A tree evaluation method for (n,f) cross section experimental data in energy region above 0.1 MeV

Abstract

This invention relates to a tree-structured evaluation method for experimental data of the (n,f) cross-section in the energy range above 0.1 MeV. The method includes the following steps: collecting experimental data and evaluation data for the (n,f) cross-section; evaluating the collected experimental data based on latent variables; determining the consistency of the evaluated experimental data for each type; and processing the evaluation results of the experimental data. This tree-structured evaluation method for experimental data of the (n,f) cross-section in the energy range above 0.1 MeV provides a detailed and scientific classification and evaluation method for this energy range, offers a weighting analysis method to clarify discrepancies in the (n,f) cross-section experimental data, provides a mathematical basis, standardizes the evaluation process, saves evaluation time, improves evaluation efficiency, and can be used as a reference for evaluating other cross-sections.

Description

Technical Field

[0001] This invention belongs to the field of nuclear data evaluation technology, specifically relating to a tree-structured evaluation method for experimental data in the (n,f) cross-section of the energy range above 0.1 MeV. Background Technology

[0002] The refined research and rapid development of nuclear energy have created an urgent demand for high-precision, diverse, and high-quality nuclear data. The quality and accuracy of nuclear data are crucial for achieving the expected results in nuclear engineering. For example, the accuracy and reliability of results in nuclear reactor design, testing, verification, and safety analysis are closely related to the quality of the nuclear data. In fast breeder reactor design, to improve critical mass accuracy, the breeder coefficient accuracy is required to reach even 0.3%, which necessitates an accuracy of 1–3% for the fission cross section (n,f) in the main energy region.

[0003] Currently, significant discrepancies exist between experimental data on fission cross-sections of major fission nuclei, between experimental and evaluation data, and among different evaluation data, causing difficulties for users in utilizing this cross-sectional data. Improving the accuracy of fission evaluation cross-sections and clarifying data discrepancies are of great significance. In textbook evaluation approaches that emphasize statistical analysis, it is assumed that the measured data are random events, their distribution follows a Gaussian distribution, and the true value is the value corresponding to the peak of the Gaussian distribution, i.e., the mathematical expectation. This is an ideal state of mathematical statistics, requiring two conditions to be met: ① the amount of data is sufficient to allow for statistical processing; ② the measurements are completely independent events with no correlation between them.

[0004] However, in practical applications, measurement data do not necessarily follow a Gaussian distribution due to factors such as the experimental year, method, equipment, samples, measurement environment, and data correction. Instead, data discrepancies often lead to the PPP problem during data fitting. Furthermore, there are often correlations between data points. Internationally, discussions on the accuracy and uncertainty of data evaluation, and the resolution of the PPP problem, have been hot topics in data evaluation. To make recommended data closer to the true value, a detailed evaluation of the uncertainty of each dataset is necessary, which significantly increases the workload of the evaluation process.

[0005] Another evaluation approach focuses more on physical evaluation, primarily relying on experimental measurement conditions and recommending only a few experimental data points with good accuracy and precision as the evaluation basis, while using most other experimental data as evaluation references. This evaluation method has been used in evaluating the reaction cross-sections of abundant experimental data for certain nuclides in the US ENDF / B-VIII.0 library, the Chinese CENDL-3.2 library, and the CENDL-3.1 library. However, this method lacks a detailed report due to a lack of mathematical basis. Both of these evaluation approaches require extensive knowledge and a clear understanding of experimental measurements to reasonably evaluate experimental data from different eras and laboratories for further data fitting.

[0006] Research has revealed that Simpson's paradox in statistics and the PPP problem faced in nuclear data evaluation are the same type of statistical problem. Applying the solution to Simpson's paradox to the evaluation of (n,α) and (n,x) cross-section data near 14 MeV yielded relatively reliable results. However, the measurement process for the (n,f) cross-section is more complex; the data types, experimental methods, neutron sources, and detectors are completely different from those for the (n,x) cross-section measured by the activation method. Therefore, a tree-structured evaluation method for experimental data of the (n,f) cross-section in the energy range above 0.1 MeV is needed. Summary of the Invention

[0007] To address the shortcomings of existing technologies, the purpose of this invention is to provide a tree-structured evaluation method for experimental data of the (n,f) cross section in the energy range above 0.1 MeV, thereby clarifying data discrepancies, improving the accuracy and reliability of evaluation data, achieving accurate evaluation of (n,f) cross section data, and providing a methodological reference for the evaluation of other cross sections.

[0008] To achieve the above objectives, the technical solution adopted by this invention is: a tree-structured evaluation method for experimental data of (n,f) cross sections in the energy range above 0.1 MeV, comprising the following steps: collecting experimental data and evaluation data of (n,f) cross sections; evaluating the collected experimental data based on latent variables; making consistency judgments on the evaluated experimental data of each type; and processing the evaluation results of the experimental data.

[0009] Furthermore, the evaluation of the collected experimental data includes: evaluation of experimental measurement type, evaluation of experimental method, evaluation of neutron source, evaluation of detector, and evaluation of sample preparation.

[0010] Furthermore, in the evaluation of experimental measurement types, the collected experimental data types include cross-sectional absolute measurement data, average measurement data AV, derived data DERIV, evaluation data EVAL, ratio data with other cross-sections RATIO, spectral average SPA, fission spectral average FIS, fast reactor neutron spectral average FST, Maxwell spectral average MXW, and relative measurement REL. Based on the characteristics of data uncertainty, data volume, data consistency, and energy range covered by the data, a weight analysis is performed on the collected experimental data types.

[0011] Furthermore, in the evaluation of the experimental method, neutron-induced fission reaction is used, and fission events are marked by measuring fission fragments. The equipment for measuring fission is a fission ionization chamber. Based on the characteristics of different fission ionization chambers, a weighted analysis is performed on the measurement data of each type of fission ionization chamber.

[0012] Furthermore, in the evaluation of neutron sources, the measurement of fission cross-section includes white light neutron sources and monoenergetic or quasi-energetic neutron sources. Based on the neutron mass produced by the neutron sources, the experimental data measured by different neutron sources are weighted and analyzed.

[0013] Furthermore, the detector evaluation includes light particle measurement and fission fragment measurement, and weighted analysis is performed on the experimental data measured by different detectors based on the detector's resolution.

[0014] Furthermore, in the sample preparation evaluation, greater weight is given to data with precise quantitative analysis of the sample.

[0015] Furthermore, the consistency judgment is to determine whether all the key recommended experimental data are distributed within one sigma after each of the latent variables has been evaluated. If so, the data is considered consistent; otherwise, further evaluation is performed.

[0016] Furthermore, the data processing includes data fitting, physical analysis, and data recommendation.

[0017] Furthermore, the data fitting employs the least squares method to fit all experimental data. The physical analysis includes analyzing the discrepancies between experimental data and evaluating the data. The data recommendation uses the data obtained by the least squares method as the recommended data center value.

[0018] The advantages of this invention are: it provides a detailed and scientific classification and evaluation method for the (n,f) cross-sectional data evaluation in the energy range above 0.1 MeV; it proposes a weighted analysis method to clarify the discrepancies in the (n,f) cross-sectional experimental data, finds a mathematical basis, standardizes the evaluation process, saves evaluation time, improves evaluation efficiency, and can be used as a reference for the evaluation of other cross-sections. Attached Figure Description

[0019] Figure 1This is a flowchart of a module for a tree-shaped evaluation method for energy ranges above 0.1 MeV (n, f).

[0020] Figure 2 Schematic diagram of the fission chamber principle;

[0021] Figure 3 This is a schematic diagram of the monochromaticity of various neutron sources;

[0022] Figure 4 This is a schematic diagram of the ionization chamber structure of a parallel plate avalanche detector;

[0023] Figure 5 This is a schematic diagram of the time projection room structure;

[0024] Figure 6 yes 238 A schematic diagram of experimental data and white light neutron source measurement data for the U(n,f) cross section;

[0025] Figure 7 yes 238 A schematic diagram of the data measured at the DT neutron source in the U(n,f) / section experimental data;

[0026] Figure 8 yes 238 A schematic diagram of U(n,f) / section experimental data measured near 14 MeV using the DT neutron source;

[0027] Figure 9 yes 238 A schematic diagram of experimental data on the U(n,f) / section, using the DT neutron source to measure neutron flux at around 14 MeV, clearly defined by COIN or ASSOP.

[0028] Figure 10 yes 238 A schematic diagram of U(n,f) / section experimental data using DD neutron source measurement data;

[0029] Figure 11 yes 238 A schematic diagram of U(n,f) / section experimental data using measurements taken near 2.5 MeV from the DD neutron source;

[0030] Figure 12 yes 238 U(n,f) / 235 A schematic diagram of experimental data for the U(n,f) cross section measured using a white-light neutron source;

[0031] Figure 13 yes 238 U(n,f) / 235A schematic diagram of measurement data from the U(n,f) cross-section white light neutron source using the time projection chamber and avalanche ionization chamber;

[0032] Figure 14 yes 238 U(n,f) / 235 A schematic diagram of experimental data measured using the DT neutron source in the U(n,f) cross section;

[0033] Figure 15 yes 238 U(n,f) / 235 A schematic diagram of the U(n,f) cross section using measurement data from the DT neutron source near 14 MeV;

[0034] Figure 16 yes 238 U(n,f) / 235 A schematic diagram of experimental data measured using the DD neutron source in the U(n,f) cross section;

[0035] Figure 17 yes 238 U(n,f) / 235 A schematic diagram of data measured at other neutron sources in the U(n,f) cross-section experimental data;

[0036] Figure 18 yes 238 A diagram comparing the recommended results of the experimental data of the U(n,f) section with the evaluation results of the international standard section;

[0037] Figure 19 yes 238 U(n,f) / 235 A diagram showing the comparison between the experimental data recommendation results of the U(n,f) cross-section ratio and the evaluation results of the international standard cross-section;

[0038] Figure 20 It is recommended 238 A schematic diagram comparing the cross section U(n,f) with experimental and evaluation data. Detailed Implementation

[0039] The present invention will now be further described with reference to the accompanying drawings and specific embodiments.

[0040] like Figure 1 As shown, the present invention provides a tree-structured evaluation method for experimental data in the energy range (n,f) above 0.1 MeV, comprising the following steps:

[0041] S1, Collect experimental and evaluation data for the (n,f) cross section;

[0042] Specifically, based on the EXFOR (Experimental Nuclear Reaction Data) database and in conjunction with relevant literature, we comprehensively collected and organized experimental data and data measurement information related to the (n,f) cross section. Simultaneously, we collected corresponding evaluation data from commonly used international evaluation databases, such as ENDF / B-VIII.0 from the United States, JENDL-5.0 from Japan, JEFF-3.3 and TENDL-2021 from Europe, and CENDL-3.2 from China, as well as evaluation data for the (n,f) cross section to be evaluated from specialized databases such as IRDFF-II and EAF-2010.

[0043] In this embodiment, experimental data were collected from the EXFOR library to obtain relevant information. 238 There are 110 types of U(n,f) cross sections, as shown in Table 1.

[0044]

[0045]

[0046]

[0047] Table 1

[0048] The evaluation data focuses on the latest evaluation results from the US ENDF / B-VIII.0, Japan JENDL-5.0, and Europe JEFF-3.3.

[0049] S2, evaluate the collected experimental data based on latent variables;

[0050] Specifically, the collected experimental data are classified, and based on measurement keywords such as data type, neutron source, and detector, a tree-structured evaluation method is used to analyze, physically evaluate, and weight the experimental data, and to provide the evaluation results.

[0051] It is understandable that the introduction of weighting factors is based on the solution to Simpson's paradox, but their specific values ​​are subject to certain human factors and are determined based on factors such as the impact of latent variables, the amount of data, and the degree of data discrepancy. The purpose is to eliminate data discrepancies caused by the influence of latent variables.

[0052] Furthermore, the latent variables in the experimental data include: evaluation of experimental measurement type, evaluation of experimental method, evaluation of neutron source, evaluation of detector, evaluation of sample preparation, and evaluation of other factors.

[0053] Among them, the experimental measurement type evaluation includes the collection of data such as cross-sectional absolute measurement data, average measurement data AV, derived data DERIV, evaluation data EVAL, ratio data with other cross-sections RATIO, spectral average SPA, fission spectral average FIS, fast reactor neutron spectral average FST, Maxwell spectral average MXW, relative measurement REL, etc. It is necessary to perform weight analysis on the collected experimental data types based on the characteristics of data volume, data consistency, and energy range covered by the data.

[0054] The weighting priority order is generally as follows: direct measurement of cross section, ratio measurement relative to standard cross section, average measurement, derived data, evaluation data, measurement relative to non-standard cross section, and others. This priority order is not absolute and can be adjusted according to the accuracy of the actual measurement.

[0055] For the fission cross section of certain nuclides, the ratio measurement may have results relative to more than one standard cross section, and it is necessary to select the order of the standard cross section. Generally speaking, the first-level standard takes precedence over the second-level standard, but specific situations need to be analyzed on a case-by-case basis.

[0056] In the evaluation of experimental methods, neutron-induced fission reactions are primarily characterized by the measurement of fission fragments to label fission events. The main equipment for measuring fission is the fission ionization chamber, such as... Figure 2 The diagram shows a fission ionization chamber measuring device.

[0057] In neutron source evaluation, the measurement of fission cross-section mainly utilizes two types of neutron sources: white-light neutron sources and monoenergetic or quasi-monoenergetic neutron sources. Neutron energy and the accuracy of counting are important indicators for evaluating neutron quality.

[0058] Commonly used white light neutron sources, also known as continuous spectrum neutron sources, include optical neutron sources (PHOTO) and spalled neutron sources (SPALL). Optical neutron sources (PHOTO) are used in conjunction with time-of-flight (TOF) measurements and are commonly used in the thermal neutron and resonant neutron energy regions. Spalled neutron sources (SPALL) can produce white light neutrons ranging from thermal neutrons to hundreds or even GeV.

[0059] Commonly used monoenergetic or quasi-monoenergetic neutron sources include DD, DT, P-LI7, PT, TD, and TH. Among these neutron sources, the DD neutron source near 2.5 MeV exhibits the best monochromaticity, while the DT neutron source near 14 MeV exhibits the best monochromaticity. Figure 3The monochromaticity of various neutron sources is presented, revealing significant differences in monochromaticity across different energy regions for common neutron sources. Besides the monochromaticity of the neutron source itself, the method used to determine neutron flux is also a crucial factor affecting data accuracy. Generally, the COIN method is considered the most accurate for determining neutron flux, followed by the ASSOP method (associated particle method). Experimental data using these two methods are given considerable weight.

[0060] Based on the energy range of the experimental data distribution and the mass of the neutron beam, the data can be weighted and recommended with emphasis on experimental data with higher accuracy.

[0061] In the detector evaluation: The detectors commonly used for fission cross-section measurement listed in the EXFOR information are very diverse, and can be broadly classified into two categories: light particle measurement and fission fragment measurement.

[0062] Detectors used for light particle detection include GE detectors, long neutron tubes, SI detectors, scintillation detectors, and solid-state detectors. This classification is not rigorous, but in terms of resolution, GE detectors have significant advantages in both energy and time resolution. Therefore, experimental data measured using GE detectors should be given greater weight in the evaluation. Detectors used to measure fission fragments are fission chambers. To address the problem of alpha particle accumulation in traditional fission chambers, two main approaches have been developed: one is the fast fission ionization chamber, with the best internationally available device being the parallel-plate avalanche ionization chamber (PPAC) detector developed by the European CERNnTOF collaboration; the other is a combination of two fragment coincidence measurements and track tracking, with the best internationally available device being the Time Projection Chamber (TPC) detector from LANL in the United States. Figure 4 and Figure 5 Schematic diagrams of these two devices are provided respectively. Experimental data using the better detector should be given priority consideration and recommendation in the evaluation.

[0063] Sample preparation is crucial in fission cross-section measurement. Thick samples will cause some fission fragments to be absorbed within the sample, preventing them from entering the detector and resulting in lost counts. Thin, homogeneous fission samples are not only difficult to prepare but also challenging to measure their thickness. Factors such as sample purity, sample properties, and sample weight all contribute to the uncertainty of the measurement data. Therefore, in the evaluation process, data with more precise quantitative analysis of the sample should be given greater weight.

[0064] It is understandable that the measurement of the fission cross section is quite complex, involving data processing, uncertainty analysis, and other factors that may not have been considered in the evaluation, such as data processing and uncertainty analysis.

[0065] In this embodiment, among the many data types, we mainly focus on the data types listed in Table 2. Other types, such as various spectral averages and ratio measurements relative to other cross sections, are only used as evaluation references due to their accuracy and reliability.

[0066]

[0067] Table 2

[0068] After evaluating the types of experimental data, it is necessary to list in detail the measurement information for each type of data, including the year, author, energy region, number of points, literature, laboratory, institution, experimental method, neutron source, detector, etc., as shown in Table 3.

[0069] Understandably, due to space limitations, not all information is provided in Table 3.

[0070]

[0071]

[0072]

[0073] Table 3

[0074] by 238 Taking the evaluation of experimental data from the U(n,f) cross section as an example, the neutron source is selected as the basis for classification, and the measurement results of the same neutron source are further evaluated using the neutron flux determination method.

[0075] Figure 6 Give 238 In the U(n,f) cross-section experimental data, there are a total of four subentries measured using white-light neutron sources. Of these four, F. Tovesson (2014) and O. Shcherbakov (2002) used measurements at the Spall neutron source, J. Blons (1975) used the PHOTO neutron source, and all three of these experimental data were obtained using white-light neutron sources. 235 U(n,f),,SIG represents the monitor's measurement data. Information on the neutron source from B. Leugers (1976) is unavailable and will not be considered. J. Blons (1975) used the SCIN detector, which is considered less accurate than later data and will not be considered. F. Tovesson (2014) and O. Shcherbakov (2002) disagree on measurements above 16 MeV, but based on experimental conditions, it is impossible to determine which measurement conditions are better. Therefore, for white-light neutron source measurements, the results from both F. Tovesson (2014) and O. Shcherbakov (2002) are recommended.

[0076] Figure 7 Give 238 In the experimental data of the U(n,f) cross section, the data measured at the DT neutron source show that the consistency is not very good, especially the neutron energy is dispersed, which requires further evaluation.

[0077] exist Figure 8 Give 238 The experimental data for the U(n,f) cross section were obtained using the DT neutron source, with measurements taken near 14 MeV. It can be seen that the consistency of the experimental data is still not very good; not only are the central values ​​significantly different, but the uncertainty is also relatively large, requiring further evaluation.

[0078] Figure 9 Give Give 238 The U(n,f) cross-section experimental data, measured near 14 MeV using the DT neutron source, with neutron flux definitively determined by COIN or ASSOP, shows better consistency and uncertainty than [previous data]. Figure 8 The results are as follows. Most of these data were measured after 1970. Except for the measurement by P. Bilaud in 1958, which had relatively poor uncertainty, the uncertainties of the other data are significantly better. This is because experimental measurements before 1970 used detection methods such as NAICR, SOLST, CSICR, and TRD, which had lower precision. Further analysis of the data uncertainty is needed.

[0079] The DD neutron source exhibits optimal monoenergeticity around 2.5 MeV, allowing for the most accurate determination of the neutron flux. Figure 10-11 Give 238 U(n,f) cross-section experimental data, DD neutron source measurement data and 238 The U(n,f) cross-section experimental data used measurements from the DD neutron source near 2.5 MeV. Three sources were used, and the data showed relatively good consistency and uncertainty. Among them, the measurements by IMKuks (1971) used ASSOP and are therefore preferred.

[0080] for 238 The experimental data for the U(n,f) cross section used measurement data from other neutron sources. After plotting and comparing, the consistency and uncertainty were not good, and therefore were not considered in this evaluation.

[0081] The classification is still based on neutron sources. For white light neutron source measurements, the detector is used as the secondary classification criterion, while for monoenergetic neutron source measurements, the neutron flux determination method is used as the basis for further evaluation.

[0082] Figure 12 Give 238 U(n,f) / 235In the experimental data of the U(n,f) cross section, the data measured by the white light neutron source show that the data are statistically consistent within the error range, but the center values ​​still have large differences.

[0083] Figure 13 Give 238 U(n,f) / 235 Data from U(n,f) cross-section white-light neutron sources, using measurements from a time-projection chamber (TPC) and an avalanche ionization chamber (PPAC+COINC), show poor consistency. Therefore, we prioritize the results from the time-projection chamber because the avalanche ionization chamber is more resistant to interference from low-energy alpha particles. 239 The Pu (n,f) cross section has a more obvious advantage.

[0084] Figure 14 Give 238 U(n,f) / 235 The experimental data for the U(n,f) cross section, measured using the DT neutron source, show poor consistency and relatively dispersed neutron energies, requiring further evaluation.

[0085] Figure 15 Give 238 U(n,f) / 235 The U(n,f) cross section uses measurements from the DT neutron source at around 14 MeV. The consistency is relatively good, but there are still some differences in the center values. Among these measurements, Li Jingwen (1989) used COIN and ASSOP to determine the neutron flux, and this work prioritizes this method.

[0086] Figure 16 Give Give 238 U(n,f) / 235 The experimental data for the U(n,f) cross section are from measurements taken at the DD neutron source. It can be seen that the data consistency is not very good, and the neutron energies are quite dispersed. Only one measurement was taken near 2.5 MeV: PH White (1967), which is given priority in this evaluation.

[0087] Figure 17 Give 238 U(n,f) / 235 The U(n,f) cross-section experimental data were measured at other neutron sources. The data showed poor consistency and requires further analysis; this was not considered in this work.

[0088] S3, perform consistency judgment on the experimental data evaluated for each type;

[0089] Specifically, for each type of evaluated experimental data, a consistency discussion is conducted. Ideally, the data should be consistent or substantially consistent, indicating that the evaluation is reasonable and the evaluation results mutually validate each other. If the data consistency is poor, further evaluation is needed to explain the reasons for the discrepancies. That is, after evaluating each latent variable in step S3, it is determined whether all the key recommended experimental data are distributed within one sigma. If so, the data are considered consistent or substantially consistent; otherwise, further evaluation is required.

[0090] Figure 18 Give 238 A comparison of the recommended results of the U(n,f) cross-section experimental data with the evaluation results of the international standard cross-section ENDF / B-VIII.0. White light neutron source measurement data are considered in the ratio measurement data; DT neutron source measurement data are mainly selected around 14 MeV, with measurements using COIN or ASSOP to determine the neutron flux; DD neutron source measurement data are mainly selected around 2.5 MeV, using the ASSOP method; other neutron source measurement data are not considered at this time.

[0091] Figure 19 Give 238 U(n,f) / 235 Comparison of the recommended experimental data for the U(n,f) cross-section ratio with the evaluation results of the international standard cross-section ENDF / B-VIII.0. For white-light neutron source measurement data, the experimental data obtained by RJ Casperson (2018) at TPC is recommended; for DT neutron source measurement data, the experimental measurements near 14 MeV using COIN and ASSOP are recommended (Li Jingwen (1989)); for DD neutron source measurement data, the monoenergetic point measurements near 2.5 MeV are recommended (PH White (1967); other neutron source measurement data are not considered at this time.

[0092] As can be seen, the back-to-back analysis and recommendations of various experimental data in this work are reasonable, have good consistency, and are also relatively consistent with international standard cross sections.

[0093] S4, Data processing of the evaluation results of experimental data;

[0094] Specifically, data processing includes data fitting, physical analysis, and data recommendation. Data fitting employs the least squares method to fit all experimental data, using spline fitting programs such as SPCC. Physical analysis involves analyzing discrepancies between experimental and evaluation data, and discussing the rationality of the fitted data. Data recommendation uses the data obtained through the least squares method to recommend and evaluate data center values. If the uncertainty given by the least squares method is considered too small, an evaluation uncertainty should be given based on the measurement uncertainty of the experimental data and the degree of data dispersion.

[0095] In this embodiment, based on the experimental data evaluated above, the least squares fitting program SPCC was used for data fitting. Figure 20 Recommendations for this work are provided. 238 A comparison of the U(n,f) cross-section with experimental and evaluation data shows that the cross-section recommended in this work clarifies the discrepancies between experimental data and is quite close to the evaluation cross-section of ENDF / B-VIII.0 in the United States.

[0096] As can be seen from the above embodiments, the present invention provides a detailed and scientific classification and evaluation method for the evaluation of cross-sectional data in the energy range above 0.1 MeV (n,f), proposes a weight analysis method to clarify the discrepancies in experimental data of the (n,f) cross-section, finds a mathematical basis, standardizes the evaluation process, saves evaluation time, improves evaluation efficiency, and can be used as a reference for the evaluation of other cross-sections.

[0097] The methods and systems described in this invention are not limited to the embodiments described in the specific implementation. Other implementation methods derived by those skilled in the art based on the technical solutions of this invention also fall within the scope of technical innovation of this invention.

Claims

1. A tree-structured evaluation method for experimental data in the energy range (n,f) above 0.1 MeV, characterized in that, include: Collect experimental and evaluation data for the (n,f) cross section; The collected experimental data are evaluated based on latent variables, including: evaluation of experimental measurement type, evaluation of experimental method, evaluation of neutron source, evaluation of detector, and evaluation of sample preparation. In the evaluation of experimental measurement types, the collected experimental data types include cross-sectional absolute measurement data, average measurement data AV, derived data DERIV, evaluation data EVAL, ratio data with other cross-sections RAT IO, spectral average SPA, fission spectral average FIS, fast reactor neutron spectral average FST, Maxwell spectral average MXW, and relative measurement REL. Based on the characteristics of data uncertainty, data volume, data consistency, and energy range covered by the data, a weight analysis is performed on the collected experimental data types. In the evaluation of the experimental method, neutron-induced fission reaction is used, and fission events are marked by the measurement of fission fragments. The equipment for measuring fission is a fission ionization chamber. According to the characteristics of different fission ionization chambers, a weighted analysis is performed on the measurement data of each type of fission ionization chamber. In the evaluation of neutron sources, the measurement of fission cross-section includes white light neutron sources and monoenergetic or quasi-energetic neutron sources. Based on the neutron mass produced by the neutron sources, the experimental data measured by different neutron sources are weighted and analyzed. The detector evaluation includes light particle measurement and fission fragment measurement. Based on the resolution of the detectors, a weighted analysis is performed on the experimental data measured by different detectors. In the sample preparation evaluation, factors such as sample purity, sample properties, and sample weighing are considered, and greater weight is given to the data with precise quantitative analysis of the sample. Consistency assessment was performed on the evaluated experimental data for each type; Data processing is performed on the evaluation results of the experimental data.

2. The tree-structured evaluation method for experimental data in the (n,f) cross-section of energy ranges above 0.1 MeV as described in claim 1, characterized in that: The consistency judgment is as follows: after evaluating each of the latent variables, it is determined whether all recommended experimental data are distributed within one sigma. If so, the data are considered consistent; otherwise, further evaluation is performed.

3. The tree-structured evaluation method for experimental data in the energy range (n,f) above 0.1 MeV as described in claim 1, characterized in that: The data processing includes data fitting, physical analysis, and data recommendation.

4. The tree-structured evaluation method for experimental data in the energy range (n,f) above 0.1 MeV as described in claim 3, characterized in that: The data fitting uses the least squares method to fit all experimental data. The physical analysis includes analyzing the discrepancies between experimental data and evaluating the data. The data recommendation uses the data obtained by the least squares method as the recommended data center value.

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