- Email: [email protected]
Automatic impurity spectral line identification algorithm with noise reduction for fusion plasmas
Fusion Engineering and Design 153 (2020) 111459
Contents lists available at ScienceDirect
Fusion Engineering and Design journal homepage: www.elsevier.com/locate/fusengdes
Automatic impurity spectral line identification algorithm with noise reduction for fusion plasmas
T
Haewon Shina,b, Inwoo Songb,c, YoungHwa And, Wonho Choea,b,* a
Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea Impurity and Edge Plasma Research Center, KAIST, Daejeon 34141, Republic of Korea c Department of Physics, KAIST, Daejeon 34141, Republic of Korea d National Fusion Research Institute, Daejeon 34133, Republic of Korea b
A R T I C LE I N FO
A B S T R A C T
Keywords: Real-time post processing VUV spectroscopy Noise removal Impurity spectral line identification
Accurate diagnostics of impurity behavior inside the tokamak plasma is essential for long and stable plasma operation and also for machine protection in fusion devices including ITER. In this study, a numerical code for identifying impurity line spectra was developed and assessed by utilizing the ITER-relevant vacuum ultraviolet (VUV) spectroscopic diagnostic system in KSTAR. For real-time analysis of a spectrum, it is necessary to reduce the noise level of the data caused by high neutron and gamma photon fluxes reaching the detector such as a charge-coupled device detector. The code uses a high-order derivative method that distinguishes relatively sharp noises from spectral lines. Tests with synthetic spectra showed successful noise reduction of approximately 90%. In addition, an in-situ wavelength calibration algorithm was developed by using representative carbon emission lines (C III and C IV) as markers that appear during the current ramp-up phase in carbon-walled devices. This algorithm is followed by a matching algorithm that enables the annotation of an ionic state of an impurity on the line peak by referencing the NIST atomic database. From the integrated processing algorithm, simulated VUV spectra were tested and the results showed successful automatic annotation of the spectral lines after noise reduction.
1. Introduction Accurate diagnostics of impurities inside the fusion plasma and providing key parameters regarding the impurities are essential not only for stable plasma operation but also for tokamak machine protection [1]. For these purposes, the vacuum ultraviolet (VUV) spectrometry has been actively utilized in tokamak plasma research for decades since tungsten, as the divertor material for ITER, mostly emits VUV and extreme UV (EUV) line radiation once it is inside the plasma. It is planned that ITER VUV spectrometer will adopt a charge-coupled device (CCD) camera for its several advantages [2]. However, in future fusion devices including ITER, high-energy particle fluxes such as neutron and gamma photon fluxes will be much higher than in current tokamaks running with D–D reactions and will result in randomly distributed spiky noises in the spectral data, whose amplitude may become even larger than the real atomic spectra [3]. In the case of JET, the SPRED VUV spectrometer was exposed to radiation from D-D reactions and therefore spike noises appeared in the measured spectrum [4]. In addition, mechanical vibrations in the tokamak can bring about
⁎
miscalibration of wavelength in the detector of the VUV spectrometer. These can impose difficulties in clear interpretation of the impurity spectra, and thus, it is highly required to develop algorithms for reliable real-time automatic spectral line identification of impurities with reduced noise. Many data processing algorithms for such noise reduction have been developed in various fields, including electronics engineering, medical imaging, etc [5–10]. Especially, fusion engineering requires real-time data processing with high accuracy and speed for a quick response to a rapidly changing plasma state. In this work, an automatic spectral line identification algorithm for VUV spectroscopy with a low computational complexity was developed based on the high-order derivative method [11–13]. The developed code also includes an in-situ wavelength calibration function that minimizes a possible miscalibration due to mechanical vibration to the diagnostic setup. In the case of tungsten-walled devices, including ITER, because of the dense line transitions within the quasi-continuum range of high-Z impurity species, the in-situ wavelength calibration technique is inevitably necessary to obtain accurate spectral data by preventing miscalibration due to mechanical vibration. The developed algorithm
Corresponding author. E-mail address: [email protected] (W. Choe).
https://doi.org/10.1016/j.fusengdes.2020.111459 Received 9 September 2019; Received in revised form 6 January 2020; Accepted 7 January 2020 0920-3796/ © 2020 Published by Elsevier B.V.
Fusion Engineering and Design 153 (2020) 111459
H. Shin, et al.
was tested on spectral data of the ITER-relevant VUV spectrometer (29.0–60.0 nm) installed in KSTAR [2]. The expected neutron flux at the detector location for VUV-edge imaging spectrometer in ITER is approximately 5 × 105 n cm−2 s−1 [14]. In the case of KSTAR, the neutron yield is 1013–1014 n s−1 and thus, the expected neutron flux at the VUV spectrometer position is roughly 105–106 n cm−2 s−1 [15]. Therefore, it is reasonable to compare the VUV spectrometer in KSTAR with the one in ITER. Tests using synthetic emission spectra were performed to estimate the noise reduction capability of the developed algorithm quantitatively.
Fig. 2(a) is a time evolution of an emission spectrum, and Fig. 2(b) shows the processed spectrum using the fourth-order derivative. The advantage of the developed processing code is that the order of derivative can be chosen to minimize distortion of the line spectra. Since the spectral line shape measured by the spectrometer can vary depending on the device characteristics and plasma conditions, by selecting the appropriate derivative order, optimally processed spectral data can be obtained.
2. Algorithm for noise removal and automatic spectral line identification
Spectroscopic diagnostic systems are often prone to miscalibration caused by mechanical vibration in the tokamak. Therefore, an in-situ wavelength calibration should be conducted for reliable data acquisition with accurate spectral line identification. The code developed includes an in-situ wavelength calibration function using representative emission lines of impurities inside the plasma. First, the pixel index of the peak is listed using a function that detects the peak from the primarily noise-removed spectra in the aforementioned process. Second, the listed pixel indices, as markers, are matched with the expected wavelengths of emission lines by using the ratio of differences between each pixel index and emission line. The emission lines are referenced from the NIST atomic database, which is stored in the local PC in advance. Finally, by applying polynomial fitting on the wavelengths of the markers, more correct in-situ calibration of wavelength is obtained. The method was tested on the VUV spectrum for the argon puffing experiment at KSTAR (Shot #16950). As the diffracted radiation from the grating lies on the Rowland circle, third polynomial fitting is commonly used for wavelength calibration [18,19], which requires at least four emission lines. In this process, the five carbon emission lines of C IV - 31.242 nm, C IV - 38.403 nm, C III 38.620 nm, C IV - 41.971 nm, and C III - 45.963 nm are selected as markers that often appear during the current ramp-up phase in KSTAR. Fig. 3 and Table 1 show the carbon emission lines and the list of pixel indices matched with the corresponding carbon wavelengths, respectively, demonstrating successful wavelength calibration. Its reliability can be determined from the results of the line identification function, which is based on the calibrated wavelength. From the calibrated wavelength, the spectrum to be identified is compared with the listed emission lines based on the experimentally observed ones referenced from the NIST atomic database. If each wavelength of the emission line is within the error range, an annotation of an ionic state of an impurity is inserted on the line peak. Fig. 4 shows the ionic states of impurities annotated on each line spectrum, demonstrating that line identification is implemented successfully. The yellow peaks in the figure are random noises caused by neutron and gamma fluxes and are successfully removed by the developed noise reduction algorithm.
2.2. In-situ wavelength calibration
2.1. Noise removal Noises in the emission spectrum caused by low-energy particles or detector dark currents can be reduced by cooling the detector; however, there is a limitation in eliminating narrow spiky noises caused by neutrons, gamma photons, and neutral particles from neutral beam injection (NBI) interacting with the detector chip. These spikes are distributed randomly in both time and space. These spiky noises, which have the most common shape of noise, appear in a single pixel because the surface of the detector chip faces the direction in which most particles are injected. However, when the incidence angle is close to 90°, secondary particles from the detector surface have a higher probability to interact with multiple pixels and thus broad-shaped noises can appear in the spectra [16]. In this work, we adopt a high-order derivative method to distinguish noises with relatively narrow shapes from real spectral lines. The amplitude of the n-th order derivative is inversely proportional to the n-th power of the width of the peak shape. As the order of the derivative becomes higher, noises with narrow width become more distinctive. Therefore, when the higher-order derivative is applied to a spiky noise, the derivative value of the peak becomes dramatically larger as the order goes higher due to its steep gradient while that of the spectral line stays relatively smaller due to its gentle gradient. For the first process of noise removal, the absolute value of the higher-order derivative of a spectral line is multiplied by an appropriate correction factor to match with the amplitude of the noise in the spectra. The correction factor was calculated from the ratio of the noise amplitudes and the obtained absolute values of the derivative at the same spectral region. In the following step, the corrected derivative is subtracted from the original spectra and interpolation is applied to remove the abnormal negative values produced during the subtraction process. Finally, the noise-removed spectrum is obtained. The peak height of the spectrum can be slightly reduced through the process of subtracting derivative values from the original spectra, however, since such decrease in intensity is negligible as the reduction ratio is less than 1%, the intensity of the original line spectrum is preserved after the noise removal process. Although the spike noises are removed by this process, broadshaped noises may remain. These noises, except for possible candidates of line emissions, are selected for deletion, by removing their peak values, referencing from the NIST atomic database [17]. Adding this process makes the noise reduction more effective. These steps are simple, have low computational complexity, and are calculated for a single time frame, which is required for fast data acquisition in tokamak diagnostics. The emission spectra from KSTAR shot #16950 was used to evaluate the dependency on different orders of derivative. Fig. 1 shows the processed result comparing the second- and fourth-order derivative methods. Compared to the case of the second-order derivative, heights of peaks are better preserved without distortion and noises are more clearly removed in the case of the fourth-order derivative. For this reason, the processes adopted in this work are applied by the fourthorder derivative method (see more details in the following section).
3. Performance tests using synthetic spectra To evaluate the noise reduction ability of the developed code quantitatively, tests were performed using synthetic spectra. The synthetic spectra consist of several elements: firstly, because the line spectra have continuous distributions in time and space, these can be assumed as two-dimensional (2-D) Gaussian distributions with widths similar to the spectra obtained in the experiments. Secondly, the two types of spike noises randomly distributed to the entire detector array and relatively broad noise can be assumed as spikes with random amplitudes and 1-D Gaussian distributions, respectively. Spike noises with various amplitudes were created using a built-in function of Python language that generates random samples. Figs. 5(a), (b), and (c) show a 2-D Gaussian curve for line spectrum, 1-D Gaussian curve for broad noise, and spike for noises in 3-D images, respectively. Two types of noises and spectra with similar shape to the measured experimental data were inserted in 2048 × 500 arrays that can be assumed to be data 2
Fusion Engineering and Design 153 (2020) 111459
H. Shin, et al.
Fig. 1. Processed spectra using (a)–(b) the second-order derivative and (c)–(d) the fourthorder derivative of spectra taken at 1.05 s and 5.60 s for KSTAR shot #16950. Yellow, green, and blue curves denote the original, derivative, and processed spectra, respectively. Here, the absolute value of the derivative is indicated in negative sign (green) to avoid overlapping with the original value (yellow). In the processed spectra using the fourth-order derivative, noises are removed more clearly and with negligible spectral distortion compared to the case of the second-order derivative.
Fig. 2. Time evolution of the emission spectrum from KSTAR shot #16950: (a) the original spectrum and (b) the processed spectrum using the fourth-order derivative. Spike noises randomly distributed on the spectrum are significantly reduced after the removal process.
Table 1 List of pixel indices matched with carbon emission lines referenced from NIST. Carbon emission line
Pixel index
C C C C C
1872 1157 1137 812 432
IV IV III IV III
-
31.242 38.403 38.620 41.971 45.963
[nm] [nm] [nm] [nm] [nm]
becomes 14.6, showing 89.6% reduction. Simultaneously, the intensity of the line emission shows a negligible change of less than 1%, indicating that this process successfully preserves the spectral lines and only removes noises. Fig. 3. Representative carbon emission lines as markers that occur during the current ramp-up phase for carbon-walled devices (KSTAR shot #16950).
4. Summary
arrays obtained from the VUV CCD detector. Fig. 5(d) shows the completely fabricated synthetic spectra. In Fig. 5(e), the noise removal process is applied to the synthetic spectra arrays. The average value of the estimated noise (all peaks except Gaussian curves) in the synthetic spectra, referred to as the noise power, is 140.9 in arbitrary units. After the process, the noise power
Reliable spectral line identification of a VUV spectrum requires a careful processing technique because of the complex shape of the spectrum with large-amplitude noises caused by high energy particle such as neutrons and gamma photons from the tokamak plasma. Moreover, as the spectrometers in ITER and KSTAR are facing directly the NBI [20] that can produce high energy particles, there may occur 3
Fusion Engineering and Design 153 (2020) 111459
H. Shin, et al.
Fig. 4. Original (yellow line) and processed (blue line) VUV spectra with annotations of ionic states of impurities in KSTAR argon injection experiment (shot #16950): (a) spectra taken at 1.05 s and (b) spectra taken at 5.60 s. Line identification was performed appropriately.
Fig. 5. Synthetic spectrum elements and test result. (a) 2-D Gaussian profile for line spectrum, (b) 1-D Gaussian profile for broad noises, (c) spike for noises, (d) original synthetic spectrum, and (e) test result with significantly reduced noise.
Acknowledgments
severe noises on spectra. Therefore, accurate noise reduction becomes more important. A data processing algorithm based on the high-order derivative method was developed and validated using ITER-relevant VUV spectrometer experimental data from the KSTAR plasma. The detection of noise spikes was performed by high-order derivative, and noise removal was carried out by subtracting the noise from the original spectrum. Using in-situ wavelength calibration, spectral impurity line identification was performed successfully on the experimental data, and validation of the algorithm was demonstrated with a high noise reduction ratio of approximately 90% in a test using a synthetic spectrum. The algorithm is appropriate for the data processing required for a plasma diagnostic and control system in terms of efficiency because of its low complexity and accuracy. Further improvements and modifications for reliable data acquisition will contribute to the establishment of a real-time impurity monitoring and control system.
This work was supported by the National R&D Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (NRF-2014M1A7A1A03045092 and NRF2019M1A7A1A03087560) and also supported partly by the ITERproject contract (2007-2006997). References [1] W. Biel, et al., Diagnostics for plasma control – from ITER to DEMO, Fusion Eng. Des. 146 (2019) 465–472, https://doi.org/10.1016/j.fusengdes.2018.12.092. [2] C.R. Seon, et al., Test of prototype ITER vacuum ultraviolet spectrometer and its application to impurity study in KSTAR plasmas, Rev. Sci. Instrum. 85 (2014) 11E403, https://doi.org/10.1063/1.4886430. [3] Y. Liu, et al., Effect of neutron and gamma-ray on charge-coupled device for vacuum/extreme ultraviolet spectroscopy in deuterium discharges of large helical device, Rev. Sci. Instrum. 89 (10) (2018) 10I109, , https://doi.org/10.1063/1. 5037233. [4] P.E. Stott, Giuseppe Gorini, Elio Sindoni, Diagnostics for Experimental Thermonuclear Fusion Reactors, Plenum Press, New York and London, 1996, https://doi.org/10.1007/978-1-4613-0369-5. [5] B. Kurzan, et al., Signal processing of Thomson scattering data in a noisy environment in ASDEX Upgrade, Plasma Phys. Control. Fusion 46 (2004) 299–317,
Conflict of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 4
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[13] D. Yi, S. Lee, Fourth-order partial differential equations for image enhancement, Appl. Math. Comput. 175 (2006) 430–440, https://doi.org/10.1016/j.amc.2005. 07.043. [14] Y.H. An, et al., Radiation shielding design evaluation for ITER VUV edge imaging spectrometer, Fusion Eng. Des. 123 (2017) 787–792, https://doi.org/10.1016/j. fusengdes.2017.04.096. [15] J.-G. Kwak, et al., Accumulated 2-D neutron flux distribution during KSTAR operation, Fusion Eng. Des. 136 (2018) 777–781, https://doi.org/10.1016/j. fusengdes.2018.04.008. [16] J. Baggio, et al., Transient noise in a CCD camera sensor induced by neutron and gamma irradiation, 6th European Conference on Radiation and Its Effects on Components and Systems (2001) 186–191, https://doi.org/10.1109/RADECS. 2001.1159278. [17] https://www.nist.gov/pml/atomic-spectra-database. [18] G. Xiong, et al., Different approaches to precise wavelength calibration of a flat-field grating spectrometer for laser-produced plasmas, Phys. Scr. 89 (6) (2014), https:// doi.org/10.1088/0031-8949/89/6/065005. [19] P. Richard, Atomic Physics Accelerators, Methods in Experimental Physics, Academic Press, Elsevier Inc, 1980. [20] A.E. Costley et al., (IT/1-5) The design and implementation of diagnostic systems on ITER, Proc. 21st IAEA FEC, Chengdu (2006), IAEA-CN-149.
https://doi.org/10.1088/0741-3335/46/1/019. [6] L. Giannone, et al., Data acquisition and real-time signal processing of plasma diagnostics on ASDEX Upgrade using LabVIEW RT, Fusion Eng. Des. 85 (2010) 303–307, https://doi.org/10.1016/j.fusengdes.2010.03.030. [7] L. Marrelli, et al., Singular spectrum analysis as a tool for plasma fluctuations analysis, Rev. Sci. Instrum. 72 (2001) 499, https://doi.org/10.1063/1.1323250. [8] M. Katajamaa, M. Orešič, Data processing for mass spectrometry-based metabolomics, J. Chromatogr. A 1158 (1–2) (2007) 318–328, https://doi.org/10.1016/j. chroma.2007.04.021. [9] M.R. Hajiaboli, An anisotropic fourth-order diffusion filter for image noise removal, Int. J. Comput. Vis. 92 (2) (2011) 177–191, https://doi.org/10.1007/s11263-0100330-1. [10] H. Takeuchi, S. Hashimoto, I. Harada, Simple and efficient method to eliminate spike noise from spectra recorded on charge-coupled device detectors, Appl. Spectrosc. 47 (1) (1993) 129–131, https://doi.org/10.1366/0003702934048578. [11] Y.L. You, M. Kaveh, Fourth-order partial differential equations for noise removal, IEEE Trans. Image Process. 9 (10) (2000) 1723–1730, https://doi.org/10.1109/83. 869184. [12] M. Lysaker, A. Lundervold, X.-C. Xue-Cheng Tai, Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time, IEEE Trans. Image Process. 12 (12) (2003) 1579–1590, https://doi.org/10.1109/TIP.2003.819229.
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Contents lists available at ScienceDirect
Fusion Engineering and Design journal homepage: www.elsevier.com/locate/fusengdes
Automatic impurity spectral line identification algorithm with noise reduction for fusion plasmas
T
Haewon Shina,b, Inwoo Songb,c, YoungHwa And, Wonho Choea,b,* a
Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea Impurity and Edge Plasma Research Center, KAIST, Daejeon 34141, Republic of Korea c Department of Physics, KAIST, Daejeon 34141, Republic of Korea d National Fusion Research Institute, Daejeon 34133, Republic of Korea b
A R T I C LE I N FO
A B S T R A C T
Keywords: Real-time post processing VUV spectroscopy Noise removal Impurity spectral line identification
Accurate diagnostics of impurity behavior inside the tokamak plasma is essential for long and stable plasma operation and also for machine protection in fusion devices including ITER. In this study, a numerical code for identifying impurity line spectra was developed and assessed by utilizing the ITER-relevant vacuum ultraviolet (VUV) spectroscopic diagnostic system in KSTAR. For real-time analysis of a spectrum, it is necessary to reduce the noise level of the data caused by high neutron and gamma photon fluxes reaching the detector such as a charge-coupled device detector. The code uses a high-order derivative method that distinguishes relatively sharp noises from spectral lines. Tests with synthetic spectra showed successful noise reduction of approximately 90%. In addition, an in-situ wavelength calibration algorithm was developed by using representative carbon emission lines (C III and C IV) as markers that appear during the current ramp-up phase in carbon-walled devices. This algorithm is followed by a matching algorithm that enables the annotation of an ionic state of an impurity on the line peak by referencing the NIST atomic database. From the integrated processing algorithm, simulated VUV spectra were tested and the results showed successful automatic annotation of the spectral lines after noise reduction.
1. Introduction Accurate diagnostics of impurities inside the fusion plasma and providing key parameters regarding the impurities are essential not only for stable plasma operation but also for tokamak machine protection [1]. For these purposes, the vacuum ultraviolet (VUV) spectrometry has been actively utilized in tokamak plasma research for decades since tungsten, as the divertor material for ITER, mostly emits VUV and extreme UV (EUV) line radiation once it is inside the plasma. It is planned that ITER VUV spectrometer will adopt a charge-coupled device (CCD) camera for its several advantages [2]. However, in future fusion devices including ITER, high-energy particle fluxes such as neutron and gamma photon fluxes will be much higher than in current tokamaks running with D–D reactions and will result in randomly distributed spiky noises in the spectral data, whose amplitude may become even larger than the real atomic spectra [3]. In the case of JET, the SPRED VUV spectrometer was exposed to radiation from D-D reactions and therefore spike noises appeared in the measured spectrum [4]. In addition, mechanical vibrations in the tokamak can bring about
⁎
miscalibration of wavelength in the detector of the VUV spectrometer. These can impose difficulties in clear interpretation of the impurity spectra, and thus, it is highly required to develop algorithms for reliable real-time automatic spectral line identification of impurities with reduced noise. Many data processing algorithms for such noise reduction have been developed in various fields, including electronics engineering, medical imaging, etc [5–10]. Especially, fusion engineering requires real-time data processing with high accuracy and speed for a quick response to a rapidly changing plasma state. In this work, an automatic spectral line identification algorithm for VUV spectroscopy with a low computational complexity was developed based on the high-order derivative method [11–13]. The developed code also includes an in-situ wavelength calibration function that minimizes a possible miscalibration due to mechanical vibration to the diagnostic setup. In the case of tungsten-walled devices, including ITER, because of the dense line transitions within the quasi-continuum range of high-Z impurity species, the in-situ wavelength calibration technique is inevitably necessary to obtain accurate spectral data by preventing miscalibration due to mechanical vibration. The developed algorithm
Corresponding author. E-mail address: [email protected] (W. Choe).
https://doi.org/10.1016/j.fusengdes.2020.111459 Received 9 September 2019; Received in revised form 6 January 2020; Accepted 7 January 2020 0920-3796/ © 2020 Published by Elsevier B.V.
Fusion Engineering and Design 153 (2020) 111459
H. Shin, et al.
was tested on spectral data of the ITER-relevant VUV spectrometer (29.0–60.0 nm) installed in KSTAR [2]. The expected neutron flux at the detector location for VUV-edge imaging spectrometer in ITER is approximately 5 × 105 n cm−2 s−1 [14]. In the case of KSTAR, the neutron yield is 1013–1014 n s−1 and thus, the expected neutron flux at the VUV spectrometer position is roughly 105–106 n cm−2 s−1 [15]. Therefore, it is reasonable to compare the VUV spectrometer in KSTAR with the one in ITER. Tests using synthetic emission spectra were performed to estimate the noise reduction capability of the developed algorithm quantitatively.
Fig. 2(a) is a time evolution of an emission spectrum, and Fig. 2(b) shows the processed spectrum using the fourth-order derivative. The advantage of the developed processing code is that the order of derivative can be chosen to minimize distortion of the line spectra. Since the spectral line shape measured by the spectrometer can vary depending on the device characteristics and plasma conditions, by selecting the appropriate derivative order, optimally processed spectral data can be obtained.
2. Algorithm for noise removal and automatic spectral line identification
Spectroscopic diagnostic systems are often prone to miscalibration caused by mechanical vibration in the tokamak. Therefore, an in-situ wavelength calibration should be conducted for reliable data acquisition with accurate spectral line identification. The code developed includes an in-situ wavelength calibration function using representative emission lines of impurities inside the plasma. First, the pixel index of the peak is listed using a function that detects the peak from the primarily noise-removed spectra in the aforementioned process. Second, the listed pixel indices, as markers, are matched with the expected wavelengths of emission lines by using the ratio of differences between each pixel index and emission line. The emission lines are referenced from the NIST atomic database, which is stored in the local PC in advance. Finally, by applying polynomial fitting on the wavelengths of the markers, more correct in-situ calibration of wavelength is obtained. The method was tested on the VUV spectrum for the argon puffing experiment at KSTAR (Shot #16950). As the diffracted radiation from the grating lies on the Rowland circle, third polynomial fitting is commonly used for wavelength calibration [18,19], which requires at least four emission lines. In this process, the five carbon emission lines of C IV - 31.242 nm, C IV - 38.403 nm, C III 38.620 nm, C IV - 41.971 nm, and C III - 45.963 nm are selected as markers that often appear during the current ramp-up phase in KSTAR. Fig. 3 and Table 1 show the carbon emission lines and the list of pixel indices matched with the corresponding carbon wavelengths, respectively, demonstrating successful wavelength calibration. Its reliability can be determined from the results of the line identification function, which is based on the calibrated wavelength. From the calibrated wavelength, the spectrum to be identified is compared with the listed emission lines based on the experimentally observed ones referenced from the NIST atomic database. If each wavelength of the emission line is within the error range, an annotation of an ionic state of an impurity is inserted on the line peak. Fig. 4 shows the ionic states of impurities annotated on each line spectrum, demonstrating that line identification is implemented successfully. The yellow peaks in the figure are random noises caused by neutron and gamma fluxes and are successfully removed by the developed noise reduction algorithm.
2.2. In-situ wavelength calibration
2.1. Noise removal Noises in the emission spectrum caused by low-energy particles or detector dark currents can be reduced by cooling the detector; however, there is a limitation in eliminating narrow spiky noises caused by neutrons, gamma photons, and neutral particles from neutral beam injection (NBI) interacting with the detector chip. These spikes are distributed randomly in both time and space. These spiky noises, which have the most common shape of noise, appear in a single pixel because the surface of the detector chip faces the direction in which most particles are injected. However, when the incidence angle is close to 90°, secondary particles from the detector surface have a higher probability to interact with multiple pixels and thus broad-shaped noises can appear in the spectra [16]. In this work, we adopt a high-order derivative method to distinguish noises with relatively narrow shapes from real spectral lines. The amplitude of the n-th order derivative is inversely proportional to the n-th power of the width of the peak shape. As the order of the derivative becomes higher, noises with narrow width become more distinctive. Therefore, when the higher-order derivative is applied to a spiky noise, the derivative value of the peak becomes dramatically larger as the order goes higher due to its steep gradient while that of the spectral line stays relatively smaller due to its gentle gradient. For the first process of noise removal, the absolute value of the higher-order derivative of a spectral line is multiplied by an appropriate correction factor to match with the amplitude of the noise in the spectra. The correction factor was calculated from the ratio of the noise amplitudes and the obtained absolute values of the derivative at the same spectral region. In the following step, the corrected derivative is subtracted from the original spectra and interpolation is applied to remove the abnormal negative values produced during the subtraction process. Finally, the noise-removed spectrum is obtained. The peak height of the spectrum can be slightly reduced through the process of subtracting derivative values from the original spectra, however, since such decrease in intensity is negligible as the reduction ratio is less than 1%, the intensity of the original line spectrum is preserved after the noise removal process. Although the spike noises are removed by this process, broadshaped noises may remain. These noises, except for possible candidates of line emissions, are selected for deletion, by removing their peak values, referencing from the NIST atomic database [17]. Adding this process makes the noise reduction more effective. These steps are simple, have low computational complexity, and are calculated for a single time frame, which is required for fast data acquisition in tokamak diagnostics. The emission spectra from KSTAR shot #16950 was used to evaluate the dependency on different orders of derivative. Fig. 1 shows the processed result comparing the second- and fourth-order derivative methods. Compared to the case of the second-order derivative, heights of peaks are better preserved without distortion and noises are more clearly removed in the case of the fourth-order derivative. For this reason, the processes adopted in this work are applied by the fourthorder derivative method (see more details in the following section).
3. Performance tests using synthetic spectra To evaluate the noise reduction ability of the developed code quantitatively, tests were performed using synthetic spectra. The synthetic spectra consist of several elements: firstly, because the line spectra have continuous distributions in time and space, these can be assumed as two-dimensional (2-D) Gaussian distributions with widths similar to the spectra obtained in the experiments. Secondly, the two types of spike noises randomly distributed to the entire detector array and relatively broad noise can be assumed as spikes with random amplitudes and 1-D Gaussian distributions, respectively. Spike noises with various amplitudes were created using a built-in function of Python language that generates random samples. Figs. 5(a), (b), and (c) show a 2-D Gaussian curve for line spectrum, 1-D Gaussian curve for broad noise, and spike for noises in 3-D images, respectively. Two types of noises and spectra with similar shape to the measured experimental data were inserted in 2048 × 500 arrays that can be assumed to be data 2
Fusion Engineering and Design 153 (2020) 111459
H. Shin, et al.
Fig. 1. Processed spectra using (a)–(b) the second-order derivative and (c)–(d) the fourthorder derivative of spectra taken at 1.05 s and 5.60 s for KSTAR shot #16950. Yellow, green, and blue curves denote the original, derivative, and processed spectra, respectively. Here, the absolute value of the derivative is indicated in negative sign (green) to avoid overlapping with the original value (yellow). In the processed spectra using the fourth-order derivative, noises are removed more clearly and with negligible spectral distortion compared to the case of the second-order derivative.
Fig. 2. Time evolution of the emission spectrum from KSTAR shot #16950: (a) the original spectrum and (b) the processed spectrum using the fourth-order derivative. Spike noises randomly distributed on the spectrum are significantly reduced after the removal process.
Table 1 List of pixel indices matched with carbon emission lines referenced from NIST. Carbon emission line
Pixel index
C C C C C
1872 1157 1137 812 432
IV IV III IV III
-
31.242 38.403 38.620 41.971 45.963
[nm] [nm] [nm] [nm] [nm]
becomes 14.6, showing 89.6% reduction. Simultaneously, the intensity of the line emission shows a negligible change of less than 1%, indicating that this process successfully preserves the spectral lines and only removes noises. Fig. 3. Representative carbon emission lines as markers that occur during the current ramp-up phase for carbon-walled devices (KSTAR shot #16950).
4. Summary
arrays obtained from the VUV CCD detector. Fig. 5(d) shows the completely fabricated synthetic spectra. In Fig. 5(e), the noise removal process is applied to the synthetic spectra arrays. The average value of the estimated noise (all peaks except Gaussian curves) in the synthetic spectra, referred to as the noise power, is 140.9 in arbitrary units. After the process, the noise power
Reliable spectral line identification of a VUV spectrum requires a careful processing technique because of the complex shape of the spectrum with large-amplitude noises caused by high energy particle such as neutrons and gamma photons from the tokamak plasma. Moreover, as the spectrometers in ITER and KSTAR are facing directly the NBI [20] that can produce high energy particles, there may occur 3
Fusion Engineering and Design 153 (2020) 111459
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Fig. 4. Original (yellow line) and processed (blue line) VUV spectra with annotations of ionic states of impurities in KSTAR argon injection experiment (shot #16950): (a) spectra taken at 1.05 s and (b) spectra taken at 5.60 s. Line identification was performed appropriately.
Fig. 5. Synthetic spectrum elements and test result. (a) 2-D Gaussian profile for line spectrum, (b) 1-D Gaussian profile for broad noises, (c) spike for noises, (d) original synthetic spectrum, and (e) test result with significantly reduced noise.
Acknowledgments
severe noises on spectra. Therefore, accurate noise reduction becomes more important. A data processing algorithm based on the high-order derivative method was developed and validated using ITER-relevant VUV spectrometer experimental data from the KSTAR plasma. The detection of noise spikes was performed by high-order derivative, and noise removal was carried out by subtracting the noise from the original spectrum. Using in-situ wavelength calibration, spectral impurity line identification was performed successfully on the experimental data, and validation of the algorithm was demonstrated with a high noise reduction ratio of approximately 90% in a test using a synthetic spectrum. The algorithm is appropriate for the data processing required for a plasma diagnostic and control system in terms of efficiency because of its low complexity and accuracy. Further improvements and modifications for reliable data acquisition will contribute to the establishment of a real-time impurity monitoring and control system.
This work was supported by the National R&D Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (NRF-2014M1A7A1A03045092 and NRF2019M1A7A1A03087560) and also supported partly by the ITERproject contract (2007-2006997). References [1] W. Biel, et al., Diagnostics for plasma control – from ITER to DEMO, Fusion Eng. Des. 146 (2019) 465–472, https://doi.org/10.1016/j.fusengdes.2018.12.092. [2] C.R. Seon, et al., Test of prototype ITER vacuum ultraviolet spectrometer and its application to impurity study in KSTAR plasmas, Rev. Sci. Instrum. 85 (2014) 11E403, https://doi.org/10.1063/1.4886430. [3] Y. Liu, et al., Effect of neutron and gamma-ray on charge-coupled device for vacuum/extreme ultraviolet spectroscopy in deuterium discharges of large helical device, Rev. Sci. Instrum. 89 (10) (2018) 10I109, , https://doi.org/10.1063/1. 5037233. [4] P.E. Stott, Giuseppe Gorini, Elio Sindoni, Diagnostics for Experimental Thermonuclear Fusion Reactors, Plenum Press, New York and London, 1996, https://doi.org/10.1007/978-1-4613-0369-5. [5] B. Kurzan, et al., Signal processing of Thomson scattering data in a noisy environment in ASDEX Upgrade, Plasma Phys. Control. Fusion 46 (2004) 299–317,
Conflict of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 4
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