Self-adaptive energy calibration method of Timepix3 detector

Abstract

The invention discloses an adaptive energy calibration method for a Timepix3 detector, and belongs to the field of plasma diagnosis, and the method comprises the steps: collecting a photon event through a detector, the photon event comprising arrival time, coordinates and energy information; sorting the photon events according to the arrival time of the photon events to obtain a sequence; mapping the sequence to an empty matrix in a time window sliding mode, searching and recording the area of a pixel cluster and the sum of count values, and compensating the count values according to a set threshold value; classifying according to the area of the pixel cluster, summing the counting values of the pixels in the pixel cluster to obtain the volume of the pixel cluster, and drawing a statistical histogram of the volume of the pixel cluster to form an energy spectrum; identifying an energy peak of each energy spectrum and performing Gaussian fitting, calculating a peak abscissa, and establishing a relationship between the volume of a pixel cluster and actual energy; and completing energy calibration by utilizing linear fitting of volume energy data of multiple groups of pixel clusters. According to the invention, the requirements for setting parameters such as radioactive source intensity, detector threshold, exposure time and the like are reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of plasma diagnosis, and in particular relates to an adaptive energy calibration method for a Timepix3 detector. Background Art

[0002] In magnetic confinement fusion research, the interaction between fast particles and magnetohydrodynamic (MHD) instabilities is a key physical process affecting tokamak plasma confinement performance and particle loss. High-precision soft X-ray radiation diagnostics allow direct observation of this interaction. The Timepix3 detector can capture information such as the energy, arrival time, and coordinates of individual X-ray photon events. Therefore, accurate detector energy calibration is essential.

[0003] The Timepix3 detector has excellent energy spectrum resolution, allowing it to detect photons across different energy ranges. In the detector's Time-Over-Threshold (TOT) mode, photons interact with detector pixels, and their energy information is recorded, forming a single-photon event. However, due to the charge-sharing effect, the photon's energy may be absorbed by adjacent pixels, leading to the formation of pixel clustering, where the energy of a single photon is deposited in multiple adjacent pixels. Furthermore, if the radiation source intensity is too high and the exposure time is set too long, the energy information generated by subsequent photons reaching the same pixel can be superimposed, resulting in a decrease in the number of photon events and an increase in energy. During energy calibration, if the detector threshold is set too high, the energy information deposited in adjacent pixels can be lost, distorting the energy spectrum.

[0004] In traditional photon-counting detector energy calibration, a weaker radiation source is typically used. Single-pixel energy spectra are acquired by reducing exposure time and setting the threshold as low as possible. Some detectors can operate in special modes, such as the Medipix3 detector, which features a charge summation mode that dynamically examines the charge generated in any four-pixel matrix. The charge is allocated to the pixel with the highest signal intensity, mitigating the impact of charge sharing. Existing methods cannot effectively address the energy spectrum distortion caused by charge sharing, long exposure times, and inappropriate threshold settings during the Timepix3 detector energy calibration process. Summary of the Invention

[0005] This paper proposes an adaptive energy calibration method for the Timepix3 detector to address systematic errors in soft X-ray energy spectrum calibration caused by charge sharing, long exposure times, and threshold settings. This method eliminates energy spectrum distortion and energy peak position shifts caused by non-ideal physical processes within the detector, improving energy calibration accuracy.

[0006] The specific technical solutions of the present invention are:

[0007] An adaptive energy calibration method for a Timepix3 detector comprises the following steps:

[0008] Step 1: Collect photon event data using the Timepix3 detector. The photon event data includes arrival time, coordinates, and energy information.

[0009] Step 2: Sort the photon events containing coordinates and energy information according to the arrival time of the photon events to obtain a photon event sequence;

[0010] Step 3: Map the sorted photon event sequence to an empty matrix in a time window sliding manner, search and record the area and count value of the pixel cluster, i.e., the sum of energy information, and then compensate the count value according to the set threshold;

[0011] Step 4: classify the pixel clusters by their area, sum the count values ​​of each pixel in the pixel cluster to obtain the pixel cluster volume, and draw a statistical histogram of the pixel cluster volume to form an energy spectrum;

[0012] Step 5: Identify the energy peaks of each energy spectrum and perform Gaussian fitting. Calculate the peak abscissa based on linear weighting according to the proportion of pixel clusters with different areas, and establish the corresponding relationship between the pixel cluster volume and the actual energy.

[0013] Step 6: Energy calibration is completed using linear fitting of multiple sets of pixel cluster volumes and actual energy data.

[0014] The present invention has the following beneficial effects:

[0015] 1. The energy calibration method of the Timepix3 detector is optimized to improve operational robustness and adapt to complex environmental conditions. In the energy calibration experiment of traditional photon counting detectors, it is necessary not only to find a radioactive source with weaker intensity, but also to adjust parameters such as the detector threshold and exposure time to obtain a sufficient number of pixel clusters that are not stacked with each other. The present invention uses the original data of photon events collected by the detector to restore the photon events within a limited time window into pixel clusters, and compensates the sum of the pixel cluster count values ​​according to the threshold set by the detector, effectively avoiding the stacking between pixel clusters, reducing the requirements for the setting of parameters such as the radiation source intensity and the detector threshold and exposure time, and simplifying the experimental process.

[0016] 2. Effectively mitigates the problems of photon energy loss and inaccurate energy deposition caused by the charge sharing effect. By restoring photon events to pixel clusters and compensating the sum of pixel cluster counts based on the detector's set threshold, the energy deposition position and energy level of individual X-ray photons are accurately restored.

[0017] 3. Increase the data processing speed. By translation mapping conversion to reduce the 512x512 matrix to 256x256 or smaller matrix, this processing method well preserves the position relationship between the photon events, significantly improves the search efficiency of the pixel cluster.

[0018] 4. Energy peak identification and energy peak position offset calibration. Discard the inaccurate points on the left side of the energy spectrum affected by noise and threshold setting, perform Gaussian fitting to obtain the horizontal coordinates (pixel cluster volume) corresponding to the energy peak of different area pixel clusters, and finally calibrate the offset peak value by linear weighting. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 : Experimental setup diagram;

[0020] Figure 2 : Pixel cluster schematic diagram;

[0021] Figure 3 : Pixel cluster recovery flowchart;

[0022] Figure 4 : Translation mapping schematic diagram;

[0023] Figure 5 : Metal zirconium characteristic X-ray fluorescence spectrum schematic diagram;

[0024] Figure 6 : Energy peak identification, Gaussian fitting, and peak horizontal coordinate diagram. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other. In order to achieve the above purpose, the present application adopts the following technical scheme.

[0026] The present application provides an adaptive energy calibration method for a Timepix3 detector, comprising the following steps:

[0027] Step 1. Data acquisition. The characteristic X-ray fluorescence of the metal material excited by the synchrotron radiation source is selected. In the threshold crossing time mode of the Timepix3 detector, a long enough exposure time is set to ensure that a sufficient number of photon events are obtained.

[0028] Step 2. Data preprocessing. The Timepix3 detector collects data and saves it in both image and raw data formats. The Timepix3 detector is event-driven, and the saved raw data contains the arrival time, coordinates, and energy of each photon event. The arrival time, coordinates, and energy of each photon event in the raw data are extracted, and the sequence containing the coordinates and energy information is sorted according to the arrival time of the photon event.

[0029] Step 3. Pixel cluster recovery. Initialize an empty matrix based on the Timepix3 detector pixel array size and set a time window of appropriate length. Extract photon event information from the sequence, including coordinates and energy information, according to the set time window. Fill the corresponding position of the empty matrix with the energy information of each photon event based on its x and y coordinates. After the photon event information within the current time window is extracted, search for pixel clusters in the matrix and classify them according to their area. Record the count value of each pixel cluster. A pixel cluster consists of one or more pixels. The total count value of a pixel cluster is the sum of the count values ​​of all pixels that make up the cluster. The count value of each pixel represents the energy of the photon event, that is, the total energy information recorded (the cluster volume). After recording the pixel clusters within the current time window, slide the time window and clear the matrix. Repeat the above process of extracting, searching, and recording pixel clusters until all photon events have been restored to pixel clusters.

[0030] Unreasonable threshold settings may cause the loss of energy deposited in adjacent pixels. After obtaining the restored pixel cluster, it is necessary to set the threshold value set during acquisition based on the noise generated by the detector itself and the noise level of the current experimental environment to ensure that data acquisition will not be interfered by noise and a sufficient number of photon events can be collected. If the threshold value is set too low, it will be interfered by noise, and if the threshold value is set too high, photon events cannot be effectively acquired. The total count value of the pixel cluster is compensated accordingly, and compensation is performed based on the number of pixels contained in the pixel cluster and the currently set threshold value. Let the total count value after compensation be , the total count value before compensation is , the number of pixels in the pixel cluster is n, the compensation coefficient is k, and the compensation coefficient is determined by the set threshold, then .

[0031] It should be noted that the time window length needs to be set reasonably. The time window length should be selected to ensure that photon events belonging to the same pixel cluster are included in the same time window, while ensuring that there is no overlap between pixel clusters. In actual data processing, the time window size is set to 1000ns to 10000ns. A longer time window can increase the data processing speed, but it will increase the probability of overlap between different pixel clusters, resulting in the inability to accurately separate each pixel cluster. If the time window is set too short, a large-area pixel cluster may be split into several small-area pixel clusters, resulting in a tail effect in the low-energy segment of the energy spectrum, and also increasing the data processing time.

[0032] Step 4. Draw the energy spectrum. Draw the energy spectrum for each pixel cluster according to its area. Sum the count values ​​of each pixel within the cluster to obtain a value, which is called the cluster volume. Draw the statistical histogram of the cluster volume for each pixel cluster of different areas. These statistical histograms are the pixel cluster energy spectra.

[0033] Step 5. Energy peak identification. Identify energy peaks from all pixel cluster energy spectra and perform Gaussian fitting. The peak intensity and position vary for pixel clusters of different areas. Calculate the proportion of pixel clusters of different areas and use linear weighting to determine the abscissa of the peak position. The abscissa of the peak position (pixel cluster volume) corresponds to the actual energy of the peak, resulting in a set of pixel cluster volumes and corresponding energies.

[0034] Step 6. Complete energy calibration. Based on the input data, multiple sets of pixel cluster volumes and corresponding energies are obtained. Linear fitting is used to obtain the relationship between pixel cluster volume and energy information to complete energy calibration.

[0035] In step 1, the radiation source uses the characteristic X-ray fluorescence of metal materials. Figure 1 As shown, a synchrotron radiation source is used to illuminate different metal foils to stimulate their characteristic X-rays. The detector is set with a sufficient exposure time to obtain a sufficient number of photon events.

[0036] In step 2, pixel cluster recovery, search, and recording are as follows:

[0037] Taking a pixel as the center, if the eight adjacent pixels around the pixel have count values, then all pixels with count values ​​belong to the same pixel cluster. Figure 2 It is shown as a pixel cluster, and the numerical value represents the pixel count value, that is, the energy information.

[0038] Pixel cluster recovery process, such as Figure 3All the photon events are sorted by arrival time, and a time window with proper length is set. A 512x512 empty matrix is initialized, and the energy information of each photon event in the window is filled into the corresponding position of the matrix according to the x, y coordinates. The pixel cluster count value sum is compensated according to the threshold set during data acquisition. The pixel clusters are searched in the matrix, and the area size and count value sum of each pixel cluster are recorded. After the current time window pixel cluster is searched and recorded, the time window is slid and the matrix is emptied. Repeat the above operation until all the photon events are recovered into pixel clusters, and each pixel cluster is recorded. In order to reduce the data processing time and increase the search efficiency of the pixel cluster, a smaller empty matrix is constructed during the recovery of the pixel cluster. At this time, the coordinates of the photon events need to be mapped from the original large matrix (512x512) to the small matrix (256x256). Translation mapping is used because when searching for pixel clusters, the absolute coordinates of each photon event are not concerned, and only the positional relationship between the photon events is needed.

[0039] As shown in Figure 4 , the dashed part represents a 256x256 matrix, and the solid part represents a 512x512 matrix.

[0040] x, y represent the row and column positions of the pixel in the matrix. Let the pixel in the original matrix be , and the element in the newly constructed small matrix be , The mapping relationship is:

[0041] If , ;

[0042] If , ;

[0043] If , ;

[0044] If , .

[0045] In step 5, the energy peak is identified. The energy peak is identified from all the pixel energy spectra, and Gaussian fitting is performed. The proportion of pixel clusters of different areas is calculated, and the abscissa of the peak position is calibrated using linear weighting.

[0046] Let the calibrated peak position be y, the proportion of pixel clusters of different areas be , and the peak position of pixel clusters of different areas be ;

[0047] Then .

[0048] As Figure 5 shown, it is the pixel cluster volume histogram of the characteristic X-ray of metal zirconium, that is, the pixel cluster energy spectrum. Among them, the pixel cluster area is different, and the corresponding energy spectrum is also different. The larger the pixel cluster volume, the greater the energy of the detected photons.

[0049] As Figure 6 shown, it is the Gaussian fitting result of the energy peak. Among them respectively represent the horizontal coordinates corresponding to the energy peak value of the first area pixel cluster, the second area pixel cluster, the third area pixel cluster, and the fourth area pixel cluster.

Claims

1. An adaptive energy calibration method for a Timepix3 detector, characterized in that: The following steps are involved: Step 1: Collect photon event data using the Timepix3 detector. The photon event data includes arrival time, coordinates, and energy information. Step 2: Sort the photon events containing coordinates and energy information according to the arrival time of the photon events to obtain a photon event sequence; Step 3: Map the sorted photon event sequence to an empty matrix in a time window sliding manner, search and record the area and count value of the pixel cluster, i.e., the sum of energy information, and then compensate the count value according to the set threshold; Step 4: classify the pixel clusters by their area, sum the count values ​​of each pixel in the pixel cluster to obtain the pixel cluster volume, and draw a statistical histogram of the pixel cluster volume to form an energy spectrum; Step 5: Identify the energy peaks of each energy spectrum and perform Gaussian fitting. Calculate the peak abscissa based on linear weighting according to the proportion of pixel clusters with different areas, and establish the corresponding relationship between the pixel cluster volume and the actual energy. Step 6: Energy calibration is completed using linear fitting of multiple sets of pixel cluster volumes and actual energy data.

2. The adaptive energy calibration method of the Timepix3 detector according to claim 1, characterized in that: In step 1, the metal foil is excited by synchrotron radiation to generate characteristic X-ray fluorescence, and the Timepix3 detector detects the X-ray fluorescence to obtain photon events.

3. The adaptive energy calibration method of the Timepix3 detector according to claim 1, characterized in that: The step 3 is specifically as follows: an empty matrix is ​​initialized according to the size of the detector pixel array, and a time window of appropriate length is set, information of photon events is extracted from the information sequence in sequence according to the set time window, and energy information of the photon events is filled in the corresponding position of the empty matrix according to the x, y coordinates of each photon event, after the extraction of the photon event information in the current time window is completed, pixel clusters are searched in the matrix, and classified according to the area size of the pixel clusters, and the total count value of each pixel cluster, i.e., the volume of the pixel cluster, is recorded; after the pixel clusters in the current time window are recorded, the time window is slid and the matrix is ​​cleared, and the above process of extracting, searching, and recording pixel clusters is repeated until all photon events are restored to pixel clusters.

4. The adaptive energy calibration method of the Timepix3 detector according to claim 3, characterized in that: The pixel cluster search in step 3 is specifically as follows: taking any pixel as the center, if there are count values ​​in the eight adjacent pixels around it, then all pixels with count values ​​belong to the same pixel cluster.

5. The adaptive energy calibration method of the Timepix3 detector according to claim 3, characterized in that: In the pixel cluster recovery stage in step 3, the 512×512 matrix coordinates are converted into 256×256 matrix coordinates through translation mapping.

6. The adaptive energy calibration method of the Timepix3 detector according to claim 1, characterized in that: The calculation formula of the peak horizontal coordinate y in step 5 is: ； The proportion of pixel clusters of different areas is , the peak position of pixel clusters with different areas is .

7. The adaptive energy calibration method of the Timepix3 detector according to claim 5, characterized in that: x, y represent the row and column positions of the pixel in the matrix. Suppose the pixel in the 512×512 matrix is , the elements in the reconstructed 256×256 matrix are , The mapping relationship is: if , ; if , ; if , ; if , .

8. The adaptive energy calibration method of the Timepix3 detector according to claim 1, characterized in that: The time window size is set from 1000ns to 10000ns.

9. The adaptive energy calibration method of the Timepix3 detector according to claim 1, characterized in that: In step 3, the compensation count value is specifically: the total count value after compensation is , the total count value before compensation is , the number of pixels in the pixel cluster is n, the compensation coefficient is k, and the compensation coefficient is determined by the set threshold, then .

10. The adaptive energy calibration method of the Timepix3 detector according to claim 2, characterized in that: The metal foil is more preferably a metal zirconium foil.

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