A Data Processing Method for Single-Event Multi-Path Based on Time Projection Room

Abstract

This invention relates to a data processing method for single-event multi-track detection based on a time projection chamber, belonging to the field of particle track analysis technology. The method includes the following steps: S1: Denoising preprocessing of the original points; S2: Reconstructing and correcting all tracks in the event using three-dimensional iterative Hough transform and weighted least squares method, outputting the linear equation of each track; S3: For a given straight line, calculating the distance from all points in the event to the straight line, setting a distance threshold, and considering points less than the threshold as points located on the straight line, thereby achieving track splitting; S4: Overfitting processing of the split tracks. This invention improves the processing effect of time projection chamber and other particle track detectors on single-event multi-track detection, providing a practical solution for high-energy particle multi-track detection and identification.

Description

Technical Field

[0001] This invention belongs to the field of particle track analysis technology and relates to a data processing method for single-event multi-track based on a time projection chamber. Background Technology

[0002] Currently, in the field of high-energy physics experiments, commonly used particle track detectors, such as time projection chambers, can detect electrons ionized from charged particles. Pixelated readout detectors (such as microgrid planar gas detectors and gas electron multipliers) locate the position coordinates of the ionized electron clusters, thereby reconstructing and identifying the tracks of the particles under test. Current algorithms for three-dimensional particle track reconstruction (such as least squares, Hough transform, and random sampling consensus algorithms) can reconstruct and identify tracks from a single source of particles well. However, when dealing with wide energy ranges and multi-particle reactions, current algorithms cannot effectively reconstruct and identify multiple particle tracks. Taking white-light neutrons as an example, neutrons with a wide energy range can produce various interactions with a target (such as an isotope target of uranium), such as neutron-induced fission reactions producing fission fragments, the decay of heavy fission products producing alpha particles, and elastic scattering with the target substrate producing recoil protons. In other words, multiple particles, i.e., multiple tracks, may be superimposed in a single detection event. Currently, there are no specific methods in China for reconstructing and identifying multiple tracks in a single event. This invention is based on the three-dimensional Hough transform and processes single-event multi-track cases by combining the calculation of the distance from the midpoint of the track to the reconstructed line and the spacing between adjacent points. Existing case results show that this technique has excellent processing performance and will provide important reference for existing particle track processing methods. Summary of the Invention

[0003] In view of this, the purpose of this invention is to address the problem that particle track detectors such as time projection chambers cannot effectively reconstruct and identify various particle tracks when dealing with multiple tracks in a single event. Based on the three-dimensional iterative Hough transform, an algorithm is proposed that can accurately identify and decompose multiple tracks in a single event.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

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

[0006] S1: Perform noise reduction preprocessing on the original points;

[0007] S2: Reconstruct and correct all tracks in the case using the three-dimensional iterative Hough transform and weighted least squares method, and output the straight line equation for each track.

[0008] S3: For a given straight line, calculate the distance from all points in the instance to the straight line, set a distance criterion threshold, and consider points with distances less than the distance criterion threshold as points located on the straight line, thereby achieving track splitting;

[0009] S4: Overfitting processing of the split track: Calculate the distance between all adjacent points on the split track, set a point distance judgment threshold. If more than one point on a track has a distance greater than the point distance judgment threshold, the track is discarded. If only one point has a distance greater than the point distance judgment threshold, the track is divided into two segments at the position with a wider point distance. The part with fewer points is discarded, and the other part is the corrected reconstructed track.

[0010] Furthermore, the noise reduction preprocessing of the original points described in step S1 includes: using the maximum amplitude value in the example as the standard, dividing the amplitude of all points by the maximum value to obtain the amplitude ratio, and removing points whose amplitude ratio is less than a preset threshold.

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

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

[0013]

[0014] in Let be any point on the line; The direction vector of the line is represented by the deflection angle. And pitch angle θ represents;

[0015] Eliminate redundant parameters using the optimal linear representation method;

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

[0017] The signal amplitude at all points on the straight line is taken as the weight, and then the weighted least squares method is used to correct the straight line after the Hough transform.

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

[0019] First, define a plane that passes through the origin and is perpendicular to the line;

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

[0021]

[0022] any point That is, the expression for a straight line is:

[0023]

[0024] Furthermore, the discretization of the parameter space using the Pareto tessellation method and the reconstruction of all lines in a single instance 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 using the golden ratio r.

[0026]

[0027] For a point in the input point cloud Belongs to a straight line The criterion is: the perpendicular distance from the point to the line is less than the Hough space, which is the grid width dx of the plane containing the intersection point (x′, y′).

[0028]

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

[0030] Furthermore, the signal amplitude at all points on the straight line is taken as a weight, and then the weighted least squares method is used to correct the straight line after the Hough transform, specifically including:

[0031] Using the signal amplitude at each point as a weight, the weighted least squares method is applied to correct the straight line after the Hough transform. The core idea of ​​the least squares method is to solve a system of multivariate equations, represented by the matrix At = B, where t is the parameter matrix of the line to be determined. Then t is:

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

[0033] Introduce an amplitude weighting diagonal matrix:

[0034]

[0035] Then the expression for t becomes:

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

[0037] After weighting the results of the Hough transform, the linear equation for each path is output.

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

[0039] For a certain straight line L:

[0040]

[0041] Calculate the distance from all points in this example 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, and (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 then considered to be located on the straight line L, thus achieving the splitting of the track.

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

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

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

[0051]

[0052] i = 1, 2, ..., n-1

[0053] Set the threshold for determining the distance between points g th And quantity judgment initial value N t =0, if

[0054] g i >g th i = 1, 2, ..., n-1

[0055] Let N t Increment the value by 1;

[0056] If N t If the value is ≥2, then the path is discarded.

[0057] If N t=1, then the spacing value is greater than g. th The track is divided into two segments between the two corresponding points. 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 considered as the corrected reconstructed track; if it is less than min_num, the track is discarded.

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

[0059] The beneficial effects of this invention are as follows: This invention improves the processing performance of particle track detectors such as time projection chambers for single-event multitracks, and provides a practical solution for the detection and identification of high-energy particle multitracks.

[0060] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0061] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

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

[0063] Figure 2 This is a flowchart of multi-path decomposition;

[0064] Figure 3 The diagram shows the result of splitting multiple tracks in a single case; where (a) represents all tracks reconstructed before splitting; and (b)-(f) represent the five tracks that were split out. Detailed Implementation

[0065] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed 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 representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

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

[0067] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the 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 embodiments of the invention.

[0068] Example 1:

[0069] Because white light sources have a wide energy range, the emitted neutrons, especially high-energy neutrons, will eject various particles onto the target, substrate, or detector inner wall. Therefore, in white light neutron source mode, a single event acquired by the time projection chamber may contain more than one particle track; tracks of multiple particles may be superimposed on the same event. Before further data analysis, it is necessary to reconstruct and separate each track in a single event. This invention provides a data processing method for multiple tracks in a single event based on a time projection chamber, including the following steps:

[0070] First, the original points are pre-processed to remove noise. During the experiment, the electron collection readout board may experience false triggers. In such cases, some scattered points deviating from the main track will appear in the particle track distribution, possibly caused by noise or false triggers. The signal amplitude of points caused by noise or false triggers is usually much smaller than the signal amplitude of the actual particle track points. Using the maximum amplitude value in the example as a 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, the scattered points are removed.

[0071] Then, all tracks in the case are reconstructed using the three-dimensional iterative Hough transform and weighted least squares method. A straight line to be reconstructed (composed of multiple three-dimensional point coordinates) is represented by a vector, and the optimal straight line representation method is used to eliminate redundant parameters. The parameter space is discretized using the Pareto tessellation method, and all straight lines in a single case are reconstructed using the three-dimensional Hough transform. Next, the signal amplitude of all points on the straight line is taken as weights, and then the weighted least squares method is used to correct the Hough transformed straight line.

[0072] After reconstruction and correction, the linear equation for each track is output. For a given line, the distance from all points in the instance to that line is calculated. Points closer to the line have the smallest distance to it. A distance threshold is set, and points with distances less than this threshold are considered to be located on the line, thus achieving track splitting.

[0073] After track splitting, 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 exceptionally long lengths; this is a result of overfitting. These tracks can be categorized into two types: one where scattered points that don't belong to any straight line are fitted together because their spatial positions happen to be in the same direction; and another where points that don't belong to the track are fitted onto it. Both types share a common characteristic: the distances between points on these tracks and their adjacent points will have one or more exceptionally large values. Therefore, it's necessary to calculate the distances between all adjacent points on the split tracks and plot them as a scatter plot. The first type of distance scatter plot shows multiple exceptionally large values, while the second type typically only shows one exceptionally large value. Therefore, a threshold for point distance is added. If more than one point distance exceeds this threshold, the track is discarded. If there is only one exceptionally large value, the track is split into two segments at the wider point distance location; 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 this detector measures U-235 fission fragments at a white-light neutron source, it is necessary to reconstruct and separate multiple particle tracks in a single event. In processing the raw data points acquired from 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. For example Figure 1 As shown, point clouds are acquired through a detector. The original point coordinates of all particle tracks and the signal amplitude h of each point are represented as follows:

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

[0078] Where P i,j x represents the i-th point of the j-th instance, with a total of n points; i ,y i ,z i They represent point P respectively. i,jx-axis, y-axis, z-axis coordinates, h i P represents i,j The signal amplitude. Taking the j-th case as an example, find the maximum value H of the signal amplitude at all points in it. j =max(h1,…,h) i If the amplitude ratio R at each point is:

[0079]

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

[0081] R i <R th

[0082] Then discard that point.

[0083] 2. For a straight line to be reconstructed, it can be represented by a vector as follows:

[0084]

[0085] in Let be any point on the line; The direction vector of the line is represented by the deflection angle. The pitch angle θ represents the angle of attack.

[0086] When the position of a line is represented by an arbitrary point, this results in three parameters, one of which is redundant. To eliminate this redundancy, an optimal line representation 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 point of the line and the plane in the plane's own two-dimensional Cartesian coordinate system. Then, for any point on the line... The expressions for x′ and y′ can be calculated as follows:

[0087]

[0088] And any point In other words, the expression for a straight line can be represented as:

[0089]

[0090] 3. Discretization of direction vectors

[0091] The parameter space is discretized using the Pareto tessellation method. The Pareto solid with the most vertices is the icosahedron, which has 12 vertices. The coordinates of the vertices can be described using the golden ratio r.

[0092]

[0093] For a point in the input point cloud Belongs to a straight line The criterion is: the perpendicular distance from the point to the line is less than the Hough space, which is the grid width dx of the plane containing the intersection point (x′, y′).

[0094]

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

[0096] 4. Weight Adjustment

[0097] Using the signal amplitude at each point as a weight, the weighted least squares method is applied to correct the straight line after the Hough transform. The core idea of ​​the least squares method can be reduced to solving a system of multivariate equations, represented by matrix at = B, where t is the parameter matrix of the line to be solved. Then t is:

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

[0099] Introduce an amplitude weighting diagonal matrix:

[0100]

[0101] Then the expression for t becomes:

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

[0103] After weighting the results of the Hough transform, the linear equation for each path 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 number of points threshold min_num. Then exit the loop and proceed to the next step.

[0105] 5. Track decomposition

[0106] like Figure 2 As shown, the linear equations for each track are obtained after reconstruction. For a certain line L:

[0107]

[0108] Then calculate the distance from all points in the example 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 threshold d for the distance criterion. th ,like

[0113] d <d th

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

[0115] Input the minimum number of points threshold min_num for the track. If the number of points in the split track is less than min_num, then discard the track.

[0116] 6. Overfitting treatment

[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 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 If the value is ≥2, then the path is discarded.

[0125] If N t =1, then the spacing value is greater than g. th The track is divided into two segments between the two corresponding points. 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 considered as the corrected reconstructed track. If it is less than min_num, the track is discarded. Generally, fewer than 5 points are considered too few to reconstruct the track, and it is very likely caused by noise. The usual practice is to discard it.

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

[0127] like Figure 3 The image shows the result of splitting multiple tracks in a single instance, where the origin is the original point and the blue lines represent the reconstructed tracks. (a) shows all reconstructed tracks before splitting; (b)-(f) show the five tracks that were split.

[0128] In the above embodiments, the reference to "this embodiment" in the specification indicates that a specific feature, structure, or characteristic described in connection with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple appearances 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 substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory structures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed. The embodiments of the invention are intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims.

[0130] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the methods in this embodiment.

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

[0132] The memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to cause the terminal to perform any of the methods in this embodiment.

[0133] As will be understood by those skilled in the art, the computer-readable storage medium described in this embodiment allows for the implementation of all or part of the steps in the above method embodiments by computer program-related hardware. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above method embodiments; and 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 between them. The memory is used to store computer programs, the communication interface is used to perform communication, and the processor and the transceiver are used to run the computer programs, so that the electronic terminal performs the steps of the above method.

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

[0136] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0137] This invention can be used in a wide range of general-purpose or special-purpose computing system environments or configurations. Examples include: 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 including any of the above systems or devices, etc.

[0138] This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in 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 intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within 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 in that: Includes the following steps: S1: Perform noise reduction preprocessing on the original points; S2: Reconstruct and correct all tracks in the case using the three-dimensional iterative Hough transform and weighted least squares method, and output the straight line equation for each track; Step S2 specifically includes the following steps: The line to be reconstructed is represented by a vector as follows: in Let be any point on the line; The direction vector of the line is represented by the deflection angle. and pitch angle express; Eliminate redundant parameters using the optimal linear representation method; The parameter space is discretized using the Platonic tessellation method, and all lines in a single case are reconstructed using the three-dimensional Hough transform. The signal amplitude at all points on the straight line is taken as the weight, and then the weighted least squares method is used to correct the straight line after the Hough transform. The optimal straight-line representation includes: First, define a plane that passes through the origin and is perpendicular to the line; Then define two parameters. and Let the coordinates of the intersection point of the line and the plane in the two-dimensional rectangular coordinate system of the plane itself be given by the coordinates of the intersection point of the line and the plane. For any point on the line... Calculate and The expression is: any point That is, the expression for a straight line is: The discretization of the parameter space using the Pareto tessellation method and the reconstruction of all lines in a single instance using the three-dimensional Hough transform specifically includes: The Platonic solid with the most vertices is the icosahedron, which has 12 vertices, and the coordinates of the vertices are expressed using the golden ratio. To describe: For a point in the input point cloud Belongs to a straight line The criterion is: the perpendicular distance from the point to the line is less than the Hough space, which is the intersection point. Grid width of the plane : The value can be calculated based on the boundary size of the input point cloud, or set manually. S3: For a given straight line, calculate the distance from all points in the instance to the straight line, set a distance criterion threshold, and consider points with distances less than the distance criterion threshold as points located on the straight line, thereby achieving track splitting; S4: Overfitting processing of the split track: Calculate the distance between all adjacent points on the split track, set a point distance judgment threshold. If more than one point on a track has a distance greater than the point distance judgment threshold, the track is discarded. If only one point has a distance greater than the point distance judgment threshold, the track is divided into two segments at the position with a wider point distance. The part with fewer points is discarded, and the other part 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 noise reduction preprocessing of the original points described in step S1 includes: using the maximum amplitude value in the example as the standard, dividing the amplitude of all points by the maximum value to obtain the amplitude ratio, and removing points whose amplitude ratio is 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: The process involves taking the signal amplitudes at all points along the straight line as weights, and then using the weighted least squares method to correct the straight line after the Hough transform. Specifically, this includes: Using the signal amplitude at each point as a weight, the weighted least squares method is employed to correct the straight line after the Hough transform. The core idea of ​​the least squares method is to solve a system of multivariate equations, which can be represented by a matrix. , Let be the linear parameter matrix to be determined, then for: Introduce an amplitude weighting diagonal matrix: but The expression becomes: After weighting the results of the Hough transform, the linear equation for each path is output.

4. The data processing method for single-event multi-path based on a time projection room according to claim 1, characterized in that: Step S3 specifically includes the following steps: For a certain straight line : Calculate the distance from all points in this example to the line: in Let be the distance from the point to the line. Let the coordinates be any point; Set a distance criterion threshold ,like Then the point is considered to lie on the line. The points on the track are used to split the track.

5. The data processing method for single-event multi-path based on a time projection room according to claim 1, characterized in that: In step S3, a minimum number of points threshold is set. If the number of track points after splitting is less than If so, then discard that path.

6. The data processing method for single-event multi-path based on a time projection room according to claim 5, characterized in that: The overfitting process described in step S4 specifically includes: Calculate the distance between all adjacent points on the split track: Set the threshold for judging the distance between points Initial value for quantity judgment ,like Then let Increment the value by 1; if If so, then discard the path; if Then when the spacing value is greater than The track is divided into two segments between the two corresponding points. The segment with fewer points is discarded, and the other segment is selected if it contains more than or equal to the number of points. If it is less than 1, then it is considered a corrected reconstructed track; if it is less than 1, then it is considered a corrected reconstructed track. If so, then discard that path; if If so, then the track will be preserved.

Images