th source category G_i in the total response is the ratio of C_i to the integral value of Total(E) within the target energy region ΔE; the proportion of the i-th source category G_i in the total source response is the ratio of C_i to the integral value of OriginSum(E) within the target energy region ΔE.

[0015] Furthermore, the geometric parameters include at least one of the following: moderator thickness, moderator material, reflector thickness, reflector size, shield thickness, shield material, distance between detector crystal and sample area, detector housing material, and sample area size.

[0016] This invention also provides an optimization system for a neutron activation detection device based on gamma geometric source decomposition, comprising a neutron activation detection device and a data processing module; the neutron activation detection device specifically includes a neutron source structure, a polyethylene moderator, a sample region to be tested, a graphite reflector, a boron-containing polyethylene shield, a lead shield, a detector crystal, and an aluminum detector housing; the data processing module is configured to execute the above-described neutron activation detection device optimization method. Attached Figure Description

[0017] Figure 1 This is a flowchart of the gamma geometric source decomposition process for an optimization method of a detection device based on gamma geometric source decomposition according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the detection device in an optimization system for neutron activation detection based on gamma geometric source decomposition, according to an embodiment of the present invention. Figure 3 This is a schematic diagram illustrating the closed relationship between OriginSum and Total in a detection device optimization method based on gamma geometric source decomposition according to an embodiment of the present invention.

[0018] Explanation of reference numerals in the attached figures: 1. Neutron source structural components; 2. Polyethylene moderator; 3. Sample area to be tested; 4. Graphite reflector; 51. Boron-containing polyethylene shield; 52. Lead shield; 6. Detector crystal components; 7. Aluminum detector housing. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application are described clearly and completely below with reference to the accompanying drawings. It should be understood that the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments described in this application without creative effort will fall within the scope of protection of this application.

[0020] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used in the specification of this application is for the purpose of describing specific embodiments only and is not intended to limit this application; the terms "comprising," "including," "having," "containing," "comprise," etc., in the specification, claims, and accompanying drawings of this application are open-ended terms, indicating that a method comprises one or more steps, or an apparatus comprises one or more elements, but do not exclude the inclusion of other steps or elements. The terms "first," "second," etc., in the specification, claims, or accompanying drawings of this application are used to distinguish different objects, not to describe a specific order or primary / secondary relationship. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0021] In the description of this application, it should be understood that the terms "upper", "lower", "left", "right", "front", "rear", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0022] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "attachment" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0023] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, in this application, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0024] See Figure 1 and Figure 3 This invention discloses an optimization method for a detection device based on gamma geometric source decomposition, applied to a neutron activation detection device. The neutron activation detection device includes a neutron source, a moderator, a sample region, a reflector, a shield, a detector crystal, and a detector housing. The method comprises the following steps: S1: Establish the geometric model of the neutron activation detection device. The geometric model includes the geometric dimensions, spatial position, and material composition of the neutron source, moderator, sample region, reflector, shield, detector crystal, and detector housing. S2: Perform neutron-photon coupling transport calculations in the geometric model and statistically analyze the total response spectrum Total(E) generated by all gamma rays in the detector crystal; S3: According to the geometric location of gamma ray generation, the gamma generation region in the geometric model is divided into multiple source categories G_i, and the source category G_i includes at least the sample area source, the moderator source, the reflector source, the shielding source, and the detector-related component source; S4: Obtain the source response spectrum Origin_i(E) of the gamma rays generated by each source category G_i in the detector crystal. For source category G_i other than the detector crystal, record the state parameters of the gamma rays generated in the corresponding geometric component and leaving the boundary of the geometric component. Use the state parameters as source surface data for back-reading and transport in the complete geometric model, and statistically analyze the response spectrum generated by them in the detector crystal. For detector crystal sources, directly analyze the response spectrum formed by the gamma rays generated in the detector crystal. S5: Calculate the sum of the response spectra of each source, OriginSum(E)=ΣOrigin_i(E), and calculate the residual between the total response spectrum Total(E) and the sum of the source response spectra, OriginSum(E), Residual(E)=Total(E)-OriginSum(E); S6: When the sum of the total response spectrum Total(E) and the source response spectrum OriginSum(E) satisfies the preset closure condition, calculate the contribution value and contribution ratio of each source category G_i at the target energy or target energy region; S7: Adjust the geometric parameters of the neutron activation detection device according to the contribution ratio to reduce the contribution of non-target sources to the response of the target energy or target energy region.

[0025] In this embodiment, addressing the challenges of existing neutron activation detection devices in determining the source of target energy peaks, distinguishing between effective sample signals and structural background, and optimizing the structure based on specific interference sources, the optimized neutron activation detection device includes a neutron source, a moderator, a sample region, a reflector, a shield, a detector crystal, and a detector housing. The neutron source is a 2.45 MeV neutron source, the moderator is made of polyethylene, the reflector is made of graphite, the shield includes a boron-containing polyethylene shield and a lead shield, the detector crystal is a LaBr3 crystal, and the detector housing is an aluminum alloy housing. The data processing module first establishes a complete geometric model based on the shape, size, spatial position, and material composition of each component, and performs neutron-photon coupling transport calculations within this geometric model. In the complete geometric model, neutrons emitted from the neutron source enter the sample region and interact with the materials in the sample region, moderator, reflector, shield, detector components, and neutron source structural components. The gamma rays generated by these interactions propagate within the device, with some entering the detector crystal and forming a response. The data processing module does not distinguish between gamma-ray sources, but statistically analyzes the responses formed by all gamma rays within the detector crystal to obtain the total response spectrum Total(E). Subsequently, the data processing module classifies the sources according to their geometric positions and obtains the source response spectrum Origin_i(E) for each source category. After obtaining all source response spectra, it calculates OriginSum(E) = ΣOrigin_i(E) and Residual(E) = Total(E) - OriginSum(E). When OriginSum(E) and Total(E) satisfy the closure condition, the contribution ratio of each source is further calculated at the target energy or target energy region. If the contribution ratio of the sample region source is higher than that of the non-sample source source, it indicates that the target peak mainly comes from the sample under test. If the contribution ratio of the moderator, shield, reflector, or detector-related components is high, the device geometric parameters are adjusted according to the corresponding source to reduce the contribution of non-target sources to the target energy response.

[0026] Optionally, the target energy or target energy region may include the characteristic gamma energy of the nuclide in the sample to be tested, a preset energy region near the characteristic gamma energy of the nuclide in the sample to be tested, the background characteristic gamma energy of the structural material, or a preset interference energy region for evaluating spectral peak interference.

[0027] In this embodiment, the target energy or target energy region is set according to the detection purpose. When the detection purpose is to identify the target element in the sample, the target energy is selected as the characteristic gamma energy of the corresponding nuclide. Since the detector has energy resolution, the target energy region is set with this characteristic gamma energy as the center during actual calculation. The width of the target energy region is determined according to the detector energy resolution, the width of the energy spectrum bins, and the full width at half maximum (FWHM) of the target peak. For example, when the target characteristic gamma energy is 2223 keV, the data processing module selects several energy bins covering this energy peak as the target energy region and calculates the contributions of each source within this target energy region.

[0028] When the detection objective is to evaluate structural background interference, the target energy is selected from the characteristic gamma energy of the background material corresponding to the moderator, shield, reflector, detector crystal, or detector housing material, or from a preset interference energy region that has a risk of overlapping with the characteristic peaks of the analyte. With this setting, the data processing module can evaluate both the effective signal proportion of the target sample nuclide and the degree of interference from the background generated by the structural material on the target spectral peak. If the proportion of the sample source in the target energy region is high, it indicates that the energy region is suitable for identifying the analyte; if the proportion of the structural material source is high, it indicates that there is structural background interference in the energy region, requiring further adjustment of the material, thickness, position, or shielding structure of the corresponding geometric components.

[0029] Optionally, the source category G_i includes sample area source, moderator source, reflector source, shielding source, and detector-related component source; wherein, the detector-related component source includes at least one of detector crystal source and detector housing source, and the shielding source includes at least one of boron-containing polyethylene shielding source and lead shielding source.

[0030] In this embodiment, the source categories are divided according to the geometric components that generate gamma rays, including at least sample region sources, moderator sources, reflector sources, shielding sources, and detector-related component sources. Sample region sources characterize the contribution of gamma rays generated by the nuclide in the sample under neutron influence to the detector crystal response; moderator sources characterize the contribution of the moderator material to gamma rays generated under neutron influence; reflector sources characterize the contribution of the reflector material to gamma rays; shielding sources characterize the contribution of the shielding material to gamma rays; and detector-related component sources characterize the contribution of the detector crystal and detector housing to gamma rays.

[0031] In one specific embodiment, the shielding source further includes a boron-containing polyethylene shielding source and a lead shielding source. The boron-containing polyethylene shielding is used to absorb or attenuate neutrons, and the gamma rays generated by its material under neutron influence are attributed to the boron-containing polyethylene shielding source; the lead shielding is used to attenuate gamma rays, and the gamma response generated or scattered by its material is attributed to the lead shielding source. The detector-related component sources further include a detector crystal source and a detector housing source, wherein the detector crystal source is used to characterize the gamma-related response generated within the crystal, and the detector housing source is used to characterize the gamma-ray contribution generated by the housing material.

[0032] By classifying sources as described above, the data processing module can cover the main gamma-generating regions in the neutron activation detection device, enabling the sum of the response spectra of each source, OriginSum(E), to be validated against the total response spectrum, Total(E). If a practical device has multiple shielding layers or multiple reflective structures, each layer can be set as an independent source category; if multiple small structural components are made of the same material and contribute little to the target energy region, they can also be merged into the same non-target structure source category.

[0033] Optionally, the state parameters include the position, direction of motion, energy, statistical weight, and source category identifier of the gamma ray when it leaves the boundary of the corresponding geometric component.

[0034] In this embodiment, source surface recording is performed for source categories other than detector crystals. Taking a lead shield as an example, during neutron-photon coupling transport calculations, the data processing module determines whether a gamma ray originates inside the lead shield based on the particle's origin location. When a gamma ray originates inside the lead shield and leaves its boundary, its position, direction of motion, energy, statistical weight, and source category identifier are recorded. The position is used to determine the gamma ray's exit point on the source surface, the direction of motion is used to determine its subsequent propagation direction, the energy is used to determine its energy bin, the statistical weight is used to maintain statistical consistency in transport calculations, and the source category identifier is used to maintain the gamma ray's source attribution during subsequent transport readbacks.

[0035] To avoid repeatedly recording the same gamma ray, it is preferable to record its state parameters when it first leaves the boundary of the corresponding geometric component. Gamma rays generated inside the component but absorbed before leaving it are not included in the source surface readback data because they do not enter the external geometric space and directly contribute to the detector crystal. In this way, the source surface data represents the set of gamma rays emitted outward from the geometric component that have the potential for further propagation. This source surface data is not the final detection result, but rather the input data for reconstructing the propagation process in the complete geometric model, thus enabling the independent contribution of this source category to the detector crystal.

[0036] Optionally, when the source surface data is read back and transported in the complete geometric model, the geometric structure, material composition, detector crystal position, energy binning method, and detector response aperture of the geometric model are kept consistent with those when the total response spectrum Total(E) is obtained.

[0037] In this embodiment, source surface data is readback and transported. Taking the lead shield as the source again, after the source surface recording is completed, the data processing module uses the recorded gamma ray position, direction of motion, energy, and statistical weight as a new source input complete geometric model, allowing it to continue propagating within the complete device, including the sample area, moderator, reflector, shield, detector housing, and detector crystal. During readback and transport, the geometric structure, material composition, detector crystal position, energy binning method, and detector response aperture of the complete geometric model remain consistent with those obtained when the total response spectrum (Total(E)) is acquired.

[0038] During readback transport, gamma rays from a specific source category continue to scatter, absorb, or deposit energy. The final response formed within the detector crystal is statistically represented as Origin_i(E) corresponding to that source category. If the gamma ray generates a secondary photon during propagation, and this secondary photon eventually enters the detector crystal to form a response, the response is assigned according to the source category of the original source surface data. This process maintains the correspondence between the source response spectrum and the source surface. Since readback transport preserves the complete geometric model, Origin_i(E) and Total(E) have the same propagation environment and detection statistical caliber, thus enabling closure verification of OriginSum(E) and Total(E) laterally.

[0039] Optionally, the detection response aperture includes at least one of the photon track length flux response aperture within the detector crystal and the pulse height response aperture within the detector crystal.

[0040] In this embodiment, the detection response aperture is selected according to the analysis objective. One detection response aperture is the photon track length flux response aperture within the detector crystal, which statistically analyzes the track length contribution of gamma rays within the detector crystal and forms a response spectrum according to energy bins. This aperture can reflect the flux contribution of gamma rays from different sources after entering the detector crystal, and is suitable for source decomposition closure verification and geometric optimization trend analysis.

[0041] Another detection response aperture is the pulse height response aperture within the detector crystal, which statistically analyzes the energy deposition of gamma rays within the detector crystal, forming a pulse height spectrum that more closely approximates the actual energy spectrum measurement. This aperture reflects the response characteristics of the detector's actual recorded energy spectrum and is suitable for target characteristic peak identification and spectral peak interference evaluation. Regardless of the response aperture used, Total(E), each Origin_i(E), and OriginSum(E) must maintain a consistent aperture. If Total(E) uses the pulse height response, then each Origin_i(E) uses the pulse height response; if Total(E) uses the photon track length flux response, then each Origin_i(E) uses the photon track length flux response. This consistency setting avoids distortion of closure error caused by different statistical apertures and ensures the comparability of the contribution ratios of each source.

[0042] Optional, please refer to Figure 3 The preset closure conditions include: |TO| / T≤ε; Where T is the integral value or response value of the total response spectrum Total(E) within the full spectrum, target energy region, or specified energy bin, O is the integral value or response value of the sum of source response spectra OriginSum(E) within the same energy range, and ε is the preset closure threshold.

[0043] In this embodiment, the reliability of the source decomposition result is determined by a closure condition. Specifically, after obtaining Total(E) and each Origin_i(E), the data processing module first calculates OriginSum(E) = ΣOrigin_i(E), and then calculates Residual(E) = Total(E) - OriginSum(E). The closure judgment can be performed over the entire spectrum integration range, or within the target energy region or a specified energy bin. When T represents the integral value or response value of Total(E) within the selected energy range, and O represents the integral value or response value of OriginSum(E) within the same energy range, if |TO| / T≤ε, then the source decomposition is determined to satisfy the closure condition.

[0044] ε is a preset closure threshold, the value of which is determined based on the statistical particle count, energy bin width, response aperture, and analytical precision requirements. For example, in full-spectrum integration closure verification, ε can be set to a smaller value to determine whether the source decomposition is generally reliable; in target energy region closure verification, ε can be set in conjunction with the target energy region count level. When the T value in the target energy region is low, leading to unstable relative error, the data processing module can also use the absolute residual for auxiliary judgment. The purpose of closure verification is to confirm that the sum of the response spectra of each source can reconstruct the total response spectrum, thereby proving the consistency between source decomposition, source surface recording, source surface readback, and probe response statistics. Only after the closure condition is met are the contribution proportions of each source used for subsequent structure optimization.

[0045] Optionally, within the target energy region ΔE, the contribution value C_i of the i-th source category G_i is the integral value of Origin_i(E) within the target energy region ΔE; the proportion of the i-th source category G_i in the total response is the ratio of C_i to the integral value of Total(E) within the target energy region ΔE; the proportion of the i-th source category G_i in the total source response is the ratio of C_i to the integral value of OriginSum(E) within the target energy region ΔE.

[0046] In this embodiment, the data processing module calculates the contribution value and contribution ratio of each source category within the target energy region. Let the target energy region be ΔE, and the contribution value C_i of the i-th source category G_i be the integral value of Origin_i(E) within the target energy region ΔE. The integral value of Total(E) within the target energy region ΔE is denoted as T_ΔE, and the integral value of OriginSum(E) within the target energy region ΔE is denoted as O_ΔE. The data processing module calculates the proportion of the i-th source category G_i in the total response, P_i = C_i / T_ΔE, and also calculates the proportion of the i-th source category G_i in the total source response, Q_i = C_i / O_ΔE.

[0047] Here, P_i is used as a benchmark based on the actual total response of the detector within the target energy region, characterizing the contribution of the i-th source category to the actual detection response in the target energy region. For example, when the P_i from the sample region is high, it indicates that the actual response recorded by the detector in the target energy region mainly comes from the sample to be measured, and this target peak is suitable for the identification of the analyte. When the P_i from the moderator, shield, reflector, or detector-related component sources is high, it indicates that there is strong structural background interference in the target energy region, and subsequent optimization should be carried out on the corresponding structural components in terms of material, thickness, or spatial location.

[0048] Q_i, based on the sum of the responses from the decomposed sources, characterizes the relative contribution of the i-th source category within the decomposed source set. Since the sum of Q_i for each source category is 1 or 100%, this ratio facilitates comparison of the magnitudes of different source categories. For example, if the Q_i of the moderator source is the largest among non-sample sources, it indicates that the moderator is the primary source of interference in the decomposed structural background, and adjustments can be prioritized to the moderator thickness, moderator material, or the distance between the moderator and the sample region. If the Q_i of the shielding source is the largest, then optimization should prioritize the shielding material combination, shielding thickness, or shielding opening structure.

[0049] When the closure condition is met and the proportion of Residual(E) within the target energy region is small, the values ​​of P_i and Q_i are close, indicating that the decomposed sources can fully explain the actual detection response within the target energy region. When the difference between P_i and Q_i is large, it indicates that the residual contribution within the target energy region cannot be ignored. The data processing module prioritizes checking whether the source category classification is complete, whether source surface records are missing, whether the source surface readback geometry is consistent with the complete model, and whether the detection response caliber is consistent. By simultaneously outputting P_i, Q_i, and Residual(E), the source of the target peak can be determined from two perspectives: "actual total response proportion" and "relative proportion of decomposed sources," providing a more complete quantitative basis for subsequent structure optimization.

[0050] Optionally, the geometric parameters include at least one of the following: moderator thickness, moderator material, reflector thickness, reflector size, shield thickness, shield material, distance between detector crystal and sample area, detector housing material, and sample area size.

[0051] In this embodiment, the data processing module adjusts the geometric parameters based on the contribution ratio within the target energy region. If the contribution ratio from the moderator source within the characteristic energy region of the target sample exceeds a preset interference threshold, the moderator thickness, moderator material, or the relative position between the moderator and the sample region is adjusted to reduce the gamma background generated by the moderator while maintaining the neutron spectrum modulation function. If the contribution ratio from the reflector source is high, the reflector thickness, reflector size, or reflector coverage area is adjusted to reduce interference in the target energy region while maintaining neutron utilization. If the contribution ratio from the shielding source is high, the shielding thickness, shielding material combination, lead shielding structure, or boron-containing polyethylene shielding structure is adjusted to reduce the contribution of the shielding material to the target peak.

[0052] If the source contribution ratio of the detector shell is high, the shell material with a lower background is replaced or the shell thickness is adjusted. If the source contribution ratio of the detector crystal is high, the spatial relationship between the detector crystal and the neutron source, sample region, or shield is adjusted to reduce the influence of the crystal's own background on the target energy region. If the source contribution ratio of the sample region is low, the distance between the detector crystal and the sample region, the size of the sample region, the relative position of the neutron source and the sample region, or the reflector layout are adjusted to increase the probability of characteristic gamma rays from the sample entering the detector crystal. After each geometric parameter adjustment, the data processing module re-executes the geometric model calculation, source response spectrum acquisition, closure verification, and contribution ratio calculation until the proportion of the target source increases and the contribution of non-target sources decreases.

[0053] See Figure 2 The present invention also provides an optimization system for a neutron activation detection device based on gamma geometric source decomposition, comprising a neutron activation detection device and a data processing module; the neutron activation detection device specifically includes a neutron source structure 1, a polyethylene moderator 2, a sample area to be tested 3, a graphite reflector 4, a boron-containing polyethylene shield 51, a lead shield 52, a detector crystal 6, and an aluminum detector housing 7; the data processing module is configured to execute the above-described neutron activation detection device optimization method.

[0054] In this embodiment, the system includes a neutron activation detection device and a data processing module. The neutron activation detection device includes a neutron source structure 1, a polyethylene moderator 2, a sample area to be tested 3, a graphite reflector 4, a boron-containing polyethylene shield 51, a lead shield 52, a detector crystal 6, and an aluminum detector housing 7. The neutron source structure 1 is used to generate neutrons to irradiate the sample area; the polyethylene moderator 2 is used to adjust the neutron energy; the sample area to be tested 3 is used to place the sample to be tested; the graphite reflector 4 is used to improve the neutron utilization rate near the sample area; the boron-containing polyethylene shield 51 and the lead shield 52 are used to reduce leakage radiation; the detector crystal 6 is used to receive gamma rays and form a response; and the aluminum detector housing 7 is used to support and protect the detector crystal.

[0055] The data processing module specifically includes model building, total response spectrum acquisition, source classification, source response spectrum acquisition, closure verification, contribution ratio calculation, and parameter optimization functions. The model building function is used to establish the geometric model of the neutron activation detection device; the total response spectrum acquisition function is used to calculate Total(E); the source classification function is used to classify G_i according to the gamma generation location; the source response spectrum acquisition function is used to obtain Origin_i(E); the closure verification function is used to calculate OriginSum(E) and Residual(E) and determine the closure condition; the contribution ratio calculation function is used to output P_i and Q_i within the target energy region; and the parameter optimization function is used to generate geometric parameter adjustment results based on the contribution ratio. This system integrates neutron activation detection device modeling, source decomposition, and structural optimization into a single system, suitable for detection device design, modification, and performance evaluation.

[0056] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. An optimization method for a detection device based on gamma geometric source decomposition, applied to a neutron activation detection device, wherein the neutron activation detection device comprises a neutron source, a moderator, a sample region, a reflector, a shield, a detector crystal, and a detector housing, characterized in that, Includes the following steps: S1: Establish the geometric model of the neutron activation detection device. The geometric model includes the geometric dimensions, spatial position, and material composition of the neutron source, moderator, sample region, reflector, shield, detector crystal, and detector housing. S2: Perform neutron-photon coupling transport calculations in the geometric model and statistically analyze the total response spectrum Total(E) generated by all gamma rays in the detector crystal; S3: According to the geometric location of gamma ray generation, the gamma generation region in the geometric model is divided into multiple source categories G_i, and the source category G_i includes at least the sample area source, the moderator source, the reflector source, the shielding source, and the detector-related component source; S4: Obtain the source response spectrum Origin_i(E) of the gamma rays generated by each source category G_i in the detector crystal. For source category G_i other than the detector crystal, record the state parameters of the gamma rays generated in the corresponding geometric component and leaving the boundary of the geometric component. Use the state parameters as source surface data for back-reading and transport in the complete geometric model, and statistically analyze the response spectrum generated by them in the detector crystal. For detector crystal sources, directly analyze the response spectrum formed by the gamma rays generated in the detector crystal. S5: Calculate the sum of the response spectra of each source, OriginSum(E)=ΣOrigin_i(E), and calculate the residual between the total response spectrum Total(E) and the sum of the source response spectra, OriginSum(E), Residual(E)=Total(E)-OriginSum(E); S6: When the sum of the total response spectrum Total(E) and the source response spectrum OriginSum(E) satisfies the preset closure condition, calculate the contribution value and contribution ratio of each source category G_i at the target energy or target energy region; S7: Adjust the geometric parameters of the neutron activation detection device according to the contribution ratio to reduce the contribution of non-target sources to the response of the target energy or target energy region.

2. The method for optimizing a detection device based on gamma geometric source decomposition according to claim 1, characterized in that, The target energy or target energy region includes the characteristic gamma energy of the nuclide in the sample to be tested, a preset energy region near the characteristic gamma energy of the nuclide in the sample to be tested, the background characteristic gamma energy of the structural material, or a preset interference energy region used to evaluate spectral peak interference.

3. The method for optimizing a detection device based on gamma geometric source decomposition according to claim 1, characterized in that, The source category G_i includes sample area source, moderator source, reflector source, shielding source, and detector-related component source; wherein, the detector-related component source includes at least one of detector crystal source and detector housing source, and the shielding source includes at least one of boron-containing polyethylene shielding source and lead shielding source.

4. The method for optimizing a detection device based on gamma geometric source decomposition according to claim 1, characterized in that, The state parameters include the position, direction of motion, energy, statistical weight, and source category identifier of the gamma ray when it leaves the boundary of the corresponding geometric component.

5. The method for optimizing a detection device based on gamma geometric source decomposition according to claim 1, characterized in that, When the source surface data is read back and transported in the complete geometric model, the geometric structure, material composition, detector crystal position, energy binning method, and detector response aperture of the geometric model are kept consistent with those when the total response spectrum Total(E) is obtained.

6. The method for optimizing a detection device based on gamma geometric source decomposition according to claim 5, characterized in that, The detection response aperture includes at least one of the photon track length flux response aperture within the detector crystal and the pulse height response aperture within the detector crystal.

7. The method for optimizing a detection device based on gamma geometric source decomposition according to claim 1, characterized in that, The preset closure conditions include: |TO| / T≤ε; Where T is the integral value or response value of the total response spectrum Total(E) within the full spectrum, target energy region, or specified energy bin, O is the integral value or response value of the sum of source response spectra OriginSum(E) within the same energy range, and ε is the preset closure threshold.

8. The method for optimizing a detection device based on gamma geometric source decomposition according to claim 7, characterized in that, Within the target energy range ΔE, the contribution value C_i of the i-th source category G_i is the integral value of Origin_i(E) within the target energy range ΔE; the proportion of the i-th source category G_i in the total response is the ratio of C_i to the integral value of Total(E) within the target energy range ΔE; the proportion of the i-th source category G_i in the total source response is the ratio of C_i to the integral value of OriginSum(E) within the target energy range ΔE.

9. The method for optimizing a detection device based on gamma geometric source decomposition according to claim 1, characterized in that, The geometric parameters include at least one of the following: modulator thickness, modulator material, reflector thickness, reflector size, shield thickness, shield material, distance between detector crystal and sample area, detector housing material, and sample area size.

10. An optimization system for a detection device based on gamma geometric source decomposition, characterized in that, Includes a neutron activation detection device and a data processing module; The neutron activation detection device specifically includes a neutron source structure (1), a polyethylene moderator (2), a sample area to be tested (3), a graphite reflector (4), a boron-containing polyethylene shield (51), a lead shield (52), a detector crystal (6), and an aluminum detector housing (7). The data processing module is configured to perform the neutron activation detection device optimization method according to any one of claims 1 to 9.

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