Single-case multi-track data processing method based on time projection room

Abstract

The invention relates to a single-case multi-track data processing method based on a time projection room, and belongs to the technical field of particle track analysis, and the method comprises the following steps: S1, carrying out the denoising preprocessing of an original point; s2, reconstructing and correcting all the tracks in the case by using three-dimensional iterative Hough transform and a weighted least square method, and outputting a linear equation of each track; s3, for a certain straight line, calculating the distances from all points in the case to the straight line, setting a distance criterion threshold value, and regarding the points smaller than the distance criterion threshold value as the points located on the straight line so as to realize track splitting; and S4, carrying out over-fitting processing on the split tracks. According to the method, the processing effect of particle track detectors such as a time projection chamber on single-case multi-track is improved, and a practical solution is provided for high-energy particle multi-track detection and identification.

Description

Technical Field

[0001] The present invention belongs to the technical field of particle track analysis and relates to a data processing method for single-event multiple tracks based on a time projection chamber. Background Art

[0002] Currently, in high-energy physics experiments, commonly used particle track detectors, such as time projection chambers, can detect electrons ionized by charged particles. Pixelated readout detectors (such as microgrid planar gas detectors and gas electron multipliers) locate the coordinates of the ionized electron clusters, thereby reconstructing and identifying the particle's track. Current algorithms for three-dimensional particle track reconstruction (such as least squares methods, Hough transforms, and random sampling consensus algorithms) are effective for reconstructing and identifying particle tracks from a single source. However, they struggle to reconstruct and identify multiple particle tracks across a wide energy range and in multi-particle reactions. For example, white light neutrons, across a wide energy range, can interact with targets (such as uranium isotope targets) in a variety of ways, including neutron-induced fission reactions producing fission fragments, decay of heavy fission products producing alpha particles, and elastic scattering from the target substrate producing recoil protons. This means that multiple particles, or tracks, may be superimposed within a single detection event. Currently, no specific method exists in China to reconstruct and identify multiple tracks within a single event. This method, based on the 3D Hough transform, handles the case of multiple tracks in a single event by combining the distance from the track midpoint to the reconstructed line with the spacing between adjacent points. Existing case results demonstrate the effectiveness of this technique and provide an important reference for existing particle track processing methods. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to propose an algorithm based on three-dimensional iterative Hough transform that can accurately identify and split multiple tracks in a single event, in order to address the situation where particle track detectors such as time projection chambers are unable to effectively reconstruct and identify various particle tracks when processing multiple tracks in a single event.

[0004] In order to achieve the above object, the present invention provides the following technical solutions:

[0005] A data processing method for single-event multi-path based on a time projection room comprises the following steps:

[0006] S1: denoising preprocessing of original points;

[0007] S2: Use three-dimensional iterative Hough transform and weighted least squares method to reconstruct and correct all the tracks in the case, and output the straight line equation of each track;

[0008] S3: For a certain straight line, calculate the distance from all points in the case to the straight line, set a distance judgment threshold, and consider the points smaller than the distance judgment threshold as points on the straight line, thereby realizing track splitting;

[0009] S4: Perform overfitting processing on the split track: calculate the distance between all adjacent points on the split track, set the point distance judgment threshold, if there are more than one point distance on a track that is greater than the point distance judgment threshold, then discard the track; if there is only one point distance greater than the point distance judgment threshold, then split the track into two sections at the position with wider point distance, discard the section with fewer points, and the other section is the corrected reconstructed track.

[0010] Furthermore, the denoising preprocessing of the original points described in step S1 includes: taking the maximum amplitude value in the case as a standard, dividing the amplitudes of all points by the maximum value to obtain an amplitude ratio, and removing points with amplitude ratios less than a preset threshold.

[0011] Furthermore, step S2 specifically includes the following steps:

[0012] The straight line to be reconstructed is represented by a vector:

[0013]

[0014] in is any point on the line; is the direction vector of the straight line, and the yaw angle is and the pitch angle θ represents;

[0015] Use the optimal straight line representation method to eliminate redundant parameters;

[0016] The parameter space is discretized using the Platonic solid tessellation method, and all lines in a single case are reconstructed using the 3D Hough transform.

[0017] The signal amplitudes of all points in the straight line are taken as weights, and then the weighted least squares method is used to correct the straight line after Hough transformation.

[0018] Furthermore, the optimal straight line representation method includes:

[0019] First define a plane passing through the origin and perpendicular to the line;

[0020] Then define two parameters x' and y' as the coordinates of the intersection of the line and the plane in the two-dimensional rectangular coordinate system of the plane itself. For any point on the line The expressions for x' and y' are calculated as:

[0021]

[0022] Any point That is, the straight line expression is expressed as:

[0023]

[0024] Furthermore, the method of discretizing the parameter space using the Platonic solid mosaic method and reconstructing all straight lines in a single case using the three-dimensional Hough transform specifically includes:

[0025] The Platonic solid with the most vertices is the icosahedron, which has 12 vertices. The coordinates of the vertices are described by the golden ratio r:

[0026]

[0027] For a point in the input point cloud Belong to the straight line The criterion is: the vertical distance from the point to the line is less than the Hough space, that is, the grid width dx of the plane where the intersection point (x′, y′) is located:

[0028]

[0029] The value of dx is calculated based on the boundary size of the input point cloud or set manually.

[0030] Furthermore, the signal amplitudes of all points in the straight line are taken as weights, and then the weighted least squares method is used to correct the straight line after the Hough transform, specifically including:

[0031] The signal amplitude of each point is used as the weight, and the weighted least squares method is used to correct the straight line after the Hough transform. The core idea of the least squares method is to solve a multivariate equation system, which is expressed as At = B in the matrix. t is the parameter matrix of the straight line to be calculated, then t is:

[0032] t=(A T A) -1 A T B

[0033] Introduce the amplitude weight diagonal matrix:

[0034]

[0035] Then the expression of t becomes:

[0036] t=[A T W T WA] -1 A T W T WB

[0037] After weight correction is performed on the results of the Hough transform, the straight line equation of each track is output.

[0038] Furthermore, step S3 specifically includes the following steps:

[0039] For a line L:

[0040]

[0041] Compute the distance from all points in this case to the line:

[0042]

[0043] Δx=a x -x0,Δy=a y -y0,Δz=a z -z0

[0044] Where d is the distance from the point to the line, (x0, y0, z0) are the coordinates of any point;

[0045] Set a distance criterion threshold d th ,like

[0046] d <d th

[0047] The point is considered to be located on the straight line L, thereby achieving track splitting.

[0048] Furthermore, in step S3, a minimum point threshold min_num is set. If the number of points of the split track is less than min_num, the track is discarded.

[0049] Furthermore, the overfitting process is performed on the split tracks in step S4, specifically including:

[0050] Calculate the distances between all adjacent points on the split track:

[0051]

[0052] i=1,2,…,n-1

[0053] Set the point spacing judgment threshold g th and quantity judgment initial value N t =0, if

[0054] g i >g th , i=1,2,…,n-1

[0055] Then let N t The value of is increased by 1;

[0056] If N t ≥2, the track is discarded;

[0057] If N t=1, then when the spacing value is greater than g th The track between the two corresponding points is divided into two segments, and the segment with fewer points is discarded. If the number of points in the other segment is greater than or equal to min_num, it is regarded as the corrected reconstructed track; if it is less than min_num, the track is discarded.

[0058] If N t = 0, the track is retained.

[0059] The beneficial effects of the present invention are: the present invention improves the processing effect of particle track detectors such as time projection chambers on single-event multiple tracks, and provides a practical solution for high-energy particle multiple track detection and identification.

[0060] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0062] Figure 1 is the track reconstruction and correction map;

[0063] Figure 2 It is a multi-path splitting flow chart;

[0064] Figure 3 The effect diagram of single-case multi-track splitting; (a) shows all the tracks reconstructed before splitting; (b)-(f) are the five split tracks respectively. DETAILED DESCRIPTION

[0065] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0066] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0067] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the embodiments of the present invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the embodiments of the present invention.

[0068] Example 1:

[0069] Since white light sources have a wide energy range, the emitted neutrons, especially high-energy neutrons, will hit various particles on the target, substrate, or inner wall of the detector. Therefore, in the white light neutron source mode, there may not only be one particle track in a single event collected by the time projection chamber, and the tracks of multiple particles may be superimposed in the same event. Before further data analysis, it is necessary to reconstruct and separate the tracks in a single event. The present invention provides a data processing method for multiple tracks of a single event based on a time projection chamber, comprising the following steps:

[0070] First, the original points are pre-processed for denoising. During the experiment, the electronic collection and readout plate may be accidentally touched. In this case, scattered points appear in the particle track distribution, which deviate from the main track. This may be caused by noise or accidental touches. The signal amplitude of points caused by noise or accidental touches is usually much smaller than the signal amplitude of the actual particle track points. Using the maximum amplitude value in the example as the standard, the amplitude of all points is divided by the maximum value to obtain the amplitude ratio. By setting a threshold for the amplitude ratio, scattered points are removed.

[0071] All tracks in the event are then reconstructed using a 3D iterative Hough transform and weighted least squares. A line to be reconstructed (consisting of multiple 3D point coordinates) is represented as a vector, and the optimal line representation method is used to eliminate redundant parameters. The parameter space is discretized using the Platonic solid mosaic method, and all lines in a single event are reconstructed using a 3D Hough transform. The signal amplitudes of all points on the line are then weighted, and the Hough transformed line is corrected using the weighted least squares method.

[0072] After reconstruction and correction, the equation of each track is output. For a given line, the distance from all points in the case to the line is calculated. Points close to the line have the smallest distance to the line. A distance threshold is set, and points below the threshold are considered to be on the line, thus achieving track segmentation.

[0073] After track segmentation, the start and end points, as well as the lengths, of all tracks are calculated. When calculating track lengths, some tracks may have very few points but are exceptionally long. This is a result of overfitting. These tracks can be categorized into two types: one in which scattered points that originally did not belong to any line are fitted together because they happen to be spatially located in the same direction; the other in which points that do not belong to the track are fitted onto the track. Both of these cases share a common characteristic: the distances between points on these tracks and their neighbors exhibit one or more exceptionally large values. Therefore, the distances between all adjacent points on the segmented track need to be calculated and plotted as a scatter plot. In the first case, the distance scatter plot will show multiple exceptionally large values, while in the second case, the distance scatter plot will typically only have one exceptionally large value. Therefore, a threshold for point spacing is added. If at least one point spacing value exceeds the threshold, the track is discarded. If there is only one exceptionally large value, the track is split into two segments at the location with the wider distance between the points. The segment with fewer points is discarded, and the other segment becomes the corrected reconstructed track.

[0074] Example 2:

[0075] Taking a time projection chamber for measuring fission cross sections as an example, when measuring U-235 fission fragments on a white-light neutron source, the detector needs to reconstruct and split multiple particle tracks in a single event. In the process of processing the raw data points collected in the experiment, the data processing method for multiple tracks in a single event based on a time projection chamber in this embodiment includes the following steps:

[0076] 1. If Figure 1 As shown, the point cloud is obtained through the detector The coordinates of the original points of all particle tracks in and the signal amplitude h of each point are expressed as:

[0077] P i,j =(x i ,y i ,z i ,h i )

[0078] Among them, P i,j represents the i-th point of the j-th case, with a total of n points; x i ,y i ,z i Represents point P respectively i,jThe x-axis, y-axis, and z-axis coordinates, h i Indicates P i,j The signal amplitude. Taking the jth case as an example, find the maximum value H of the signal amplitude of all points j =max(h1,…,h i ), then the amplitude ratio R of each point is expressed as:

[0079]

[0080] Input amplitude ratio threshold R th ,like:

[0081] R i <R th

[0082] Then discard this point.

[0083] 2. For a line to be reconstructed, use a vector to represent it as:

[0084]

[0085] in is any point on the line; is the direction vector of the straight line, and the yaw angle is and the pitch angle θ is represented.

[0086] When the position of a line is represented by an arbitrary anchor point, this results in three parameters, one of which is redundant. To eliminate this redundancy, the optimal line representation method is used. This method first defines a plane passing through the origin and perpendicular to the line. Then, two parameters x' and y' are defined as the coordinates of the intersection of the line and the plane in the plane's own two-dimensional rectangular coordinate system. Then, for any point on the line, The expressions for x' and y' can be calculated as:

[0087]

[0088] And any point That is, the straight line expression can be expressed as:

[0089]

[0090] 3. Discretization of direction vector

[0091] The discretization of the parameter space uses the Platonic solid tessellation method. The Platonic solid with the largest number of vertices is the icosahedron, which has 12 vertices. The coordinates of the vertices can be described by the golden ratio r:

[0092]

[0093] For a point in the input point cloud Belong to the straight line The criterion is: the vertical distance from the point to the line is less than the Hough space, that is, the grid width dx of the plane where the intersection point (x′, y′) is located:

[0094]

[0095] The judgment condition dx here is calculated based on the boundary size of the input point cloud and can also be set manually.

[0096] 4. Weight Correction

[0097] The signal amplitude of each point is used as the weight, and the weighted least squares method is used to correct the line after the Hough transform. The core idea of the least squares method can be summarized as solving a multivariate equation system, which can be expressed as a matrix At = B. t is the parameter matrix of the line to be calculated, then t is:

[0098] t=(A T A) -1 A T B

[0099] Introduce the amplitude weight diagonal matrix:

[0100]

[0101] Then the expression of t becomes:

[0102] t=[A T W T WA] -1 A T W T WB

[0103] After weight correction is performed on the results of the Hough transform, the straight line equation of each track is output.

[0104] Repeat steps 1-4 until the number of points in X is reduced to the point where a straight line cannot be established, or the number of points is less than the preset minimum point threshold min_num, then exit the loop and proceed to the next step.

[0105] 5. Track Splitting

[0106] like Figure 2 As shown, after reconstruction, the straight line equation of each track is obtained. For a straight line L:

[0107]

[0108] Then calculate the distance from all points in the case to the line:

[0109]

[0110] Δx=a x -x0,Δy=a y -y0,Δz=a z -z0

[0111] Where d is the distance from the point to the line, and (x0, y0, z0) are the coordinates of any point.

[0112] Set a distance criterion threshold d th ,like

[0113] d <d th

[0114] The point is considered to be located on the straight line L, thereby achieving track splitting.

[0115] Enter the minimum number of track points threshold min_num. If the number of track points after splitting is less than min_num, the track will be discarded.

[0116] 6. Overfitting

[0117] Calculate the distance between all adjacent points (a total of n points) on the split track

[0118]

[0119] i=1,2,…,n-1

[0120] Input spacing judgment threshold g th and quantity judgment value N t =0, if

[0121] g i >g th , i=1,2,…,n-1

[0122] but

[0123] N t =N t +1

[0124] If N t ≥2, the track is discarded;

[0125] If N t =1, then when the spacing value is greater than g th The track is divided into two segments between the two corresponding points, and the segment with fewer points is discarded. If the number of points in the other segment is greater than or equal to min_num, it is regarded as the corrected reconstructed track; if it is less than min_num, the track is discarded. Generally, less than 5 points is considered to have too few points to be reconstructed, and it is very likely caused by noise, so it is usually discarded.

[0126] If N t = 0, the track is retained.

[0127] like Figure 3 The figure shows the effect of multi-track decomposition for a single event, where the origin is the original point and the blue line is the reconstructed track. (a) shows all the reconstructed tracks before decomposition; (b)-(f) show the five decomposition tracks.

[0128] In the above embodiments, references to "this embodiment" in the specification indicate that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple occurrences of "this embodiment" do not necessarily refer to the same embodiment.

[0129] In the above embodiments, although the invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory structures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed. The embodiments of the present invention are intended to encompass all such alternatives, modifications, and variations that fall within the broad scope of the appended claims.

[0130] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, any one of the methods in this embodiment is implemented.

[0131] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0132] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0133] Regarding the computer-readable storage medium in this embodiment, those skilled in the art will appreciate that all or part of the steps in the aforementioned method embodiments can be implemented using hardware associated with the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps in the aforementioned method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0134] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used for communication, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes the various steps of the above method.

[0135] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage.

[0136] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0137] The present invention can be used in a wide variety of general-purpose or special-purpose computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments that include any of the above.

[0138] The present invention may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A data processing method for single-event multi-path based on a time projection room, characterized by: The following steps are involved: S1: denoising preprocessing of original points; S2: Use three-dimensional iterative Hough transform and weighted least squares method to reconstruct and correct all the tracks in the case, and output the straight line equation of each track; S3: For a certain straight line, calculate the distance from all points in the case to the straight line, set a distance judgment threshold, and consider the points smaller than the distance judgment threshold as points on the straight line, thereby realizing track splitting; S4: Perform overfitting processing on the split track: calculate the distance between all adjacent points on the split track, set the point distance judgment threshold, if there are more than one point distance on a track that is greater than the point distance judgment threshold, then discard the track; if there is only one point distance greater than the point distance judgment threshold, then split the track into two sections at the position with wider point distance, discard the section with fewer points, and the other section is the corrected reconstructed track.

2. The data processing method for single-event multi-path based on a time projection room according to claim 1, characterized in that: The denoising preprocessing of the original points described in step S1 includes: taking the maximum amplitude value in the case as a standard, dividing the amplitudes of all points by the maximum value to obtain an amplitude ratio, and removing points with amplitude ratios less than a preset threshold.

3. The data processing method for single-event multi-path based on a time projection room according to claim 1, characterized in that: Step S2 specifically includes the following steps: The straight line to be reconstructed is represented by a vector: in is any point on the line; is the direction vector of the straight line, and the yaw angle is and the pitch angle θ represents; Use the optimal straight line representation method to eliminate redundant parameters; The parameter space is discretized using the Platonic solid tessellation method, and all lines in a single case are reconstructed using the 3D Hough transform. The signal amplitudes of all points in the straight line are taken as weights, and then the weighted least squares method is used to correct the straight line after Hough transformation.

4. The method for processing single-event multi-path data based on a time projection room according to claim 3, characterized in that: The optimal straight line representation method includes: First define a plane passing through the origin and perpendicular to the line; Then define two parameters x' and y' as the coordinates of the intersection of the line and the plane in the two-dimensional rectangular coordinate system of the plane itself. For any point on the line The expressions for x' and y' are calculated as: Any point That is, the straight line expression is expressed as:

5. The method for processing single-event multi-path data based on a time projection room according to claim 4, characterized in that: The method of discretizing the parameter space using the Platonic solid mosaic method and reconstructing all straight lines in a single case using the three-dimensional Hough transform specifically includes: The Platonic solid with the most vertices is the icosahedron, which has 12 vertices. The coordinates of the vertices are described by the golden ratio r: For a point in the input point cloud Belong to the straight line The criterion is: the vertical distance from the point to the line is less than the Hough space, that is, the grid width dx of the plane where the intersection point (x′, y′) is located: The value of dx is calculated based on the boundary size of the input point cloud or set manually.

6. The method for processing single-event multi-path data based on a time projection room according to claim 5, characterized in that: The signal amplitudes of all points in the straight line are taken as weights, and then the weighted least squares method is used to correct the straight line after the Hough transform, specifically including: The signal amplitude of each point is used as the weight, and the weighted least squares method is used to correct the straight line after the Hough transform. The core idea of the least squares method is to solve a multivariate equation system, which is expressed as At = B in the matrix. t is the parameter matrix of the straight line to be calculated, then t is: t=(A T A) -1 A T B Introduce the amplitude weight diagonal matrix: Then the expression of t becomes: t=[A T W T WA] -1 A T W T WB After weight correction is performed on the results of the Hough transform, the straight line equation of each track is output.

7. The method for processing single-event multi-path data based on a time projection room according to claim 1, characterized in that: Step S3 specifically includes the following steps: For a line L: Compute the distance from all points in this case to the line: Δx=a x -x0,Δy=a y -y0,Δz=a z -z0 Where d is the distance from the point to the line, (x0, y0, z0) are the coordinates of any point; Set a distance criterion threshold d th ,like d<d th The point is considered to be located on the straight line L, thereby achieving track splitting.

8. The method for processing single-event multi-path data based on a time projection room according to claim 1, characterized in that: In step S3, a minimum point threshold min_num is set. If the number of points of the split track is less than min_num, the track is discarded.

9. The method for processing data of multiple traces for a single event based on a time projection room according to claim 8, characterized in that: The overfitting process of the split tracks in step S4 specifically includes: Calculate the distances between all adjacent points on the split track: Set the point spacing judgment threshold g th and quantity judgment initial value N t =0, if g i >g th ,i=1,2,…,n-1 Then let N t The value of is increased by 1; If N t ≥2, the track is discarded; If N t =1, then when the spacing value is greater than g th The track between the two corresponding points is divided into two segments, and the segment with fewer points is discarded. If the number of points in the other segment is greater than or equal to min_num, it is regarded as the corrected reconstructed track; if it is less than min_num, the track is discarded. If N t = 0, the track is retained.

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