CN-122017928-A - Method and related device for measuring transverse phase of storage ring beam

CN122017928ACN 122017928 ACN122017928 ACN 122017928ACN-122017928-A

Abstract

The embodiment of the application discloses a method and a related device for measuring transverse phase of a storage ring beam, wherein the method comprises the steps of obtaining a beam track value and a quadrupole magnet K value provided by an accelerator control system and collected in real time by a beam position monitor (Beam Position Monitor, BPM), wherein the beam track value comprises a horizontal beam track value and a vertical beam track value, calculating gradients of the beam track value through a numerical gradient method to obtain a physical track angle value of the beam track, wherein the physical track angle value comprises the horizontal beam track angle value and the vertical beam track angle value, constructing an input vector according to the horizontal beam track value, the horizontal beam track angle value, the vertical beam track angle value and the K value, and inputting the input vector into a beam phase prediction model to obtain the transverse phase value at each beam position monitor. By adopting the embodiment of the application, rapid and noninvasive transverse phase measurement can be realized, and the method has good practicability and robustness.

Inventors

XUAN KE
YANG SIYU
XU WEI
LI FANG
LIU GONGFA
LI CHUAN

Assignees

中国科学技术大学

Dates

Publication Date: 20260512
Application Date: 20260104

Claims (10)

1. A method for measuring transverse phase of a storage ring beam, the method comprising: Acquiring a beam track value acquired in real time by a beam position monitor and a quadrupole magnet K value provided by an accelerator control system, wherein the beam track value comprises a horizontal beam track value and a vertical beam track value; Calculating the gradient of the beam track value by a numerical gradient method to obtain a physical track angle value of the beam track, wherein the physical track angle value comprises a horizontal beam track angle value and a vertical beam track angle value; constructing an input vector according to the horizontal beam track value, the horizontal beam track angle value, the vertical beam track angle value and the K value; The input vector is input into a beam phase prediction model to obtain a transverse phase value at each beam position monitor, the transverse phase value comprises a horizontal transverse phase value and a vertical transverse phase value, the beam phase prediction model comprises a dynamic optical transmission attention module, a standard time sequence feature path module, a feature fusion and pooling module and a predictor module, the dynamic optical transmission attention module is used for explicitly simulating a physical transmission process of a beam in a phase space and outputting physical context features according to the input vector, the standard time sequence feature path module is used for capturing an implicit and non-physical visual mode in data and outputting time sequence dependent features according to the input vector, the feature fusion and pooling module is used for constructing a context vector according to the physical context features and the time sequence dependent features, and the predictor module is used for predicting the transverse phase value at the beam position monitor according to the context vector.
2. The method of claim 1, wherein said outputting physical context features from said input vector comprises: Determining a value vector from the horizontal beam trajectory value, the horizontal beam trajectory angle value, the vertical beam trajectory value, and the vertical beam trajectory angle value in the input vector, and determining a query vector and a key vector from the horizontal beam trajectory value, the horizontal beam trajectory angle value, the vertical beam trajectory angle value, and the K value in the input vector, the value vector being used to represent phase space information; Determining a dynamic transfer matrix according to the query vector and the key vector, wherein the dynamic transfer matrix is used for representing a phase space transfer relationship between any two beam position monitors; calculating a contribution vector transmitted from any one beam position monitor position to the other beam position monitor position according to the dynamic transfer matrix and the value vector; And summing the contribution vectors transmitted from all the beam position monitor positions in the accelerator Lattice model to obtain a contribution sum vector transmitted from all the beam position monitor positions to the target beam position monitor position, wherein the contribution sum vector is the physical context feature.
3. The method of claim 1, wherein said outputting a timing dependent feature from said input vector comprises: Mapping the input vector of a low dimension into the input vector of a high dimension through a full connection layer; performing periodic position coding on the input vector with high dimension to obtain the input vector with high dimension comprising the position coding; and capturing non-physical and implicit modes in the high-dimensional input vector comprising the position codes through a one-dimensional convolutional neural network to obtain time sequence dependency characteristics.
4. The method of claim 1, wherein the constructing a context vector from the physical context feature and the timing dependent feature comprises: Splicing the physical context features and the time sequence dependent features along the channel dimension to obtain fusion features; enhancing the dependency capacity among different positions in the fusion characteristic through a multi-head attention network to obtain an enhanced characteristic; Regularizing the enhancement features through a discarding layer to obtain regularized features; and compressing the regularized features through a global average pooling layer to obtain a context vector.
5. The method of claim 1, wherein prior to said inputting the input vector into a beam phase prediction model, the method further comprises: constructing a first training data set which only allows errors in the K value of the quadrupole magnet, a second training data set which only allows errors in the correction magnet and a test data set which simultaneously allows errors in the K value of the quadrupole magnet and errors in the correction magnet, wherein each of the first training data set, the second training data set and the test data set comprises a plurality of data pairs, and each data pair consists of the K value of the applied quadrupole magnet, a calculated track error value and a transverse phase value; Training the beam phase prediction model according to the first training data set and the second training data set; And testing the beam phase prediction model after training according to the test data set to obtain the beam phase prediction model after the test is passed.
6. The method of claim 5, wherein constructing a first training data set that only allows errors in K-values of the quadrupole magnets comprises: Determining the sampling dimension of the Latin hypercube sampler according to the number of the quadrupole magnets; Sampling the quadrupole magnets through Latin hypercube samplers of the sampling dimension to generate K value error samples obeying uniform distribution; converting the K value error samples subjected to uniform distribution into K value error samples subjected to preset normal distribution by using an inverse cumulative distribution function; constructing an accelerator Lattice model, and adding the K value error sample obeying the preset normal distribution into the accelerator Lattice model; And calculating the track error value and the transverse phase value of the accelerator Lattice model.
7. The method of claim 6, wherein the beam phase prediction model further comprises a K-value inverter for performing a physical inversion based on the context vector to obtain a predicted K-value of the quadrupole magnet.
8. The method of claim 7, wherein the total cost function of the beam phase prediction model includes an empirical risk loss term, an invariant risk penalty term, an information bottleneck penalty term, and a K-value inversion penalty term, the empirical risk loss term being determined from the predicted lateral phase value and the calculated lateral phase value, the invariant risk penalty term being determined from a gradient norm of the penalty function with respect to a virtual scalar product, the information bottleneck penalty term being determined from a variance of the context vector, the K-value inversion penalty term being determined from a K-value of an applied quadrupole magnet and a predicted K-value of the quadrupole magnet.
9. An electronic device is characterized by comprising a processor and a memory; the processor is connected to a memory, wherein the memory is adapted to store a computer program, the processor being adapted to invoke the computer program to perform the method according to any of claims 1-8.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program comprising program instructions which, when executed by a processor, perform the method of any of claims 1-8.

Description

Method and related device for measuring transverse phase of storage ring beam Technical Field The invention relates to the technical field of accelerator beam parameter measurement, in particular to a storage ring beam transverse phase measurement method and a related device. Background The storage ring beam transverse phase is a key parameter describing the state of motion of the electron beam mass in the storage ring in the transverse (horizontal or vertical) direction. The method has the key role in machine operation, firstly, the method is a key tool for diagnosing and correcting linear Lattice design deviation and magnet error in terms of linear dynamics, and high-precision beam line correction can be realized through accurate measurement. Secondly, since the nonlinear dynamics is determined by the linear Lattice function and the hexapole magnet together, the precise distribution of the transverse phase directly constrains the setting and control of the nonlinear effect. Therefore, in order to achieve accurate beam line correction, to effectively optimize beam size and emittance, and to lay a solid foundation for subsequent nonlinear dynamics analysis and tuning, it is critical to measure the lateral phase quickly and accurately. For the measurement of the transverse phase, two main types of methods are employed, namely a response matrix method and a Turn-by-Turn (TBT) method. The response matrix method has higher measurement accuracy, but the measurement process has interference on the operation of the accelerator, and the time consumption is long and the overfitting problem is easy to be introduced. In contrast, TBT methods can achieve non-invasive measurements with short measurement times, but typically require external excitation to the beam, with excitation amplitudes that are difficult to control accurately, and lateral coupling effects that can interfere with the measurement results. In addition, the method is highly sensitive to the resolution of the beam position monitor, and the data analysis process is complex. Disclosure of Invention The embodiment of the application provides a storage ring beam transverse phase measuring method and a related device, which utilize a beam track acquired by a beam position monitor to measure the storage ring transverse phase, can realize quick and noninvasive measurement of the storage ring beam transverse phase, and have good practicability and robustness. An embodiment of the present application provides a method for measuring a transverse phase of a storage ring beam, where the method includes: Acquiring a beam track value acquired in real time by a beam position monitor and a quadrupole magnet K value provided by an accelerator control system, wherein the beam track value comprises a horizontal beam track value and a vertical beam track value; Calculating the gradient of the beam track value by a numerical gradient method to obtain a physical track angle value of the beam track, wherein the physical track angle value comprises a horizontal beam track angle value and a vertical beam track angle value; constructing an input vector according to the horizontal beam track value, the horizontal beam track angle value, the vertical beam track angle value and the K value; And inputting the input vector into a beam phase prediction model to obtain a transverse phase value at each beam position monitor, wherein the transverse phase value comprises a horizontal transverse phase value and a vertical transverse phase value. The beam phase prediction model comprises a dynamic optical transmission attention module, a standard time sequence feature path module, a feature fusion and pooling module and a predictor module, wherein the dynamic optical transmission attention module is used for explicitly simulating a physical transmission process of a beam in a phase space and outputting physical context features according to the input vectors, the standard time sequence feature path module is used for capturing implicit and non-physical visual modes in data and outputting time sequence dependent features according to the input vectors, the feature fusion and pooling module is used for constructing context vectors according to the physical context features and the time sequence dependent features, and the predictor module is used for predicting transverse phase values at the beam position monitor according to the context vectors. Optionally, the outputting the physical context feature according to the input vector includes: Determining a value vector from the horizontal beam trajectory value, the horizontal beam trajectory angle value, the vertical beam trajectory value, and the vertical beam trajectory angle value in the input vector, and determining a query vector and a key vector from the horizontal beam trajectory value, the horizontal beam trajectory angle value, the vertical beam trajectory angle value, and the K value in the input vector, the value vector being used to