Optimal design method for electron accelerator bunching system for replacement injection scheme of next-generation electron-positron collider

Abstract

The present invention discloses an electron accelerator bunching system optimization design method for a new generation electron-positron collider replacement injection scheme, belonging to the field of electron beam injectors. The method adopts a genetic algorithm, takes the bunch length and energy divergence output by the bunching system as fitness functions, and optimizes the bunch length and energy divergence to converge to a minimum simultaneously, thereby obtaining preliminary design results of the bunching system layout and component parameters. Then, multi-particle simulation is further carried out using beam dynamics software to verify the preliminary design results obtained by the genetic algorithm, thereby continuously correcting the constraints in the multi-objective genetic algorithm and performing iterative calculations. After multiple iterative calculations, the optimal design results of the electron accelerator bunching system layout and components that meet the requirements of collision ring injection under the multi-particle model are obtained. The method provided by the present invention can quickly obtain the optimal design results of the electron accelerator bunching system layout and component parameters that meet the requirements of collision ring injection.

Description

Technical Field

[0001] The present invention belongs to the field of electron beam injectors, and more specifically, relates to an optimization design method for an electron accelerator bunching system for a replacement injection scheme of a new generation electron-positron collider. Background Art

[0002] In recent years, electron-positron colliders have provided technological support for understanding the nature of the strong interaction, exploring matter-antimatter asymmetry, and searching for new physics beyond the Standard Model. New-generation colliders place extremely high demands on injected beam performance. To meet the brightness requirements of electron-positron collisions, the injected electron beams must possess high charge, high current, short bunch length, and small bunch energy divergence. Therefore, new-generation colliders worldwide use a displacement injection scheme to focus and accelerate the low-energy DC beams generated by the electron gun until they meet the collider's ultra-high injection requirements. In this scheme, the focusing system, serving as the precursor to the displacement injection electron linear accelerator, consists of several subharmonic bunchers and a traveling-wave buncher. The subharmonic buncher's resonant frequency is 1 / nth of the main acceleration frequency (called the nth harmonic). The multi-section subharmonic buncher performs preliminary longitudinal focusing of the DC beam output by the electron gun, compressing the DC beam to within a quarter of the main acceleration frequency before entering the traveling-wave buncher. Conventional displacement injection bunching systems utilize a combination of two subharmonic bunchers, the first at 1 / 18 the main acceleration frequency, and the second at 1 / 6. The traveling-wave buncher provides initial acceleration until the energy requirements of the subsequent main acceleration phase are met, while the bunch length gradually decreases within the traveling-wave buncher. To ensure that the incoming bunches have high charge and current intensities, the bunching system requires a high capture rate.

[0003] For existing beam systems, optimizing beam performance is the preferred approach. While this can be done through intuitive evaluation through electromagnetic calculations and beam simulations, optimization can be limited by the inadequacy of observation and adjustment methods in existing testbeds. Furthermore, given the multiple parameters involved and their interactions, optimization often requires manual, repetitive calculations, which is inefficient. Summary of the Invention

[0004] In response to the above-mentioned defects or improvement needs of the prior art, the present invention provides an optimization design method for an electron accelerator bunching system for a new generation of electron-positron collider replacement injection scheme, which can ensure that the output bunch of the bunching system has sufficient energy and a high capture rate, while compressing the DC bunch length generated by the hot cathode electron gun as much as possible, reducing the energy divergence of the output bunch, and meeting the high-quality beam injection requirements of the new generation of electron-positron collider replacement injection scheme.

[0005] To achieve the above objectives, according to a first aspect of the present invention, a method for optimizing the design of an electron accelerator bunching system for a replacement injection scheme for a new generation electron-positron collider is provided, comprising:

[0006] S1, using a reference point-based non-dominated sorting genetic algorithm with an elitist strategy, taking the first performance of the beam output by the bunching system as the optimization target, and optimizing the setting range of each bunching system parameter corresponding to the beam with the best first performance;

[0007] The first performance includes bunch length and bunch energy divergence, the optimization goal is to minimize both bunch length and bunch energy divergence, the bunching system parameters include the phase of the first and second harmonic cavities, cavity pressure, and drift length; the fitness function of the non-dominated sorting genetic algorithm is a calculation model of bunch length and energy divergence, and during the optimization process, each population individual corresponds to a bunch distribution, and the gene length of a single population individual is determined by the possible value points of each bunching system parameter;

[0008] S2, using multi-particle beam dynamics simulation software to calculate a second performance of the beam corresponding to a combination of values ​​of all bunching system parameters and determine whether it meets the performance requirements; wherein the parameter values ​​in the combination are all within the current setting range; the second performance includes bunch energy and capture rate;

[0009] S3, if not, then increase the lower limit of the current setting range and lower the upper limit to update the current setting range, and use it as the initial setting range of each beamforming system parameter, and return to S1; if yes, then output the current setting range and use it as the optimal setting range of each beamforming system parameter.

[0010] According to a second aspect of the present invention, there is provided an electronic device comprising: a computer-readable storage medium and a processor;

[0011] The computer-readable storage medium is used to store executable instructions;

[0012] The processor is configured to read the executable instructions stored in the computer-readable storage medium and execute the method according to the first aspect.

[0013] According to a third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the method according to the first aspect.

[0014] According to a fourth aspect of the present invention, there is provided a computer program product comprising a computer program or instructions, which implement the method according to the first aspect when executed by a processor.

[0015] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0016] The method provided by the present invention uses the initial beam parameters generated by the electron gun and the injection requirement of the collision ring as constraints and optimization targets respectively, uses the calculation model of the bunch length energy divergence output by the bunching system as the fitness function, and optimizes the bunch length and energy divergence to converge to a minimum at the same time. The preliminary design results of the bunching system layout and component parameters are obtained to achieve preliminary optimization of the bunching system layout and component parameters. Then, multi-particle simulation is further carried out using beam dynamics software to verify the single-particle design results obtained by the genetic algorithm, thereby continuously correcting the constraints in the multi-objective genetic algorithm and performing the next iterative calculation. After multiple iterative calculations, the optimal design results of the electron accelerator bunching system layout and components that meet the collision ring injection requirement under the multi-particle model are obtained. The method provided by the present invention gets rid of the difficulty and complexity of manual calculation and can quickly obtain the optimal design results of the electron accelerator bunching system layout and component parameters that meet the collision ring injection requirement.

[0017] As a further preference, the method provided by the present invention uses numerical analysis software to solve the single particle motion equation, and analyzes to obtain the preliminary parameter setting range of the subharmonic buncher and the traveling wave buncher in the bunching system, which can further improve the design efficiency.

[0018] As a further preference, the method provided by the present invention uses electromagnetic field calculation software and beam dynamics software to carry out multi-particle simulation to verify the single-particle design results, fully considering the strong space charge effect of the high-current electron beam and the tail field effect of the accelerating tube, making the design results more reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic flow chart of a method for optimizing the design of an electron accelerator bunching system for a new generation electron-positron collider replacement injection scheme provided by an embodiment of the present invention;

[0020] Figure 2 A schematic diagram of the multi-objective genetic algorithm NSGA-III process flow for the layout and parameter optimization design of electron linear accelerator components for the replacement injection scheme of the next-generation electron-positron collider provided by an embodiment of the present invention;

[0021] Figure 3 Schematic diagram of the two-dimensional model of the bunching system of the high-current and large-charge electron beam injector;

[0022] Figure 4 A graph showing the non-dominated solution set of bunch length and energy divergence obtained by a multi-objective genetic algorithm according to an embodiment of the present invention;

[0023] Figure 5The electron beam phase motion trajectory diagram obtained by solving the multi-objective genetic algorithm provided in the embodiment of the present invention;

[0024] Figure 6 An electron beam energy gain graph obtained by solving a multi-objective genetic algorithm provided by an embodiment of the present invention;

[0025] Figure 7 Phase space diagrams of the electron beam at the exits of the electron gun and two subharmonic bunchers obtained by solving the multi-objective genetic algorithm provided in an embodiment of the present invention;

[0026] Figure 8 Phase space diagrams of the electron beam at the inlet and outlet of the traveling wave buncher obtained by solving the multi-objective genetic algorithm provided in an embodiment of the present invention;

[0027] Figure 9 The embodiment of the present invention provides an optimization parameter evolution diagram of an electron beam in a bunching system obtained by solving the multi-objective genetic algorithm using the three-dimensional multi-particle simulation software Parmela. DETAILED DESCRIPTION

[0028] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is 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 for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0029] Electron-positron colliders require an injection beam with extremely high energy, low energy divergence, and a short bunch length. Under the displacement injection scheme, the DC beam generated by the electron gun is longitudinally compressed into a short bunch by the bunching system, enabling the electron bunch to achieve maximum energy gain in the accelerating tube. At the same time, the energy divergence of the bunch directly affects the acceleration effect in the subsequent acceleration stage. Therefore, it is crucial to optimize the parameters of the bunching system to achieve a shorter bunch length and lower energy divergence for the beam entering the accelerating tube.

[0030] The parameter settings of the multi-section harmonic buncher include parameters such as the resonant cavity pressure, frequency, RF phase, and drift segment length. The design and optimization are difficult and generally require repeated manual calculations for optimization, and the results obtained are often not ideal.

[0031] Based on this, an embodiment of the present invention provides an optimization design method for an electron accelerator bunching system for a replacement injection scheme of a new generation electron-positron collider, comprising:

[0032] S1, using a reference point-based non-dominated sorting genetic algorithm with an elitist strategy, taking the first performance of the beam output by the bunching system as the optimization target, and optimizing the setting range of each bunching system parameter corresponding to the beam with the best first performance;

[0033] Among them, the first performance includes bunch length and bunch energy divergence, and the bunching system parameters include the phase, cavity pressure and drift length of the first and second harmonic cavities; the fitness function of the non-dominated sorting genetic algorithm is a calculation model of the bunch length and energy divergence output by the bunching system. During the optimization process, each population individual corresponds to a bunch distribution, and the gene length of a single population individual is determined by the possible value points of each bunching system parameter (i.e., the phase, cavity pressure and drift length of the first and second harmonic cavities).

[0034] The bunching system is a conventional displacement injection bunching system, including two subharmonic bunchers and a traveling wave buncher.

[0035] The theoretical motion model of the beam in the bunching system is:

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044] Equations (1) and (2) give the motion equations of a single electron in a subharmonic buncher, where e is the charge of the electron, m0c 2 is the rest energy of the electron, eV0 represents the energy obtained when the electron is emitted by the electron gun, V g represents the cavity pressure of the subharmonic buncher, represents the RF phase of the subharmonic buncher, L represents the length of the drift section, n represents the harmonic order of the subharmonic buncher, f represents the main acceleration frequency, and the frequency of the resonant cavity is f / n. The subscript i represents the electrons at different positions in the longitudinal direction, the total number of electrons output by the electron gun is N, and the total number of electrons output by the bunching system is M. The DC beam emitted by the electron gun occupies different phases in the longitudinal direction. After entering the subharmonic buncher, the speed v iModulation occurs, and the corresponding phase changes after the drift distance L.

[0045] Equation (3) gives the calculation method of the relative velocity β, relativistic factor γ and kinetic energy W of a single particle.

[0046] Equations (4) and (5) give the motion equations of a single particle in the traveling wave buncher, where z is the longitudinal position of the traveling wave accelerator tube, represents the particle phase, λ is the free space wavelength of the electromagnetic wave, β p is the cavity phase velocity. E is the electric field gradient of the traveling wave buncher.

[0047] Formula (6) uses η to represent the capture rate. A high capture rate in the bunching system is a requirement for the displacement injection scheme to inject a large charge bunch. Formulas (7) and (8) use represents the bunch length, σ δ represents the cluster energy divergence, which is the fitness function of the genetic algorithm.

[0048] A multi-objective genetic algorithm is used to optimize the beam-forming system parameters. The optimization goal is to minimize the beam length and energy divergence of the beam-forming system. The component parameters corresponding to the best beam quality are the algorithm-optimized beam-forming system design results.

[0049] During the multi-objective algorithm optimization process, the genetic operator operations for obtaining the offspring population include: performing a selection operation based on reference point sorting, performing a crossover operation using a single-point crossover method, and performing a mutation operation using a single-point mutation method.

[0050] A non-dominated sorting genetic algorithm based on a reference point and an elitist strategy was employed. The optimization objective was to achieve optimal convergence of both the energy divergence and bunch length output of the bunching system. The optimal beam quality was determined under different parameter settings. The cavity length, phase, and drift length of the two subharmonic bunchers were used as input parameters, and the optimal parameter range was determined based on the final optimization results. The algorithm considered the interactions between the optimization objectives, and while the solution might not be optimal for each individual objective, it was able to maintain the overall optimal solution.

[0051] The beamforming system setting parameters are obtained by solving the final optimization results. Since the final optimal solution is presented as a Pareto frontier, the input setting parameters are within a small range, and the specific values ​​are selected based on the actual project needs. Similarly, the specific values ​​of other beamforming system parameters such as the phase and electric field gradient of the traveling wave beamforming system, or the parameters required to be set in the theoretical motion model of the beam in the beamforming system, are selected based on the actual project needs or set based on experience. In other words, the initial setting range of each beamforming system parameter can be set based on experience. To further improve design efficiency, as a further preferred embodiment of the present invention, the initial setting range of each beamforming system parameter is obtained as follows:

[0052] A motion model of electrons in a bunching system is established in numerical analysis software, a set of to-be-selected values ​​of any bunching system parameter is constructed, the values ​​of other bunching system parameters are fixed, and each to-be-selected value of any bunching system parameter is substituted into the motion model to obtain the second performance of the corresponding beam, and the value interval of the to-be-selected value that meets the second performance requirement is used as the initial setting range of the corresponding any bunching system parameter; wherein, if the initial setting range of the other bunching system parameters is not determined, its value is set arbitrarily, otherwise the values ​​in its initial setting range are traversed according to a preset step size, and the values ​​are respectively used as its values.

[0053] Specifically, by establishing a motion model of electrons in a bunching system and solving it using numerical analysis software (such as Matlab), the second performance of the output beam under different initial beam parameters and bunching system component setting parameters can be quickly obtained. The value range of the bunching system component parameters where the second performance meets the performance requirements is used as its initial range, which greatly simplifies the design process of the injector.

[0054] Numerical analysis software is used to establish and solve the motion equations of electrons in the bunching system. The bunching parameters are directly given as numerical values ​​and can be easily adjusted and modified. The bunching effect can be solved intuitively, which greatly simplifies the design process.

[0055] For example, assuming that the initial setting ranges of the phase, cavity pressure, and drift length of the first and second harmonic cavities are unknown, if it is necessary to determine the initial setting range of the phase φ1 of the first harmonic cavity, first set the values ​​of other parameters to fixed values ​​based on experience, then set the set of values ​​to be selected for φ1 (which can be set based on experience or based on the actual settable range of the first harmonic cavity), and substitute each value in the set of values ​​to be selected for φ1 into the motion model to obtain the second performance of the corresponding beam, and use the value interval of the values ​​to be selected that meet the second performance requirements as the initial setting range of φ1; if it is necessary to determine The cavity pressure V2 of the second harmonic cavity is determined. At this time, only the initial setting range of φ1 is known, and the initial setting ranges of other parameters are unknown. Then, based on experience, the values ​​of other parameters except V2 are set to fixed values, wherein the value of φ1 traverses the initial setting range of φ1 according to a preset step size, and then a set of to-be-selected values ​​of V2 is set. Each value in the set of to-be-selected values ​​of V2 is substituted into the motion model to obtain the second performance of the corresponding beam, and the value interval of the to-be-selected values ​​that meet the second performance requirements is used as the initial setting range of V2; the initial setting ranges of other parameters are obtained in the same way.

[0056] S2, using multi-particle beam dynamics simulation software to calculate a second performance of the beam corresponding to a combination of values ​​of all bunching system parameters and determine whether it meets the performance requirements; wherein the parameter values ​​in the combination are all within the current setting range; the second performance includes bunch energy and capture rate;

[0057] S3, if not, then increase the lower limit of the current setting range and lower the upper limit to update the current setting range, and use it as the initial setting range of each beamforming system parameter, and return to S1; if yes, then output the current setting range and use it as the optimal setting range of each beamforming system parameter.

[0058] In steps S2-S3, Matlab is used to call three-dimensional multi-particle beam dynamics simulation software (such as Parmela) to verify the genetic algorithm optimization results.

[0059] Three-dimensional multi-particle beam dynamics simulations should take into account the tail field effect in the accelerator tube and the strong space charge effect under high current conditions. Although the simulation results may deviate from the single-particle calculation model, the parameter variation trend is consistent with the single-particle model optimization results. Therefore, preferably, in step S2, multi-particle beam dynamics simulation software is used to calculate the corresponding second performance of the beam for all combinations of the values ​​of the bunching system parameters, including:

[0060] A bunching system model is established using three-dimensional electromagnetic field simulation software, and the actual three-dimensional electromagnetic field distribution in the bunching system is calculated. Then, multi-particle beam dynamics simulation software is used to simulate the motion process of the beam under the actual three-dimensional electromagnetic field distribution, thereby calculating the secondary performance of the beam corresponding to the combination of values ​​of all bunching system parameters.

[0061] Specifically, a bunching system model was established using 3D electromagnetic field simulation software (such as Superfish), fully accounting for the tail field effect and calculating the actual 3D electromagnetic field distribution in the bunching system. Parmela, a multi-particle beam dynamics simulation software, was then used to solve the motion of the beam under the actual 3D electromagnetic field distribution calculated by Superfish, assuming strong space charge effects. This validated the algorithm-optimized bunching system design. To ensure the output bunch energy and capture rate were maintained, the subharmonic buncher parameter settings were iteratively adjusted. After multiple iterations, the optimal design for the bunching system parameters was achieved.

[0062] The subharmonic buncher places the input beam at different acceleration phases so that the particles at the head of the beam receive less energy than the particles at the tail, and are gradually caught up by the latter in the subsequent drift section of the resonant cavity, thereby achieving a longitudinal bunching effect. Therefore, the bunching effect of the subharmonic buncher mainly depends on the cavity pressure, phase and subsequent drift section length of the resonant cavity in the buncher. The bunching effect of the traveling wave buncher depends on the beam parameters output by the subharmonic buncher. The appropriate phase and electric field gradient of the traveling wave buncher can further accelerate the bunch in the traveling wave buncher. In this process, the bunch length is further compressed, and the energy divergence is gradually reduced. Based on this, preferably, step S3 also includes: adjusting the phase and electric field gradient of the traveling wave buncher.

[0063] That is, when verifying the design results of the bunching system optimized by the algorithm, the parameter setting range of the subharmonic buncher and the fixed parameters of the traveling-wave buncher are gradually and iteratively corrected on the premise of ensuring the output bunch energy and capture rate. After multiple iterative calculations, the optimal design results of the bunching system parameter settings can be obtained.

[0064] Considering that the single-particle model calculation ignores the tail field effect and space charge effect, the output energy result is too high. Therefore, in order to ensure that the energy in the multi-particle simulation meets the standard, the electric field gradient of the traveling wave buncher should be increased during the iteration (for example, gradually increasing from 10MV / m); usually, in order to ensure the output energy, the phase of the traveling wave buncher is set at the maximum acceleration phase of 90°. However, due to the space charge effect, the multi-particle simulation may cause the loss of particles in the tail of the bunch at 90°, that is, the capture rate is reduced. Usually, a slight advance injection is used to suppress this phenomenon, that is, the phase is gradually reduced from 90°. Based on this, preferably, before returning to S1, it also includes:

[0065] The phase and electric field gradient of the traveling wave buncher are respectively reduced and increased according to their respective adjustment steps, and the updated values ​​are used as the initial values ​​of the phase and electric field gradient.

[0066] In this way, the phase and electric field gradient of the traveling wave buncher are also optimized.

[0067] Preferably, the method provided by the present invention can also store the dynamic parameters (position, phase, and energy) of each particle during beam motion by solving the single-particle equation of motion. A computer visualization program developed using Matlab displays ① the changes in energy and phase along the beamline during each particle's motion, ② the longitudinal phase space of the beam, and ③ the changes in beam length and energy divergence along the beamline. This visualization interface allows designers to observe changing trends in beam motion and adjust simulation parameters in a timely manner.

[0068] The method provided by the present invention is further illustrated below with a specific example.

[0069] The NSGA-III algorithm uses a reference point-based selection mechanism instead, so the algorithm ensures the diversity of solutions. Its execution process is as follows: Figure 1 The optimization results of the genetic algorithm are related to the selection of independent variable parameter intervals. Based on the requirements of the collider's permutation injection scheme, the initial length of the electron gun output DC beam is set to 1296°. After preliminary estimation (i.e., establishing a motion model of electrons in the bunching system in numerical analysis software, substituting the candidate values ​​of each bunching system parameter into the motion model to obtain the corresponding beam's secondary performance, and determining the initial setting range of the corresponding bunching system parameters based on the candidate values ​​that meet the secondary performance requirements), the cavity pressure setting interval for the first subharmonic buncher is [60, 150] kV, the injection phase interval is [-90, -10]°, and the drift length interval is [0.4, 1.2] m. The cavity pressure setting interval for the second subharmonic buncher is [80, 150] kV, the injection phase interval is [-120, -40]°, and the drift length interval is [0.1, 0.8] m.

[0070] After initializing the population, determine whether a primary subpopulation has been generated. If not, then after calculating the fitness function (i.e., outputting the bunch length and energy divergence), performing a fast non-dominated sort, and executing the genetic operator, the population is generated. Regarding fast non-dominated sorting, first set the number of layers of the non-dominated sort to 1, placing all non-dominated individuals therein, then increase the number of layers by 1, placing all the remaining non-dominated individuals therein, and so on, until all individuals have layers. The generated subpopulation is then merged with the parent population, and after calculating the fitness function of the merged population, a fast non-dominated sort is performed to obtain the stratified result. The reference point distance of the individuals is then calculated, and individuals with smaller non-dominated numbers and smaller reference point distances are added to the new parent population until the initial population size is reached. This set of individuals is the generated new subpopulation. Regarding genetic operations, selection is performed based on a reference point. Crossover is performed using a single-point crossover method, with a crossover probability generally ranging from 0.5 to 1.0, but 0.8 is used in this example. Mutation is performed using a single-point mutation method, with a mutation probability generally ranging from 0 to 0.05, but 0.02 is used in this example. If the population has not reached the preset number of iterations, the process continues. If it has, the solution with the smallest non-dominated fitness value, known as the Pareto solution, is output. This ultimately results in the shortest cluster length and the smallest cluster energy divergence. Regarding the termination number, in this example, the calculation terminates at 50 generations.

[0071] The algorithm optimization results are presented as a Pareto front solution set, representing a range of optimized bunch parameters (including bunch energy divergence and length) and the corresponding bunching system component parameter settings. All cases in the solution set are solved using Matlab using the multi-particle beam dynamics simulation software Parmela. In this example, the criteria for the output bunch energy greater than 11 MeV and the capture efficiency greater than 99.5% (i.e., the secondary performance requirements) are set. If these conditions are not met, the algorithm adjusts the subharmonic buncher phase, cavity pressure, and drift length constraints (i.e., the initial setting ranges for these parameters) as well as the initial settings for the traveling-wave buncher phase and electric field gradient, and the next algorithm solution is executed. The parameter adjustment step size can be set manually within each iteration. In this example, the subharmonic buncher phase, cavity pressure, and drift length are adjusted in steps of 1°, 2 kV, and 0.1 m, respectively. The traveling-wave buncher phase and electric field gradient are adjusted in steps of 0.5° and 0.2 MV / m, respectively. The choice of step size needs to comprehensively consider the solution accuracy and solution time. The smaller the solution step size, the higher the solution accuracy will be, but it will take longer to solve.

[0072] Figure 3 This figure shows the structure of a bunching system consisting of two subharmonic bunchers and a traveling-wave buncher. The two subharmonic bunchers are designated SHB1 and SHB2, respectively, and the traveling-wave buncher is designated TW-buncher. The main acceleration frequency is 2.9982 MHz. The frequency of the first subharmonic buncher is 1 / 18 of the main acceleration frequency, and the frequency of the second subharmonic buncher is 1 / 6 of the main acceleration frequency.

[0073] The non-dominated solution set of energy divergence and bunch length is as follows Figure 4 As shown, the abscissa is the energy divergence and the ordinate is the bunch length. It can be seen that the energy divergence is less than 25% and the bunch length is less than 125° under the two subharmonic buncher parameters optimized by the method provided by the present invention.

[0074] Figure 5 Shows the selection Figure 4When one of the solutions is solved, the phase space distribution of the bunch at the exit of the electron gun, the first subharmonic buncher, and the second subharmonic buncher is plotted. The horizontal axis represents the phase of each particle relative to the central particle, and the vertical axis represents the energy deviation of each particle relative to the central particle, i.e., the energy divergence δ. The results show that using the optimized parameters of the two subharmonic bunchers, the bunch achieves good compression performance while maintaining an acceptable energy divergence. The bunching effect is primarily due to the first subharmonic buncher, while the second subharmonic buncher further compresses the bunch length and effectively reduces the energy divergence. The bunch length of the DC beam at the electron gun exit is 1296°, and at the exit of the second subharmonic buncher, it is 26.72°, achieving a compression ratio of 48.5. The energy divergence is only 7.43%, meeting the injection requirements of the traveling wave buncher.

[0075] Figure 6 、 Figure 7 Shown are the selected Figure 3 When one of the solutions is solved, the phase motion trajectory and energy gain of a single particle in the traveling wave buncher are observed using a beam motion visualization program. Figure 6 It can be seen that when the electron beam moves in front of the traveling wave buncher, the electron phase gradually oscillates and shrinks. After the electron energy gradually increases and the speed approaches the speed of light, the particle phase gradually stabilizes and the phase length no longer changes significantly.

[0076] Figure 8 The researchers show the phase space diagrams of the electron beam at the entrance and exit of the traveling wave buncher, observed using a beam motion visualization program. At the exit of the bunching system, the bunch length is further compressed to 5.1°. At the same time, there is no particle loss in the entire injector bunching system, and the capture rate of the system reaches 100%.

[0077] Figure 9 The results of calculations performed using the 3D multi-particle beam dynamics software Parmela, using the final bunching system layout and component parameter settings, are shown. The horizontal axis represents the kinetic energy of the bunch, the vertical axis represents the bunch length (solid line), and the dashed line represents the bunch energy divergence. The bunch length is primarily compressed in the first two subharmonic bunchers, where the energy range is relatively small. In the traveling-wave buncher, the energy is significantly increased, and the bunch length and energy divergence are further reduced.

[0078] In summary, the method provided by the present invention uses a multi-objective genetic algorithm with excellent convergence, and a single-particle simulation model of electron motion in a subharmonic buncher and a traveling wave buncher, and jointly calculates with a multi-particle beam dynamics simulation to solve the output of the electron accelerator bunching system using a reference point-based non-dominated sorting genetic algorithm with an elite strategy. The optimization goal is to converge the two parameters of the bunch length and bunch energy divergence output by the bunching system to the optimal direction. The initial setting range of the bunching system parameters is used as a constraint condition of the genetic algorithm to obtain the bunching system parameter setting results with the best beam quality. The results are further verified by beam simulation software, and the output results are repeatedly optimized iteratively to obtain the optimal parameter setting results.

[0079] An embodiment of the present invention provides an electronic device, comprising: a computer-readable storage medium and a processor;

[0080] The computer-readable storage medium is used to store executable instructions;

[0081] The processor is configured to read the executable instructions stored in the computer-readable storage medium and execute the method described in any one of the above embodiments.

[0082] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the method described in any of the above embodiments.

[0083] An embodiment of the present invention provides a computer program product, including a computer program or instructions, which implements the method described in any of the above embodiments when executed by a processor.

[0084] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing the design of an electron accelerator bunching system for a new generation electron-positron collider replacement injection scheme, characterized in that: include: S1, using a reference point-based non-dominated sorting genetic algorithm with an elitist strategy, taking the first performance of the beam output by the bunching system as the optimization target, and optimizing the setting range of each bunching system parameter corresponding to the beam with the best first performance; The first performance includes bunch length and bunch energy divergence, the optimization goal is to minimize both bunch length and bunch energy divergence, the bunching system parameters include the phase of the first and second harmonic cavities, cavity pressure, and drift length; the fitness function of the non-dominated sorting genetic algorithm is a calculation model of bunch length and energy divergence, and during the optimization process, each population individual corresponds to a bunch distribution, and the gene length of a single population individual is determined by the possible value points of each bunching system parameter; S2, using multi-particle beam dynamics simulation software to calculate a second performance of the beam corresponding to a combination of values ​​of all bunching system parameters and determine whether it meets the performance requirements; wherein the parameter values ​​in the combination are all within the current setting range; the second performance includes bunch energy and capture rate; S3, if not, then increase the lower limit of the current setting range and lower the upper limit to update the current setting range, and use it as the initial setting range of each beamforming system parameter, and return to S1; if yes, then output the current setting range and use it as the optimal setting range of each beamforming system parameter.

2. The method according to claim 1, wherein The initial setting range of each beamforming system parameter is obtained as follows: A motion model of electrons in a bunching system is established in numerical analysis software, a set of to-be-selected values ​​of any bunching system parameter is constructed, the values ​​of other bunching system parameters are fixed, and each to-be-selected value of any bunching system parameter is substituted into the motion model to obtain the second performance of the corresponding beam, and the value interval of the to-be-selected value that meets the second performance requirement is used as the initial setting range of the corresponding any bunching system parameter; wherein, if the initial setting range of the other bunching system parameters is not determined, its value is set arbitrarily, otherwise the values ​​in its initial setting range are traversed, and the values ​​are respectively used as its value.

3. The method according to claim 1, wherein Both the first and second harmonic bunchers have three adjustable parameters: phase, cavity pressure and drift length, with adjustment steps of 1°, 2kV and 0.1m respectively.

4. The method according to claim 1, wherein In step S2, multi-particle beam dynamics simulation software is used to calculate the second performance of the beam corresponding to the value combination of all bunching system parameters, including: A bunching system model is established using three-dimensional electromagnetic field simulation software, and the actual three-dimensional electromagnetic field distribution in the bunching system is calculated. Then, multi-particle beam dynamics simulation software is used to simulate the movement process of the beam in the actual three-dimensional electromagnetic field distribution, thereby calculating the second performance of the beam corresponding to the combination of values ​​of all bunching system parameters.

5. The method according to claim 1, wherein Before returning to S1, the process further includes: reducing and increasing the phase and electric field gradient of the traveling wave buncher according to respective adjustment steps, and using the updated values ​​as initial values ​​of the phase and electric field gradient.

6. The method according to claim 5, wherein The adjustment steps of the phase and electric field gradient of the traveling wave buncher are 0.5° and 0.2MV / m, respectively.

7. The method according to claim 1, wherein Also includes: The dynamic parameters of each particle during the beam motion are stored, and the changes in energy and phase along the beam line direction, the longitudinal phase space and beam length of each particle during the beam motion, and the changes in energy divergence along the beam line direction are displayed through a visualization program.

8. An electronic device, characterized in that: include: Computer-readable storage medium and processor; The computer-readable storage medium is used to store executable instructions; The processor is configured to read the executable instructions stored in the computer-readable storage medium and execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the method according to any one of claims 1 to 7.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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