adsabs/AstroCLIMB huggingface dataset:
Paper: The CLASS Quasar Catalog: Coronal Line Activity in Type 1 SDSS Quasars
Authors: Sara Doan, Shobita Satyapal, Michael Reefe, Remington O. Sexton, William Matzko, Jeffrey D. McKaig, Nathan J. Secrest, Jenna M. Cann, Ari Laor, Gabriela Canalizo
Figure caption: Histograms of detection fractions for the various CLs indicated as a function of Eddington ratio. Number counts are listed for each bin, and error bars are calculated using binomial statistics using a 68% confidence interval.
Paper: The CLASS Quasar Catalog: Coronal Line Activity in Type 1 SDSS Quasars
Authors: Sara Doan, Shobita Satyapal, Michael Reefe, Remington O. Sexton, William Matzko, Jeffrey D. McKaig, Nathan J. Secrest, Jenna M. Cann, Ari Laor, Gabriela Canalizo
Figure caption: [Ne V] λ3426 line luminosity vs. the bolometric luminosity for all the detections in the sample, with the best-fit power law displayed. The fit parameters and Spearman rank correlation coefficients are shown in the upper left corner.
Paper: The CLASS Quasar Catalog: Coronal Line Activity in Type 1 SDSS Quasars
Authors: Sara Doan, Shobita Satyapal, Michael Reefe, Remington O. Sexton, William Matzko, Jeffrey D. McKaig, Nathan J. Secrest, Jenna M. Cann, Ari Laor, Gabriela Canalizo
Figure caption: A 2D correlation plot between each pair of lines. The color of each box represents the number of spectra in which both lines from the horizontal and vertical labels are found together. Along the diagonal, the color instead represents the pure number of detections of that single line. The color bar is logarithmic, corresponding to ﹩{{\mathrm{log}}_{10}}\,(N+1)﹩ where N is the number of detections.
Paper: Discrete Fourier Transform and Minimum Variance Distortionless Response Beamforming Algorithms for Searching for Transients in Radio Interferometers that Experience Radio-frequency Interference
Authors: Antonios Argyriou, Shion Andrew
Figure caption: Results for the proposed algorithm. (a) Here we show the SNR gain relative to pure FFT beamforming as a function of the fraction of pulse contaminated by RFI over the band under three methods: removal of RFI-inflicted channels, proposed DFT and MVDR spatial filtering, and interference estimation and successive interference cancelation (SIC) on top of the last method. (b) MSE vs. SNR for the proposed method with different fractions of contaminated RFI channels, and different processing methods of the RFI.
Paper: Quasiperiodic Variations of the GOES Soft X-Ray Flares during Solar Cycles 21–25
Authors: Guoqing Huang, Guoding Duan, Miao Wan, Xinhua Zhao, Tingting Xu, Linhua Deng, Jie Cao, Wenhua Wang, Weihong Zhou
Figure caption: The daily occurrence rates of different classes of SXR flares (B, C, M, and X) from 1976 March to 2024 November, with daily count represented on the left vertical axis, corresponding to the scatter plot, and the monthly averaged flare count on the right vertical axis, corresponding to the curve, which represents the temporal variations of monthly averaged flare count for different classes of SXR flares.
Paper: Quasiperiodic Variations of the GOES Soft X-Ray Flares during Solar Cycles 21–25
Authors: Guoqing Huang, Guoding Duan, Miao Wan, Xinhua Zhao, Tingting Xu, Linhua Deng, Jie Cao, Wenhua Wang, Weihong Zhou
Figure caption: The local wavelet spectra (left column) and global wavelet spectra (right column) for different classes of daily SXR flares (B, C, M, and X) in the whole disk. The red dashed line in the local wavelet spectra represents the period of 155 days, while in the global wavelet spectra, the red dashed lines mark the 95% confidence level.
Paper: Quasiperiodic Variations of the GOES Soft X-Ray Flares during Solar Cycles 21–25
Authors: Guoqing Huang, Guoding Duan, Miao Wan, Xinhua Zhao, Tingting Xu, Linhua Deng, Jie Cao, Wenhua Wang, Weihong Zhou
Figure caption: Same as Figure 2, but in the southern hemisphere.
Paper: Quasiperiodic Variations of the GOES Soft X-Ray Flares during Solar Cycles 21–25
Authors: Guoqing Huang, Guoding Duan, Miao Wan, Xinhua Zhao, Tingting Xu, Linhua Deng, Jie Cao, Wenhua Wang, Weihong Zhou
Figure caption: Same as Figure 2, but in the northern hemisphere.
Paper: Quasiperiodic Variations of the GOES Soft X-Ray Flares during Solar Cycles 21–25
Authors: Guoqing Huang, Guoding Duan, Miao Wan, Xinhua Zhao, Tingting Xu, Linhua Deng, Jie Cao, Wenhua Wang, Weihong Zhou
Figure caption: The local wavelet spectra of B-class, C-class, M-class, and X-class daily SXR flares from SC21 to SC25. From top to bottom, the first/second/third/fourth/fifth rows represent SC21/SC22/SC23/SC24/SC25, respectively. The red dashed line represents the period of 155 days.
Paper: Quasiperiodic Variations of the GOES Soft X-Ray Flares during Solar Cycles 21–25
Authors: Guoqing Huang, Guoding Duan, Miao Wan, Xinhua Zhao, Tingting Xu, Linhua Deng, Jie Cao, Wenhua Wang, Weihong Zhou
Figure caption: Same as Figure 5, but in the southern hemisphere.
Paper: Quasiperiodic Variations of the GOES Soft X-Ray Flares during Solar Cycles 21–25
Authors: Guoqing Huang, Guoding Duan, Miao Wan, Xinhua Zhao, Tingting Xu, Linhua Deng, Jie Cao, Wenhua Wang, Weihong Zhou
Figure caption: Same as Figure 5, but in the northern hemisphere.
Paper: Prediction of Intensity Variations Associated with Emerging Active Regions using Helioseismic Power Maps and Machine Learning
Authors: Spiridon Kasapis, Irina N. Kitiashvili, Alexander G. Kosovichev, John T. Stefan
Figure caption: Prediction of the Ic variations for selected tiles in the vicinity of AR 13179 (tiles 38–44) from Model 8. The tiles’ locations are marked in red squares at the bottom right of the continuum intensity images. Each panel includes three plots (from top to bottom): predicted (blue curves) and observed (orange curves) time evolution of the continuum intensity in the corresponding tile and the time derivative of observed and predicted continuum intensity. The gray dashed line indicates the corresponding normalized unsigned magnetic field value, which during the time of the first warning does not exceed 5% of the maximum magnetic flux observed for AR 13179. The time derivatives of the continuum intensity are based on the emergence criteria (Equation (1)), where red are the emergence periods, while in green are the nonemerging (quiet) states. Vertical lines show NOAA’s first record of the AR (magenta), the time 2 days after NOAA’s first record (purple), the time when Model 8 produces its first emergence alarm (First Warning, black), and the start time of the observed emergence (Emergence Start, red). In the panels, the solid vertical lines correspond to tile 41 where emergence started, and the dashed vertical lines are given for comparison to other tiles.
Paper: Prediction of Intensity Variations Associated with Emerging Active Regions using Helioseismic Power Maps and Machine Learning
Authors: Spiridon Kasapis, Irina N. Kitiashvili, Alexander G. Kosovichev, John T. Stefan
Figure caption: Prediction of the Ic variations for the selected tiles in vicinity of AR 11698 (tiles 47–53) from Model 8. The tiles’ locations are marked in red squares at the bottom right of the continuum intensity images. Each panel includes three plots (from top to bottom): predicted (blue curves) and observed (orange curves) time evolution of the continuum intensity in the corresponding tile and the time derivative of observed and predicted continuum intensity. The gray dashed line indicates the corresponding normalized unsigned magnetic field value, which during the time of the first warning does not exceed 5.2% of the maximum magnetic flux observed for AR 11698. The time derivatives of the continuum intensity (observed and predicted) are based on the emergence criteria (Equation (1)), where red are the emergence periods, while in green are the nonemerging (quiet) states. Vertical lines show NOAA’s first record of the active region (magenta), the time 2 days after NOAA’s first record (purple), the time when Model 8 produces its first emergence alarm (First Warning, black), and the start time of the observed emergence (Emergence Start, red). In the panels, the solid vertical lines correspond to tile 41 where emergence started, and the dashed vertical lines are given for comparison to other tiles.
Paper: Prediction of Intensity Variations Associated with Emerging Active Regions using Helioseismic Power Maps and Machine Learning
Authors: Spiridon Kasapis, Irina N. Kitiashvili, Alexander G. Kosovichev, John T. Stefan
Figure caption: Examples of ILAP near AR 11698 (left) and AR 13179 (right). From top to bottom, the panels show comparison of the continuum intensity from observations (orange curves) and predictions for Model 1 (blue curves), Model 2 (green), and Model 8 (red curves); the intensity derivatives dPred/dt, for Models 1, 2, and 8; the variations of mean acoustic power for four frequency ranges and unsigned magnetic flux (black dashed curves); and continuum intensity images power maps for the frequency range of 2–3 mHz. Time variations of the predicted intensity derivative are in red if the emergence criterion is satisfied. The vertical dashed lines in the upper panels correspond to images at the bottom, corresponding to about 2 days after emergence. The selected tiles are marked as red squares.
Paper: Prediction of Intensity Variations Associated with Emerging Active Regions using Helioseismic Power Maps and Machine Learning
Authors: Spiridon Kasapis, Irina N. Kitiashvili, Alexander G. Kosovichev, John T. Stefan
Figure caption: Dependence of RMSE from selected hyperparameters. Each panel shows the RMSE as a function of the number of input time points of observations (a), the number of layers (b), units (c), epochs (d), learning rate (e), and the prediction window output number (f). The green curves show the mean RMSE for the tiles in which emergence has been observed, and the red curves are for the nonemerging tiles. The red and green shadows represent the standard deviation. All models were evaluated for the 12 hr prediction problem. The hyperparameters marked in bold correspond to Model 8.
Paper: Prediction of Intensity Variations Associated with Emerging Active Regions using Helioseismic Power Maps and Machine Learning
Authors: Spiridon Kasapis, Irina N. Kitiashvili, Alexander G. Kosovichev, John T. Stefan
Figure caption: The same as in Figure 7 for the selected tiles in the vicinity of AR 13183 (tiles 38–44). The gray dashed line indicates the corresponding normalized unsigned magnetic field value, which during the time of the first warning does not exceed 9.6% of the maximum magnetic flux observed for AR 13183.
Paper: Prediction of Intensity Variations Associated with Emerging Active Regions using Helioseismic Power Maps and Machine Learning
Authors: Spiridon Kasapis, Irina N. Kitiashvili, Alexander G. Kosovichev, John T. Stefan
Figure caption: Summary table of magnetic activity prediction for independently analyzed tiles and continuum intensity prediction results for test ARs. The tested tiles are categorized into quiet and magnetically active. Tiles where the emergence of the active region is observed are marked with bold font. The alarms that signal the emergence of an AR (first alarm within AR) are also marked in bold. The two incorrect local activity predictions (ILAP) and the delayed activity alarms are in red. In orange are the activity and emergence alarms that in an operational setting would be delayed (≤7 hr), while in green are the correct alarms.
Paper: Prediction of Intensity Variations Associated with Emerging Active Regions using Helioseismic Power Maps and Machine Learning
Authors: Spiridon Kasapis, Irina N. Kitiashvili, Alexander G. Kosovichev, John T. Stefan
Figure caption: The same as in Figure 7 for the selected tiles in the vicinity of AR 11726 (tiles 38–44). The gray dashed line indicates the corresponding normalized unsigned magnetic field value, which during the time of the first warning does not exceed 9.2% of the maximum magnetic flux observed for AR 11726.
Paper: Prediction of Intensity Variations Associated with Emerging Active Regions using Helioseismic Power Maps and Machine Learning
Authors: Spiridon Kasapis, Irina N. Kitiashvili, Alexander G. Kosovichev, John T. Stefan
Figure caption: The same as Figure 7 for the selected tiles in the vicinity of AR 13165 (tiles 29–35). The gray dashed line indicates the corresponding normalized unsigned magnetic field value, which during the time of the first warning does not exceed 4% of the maximum magnetic flux observed for AR 13165.
Paper: The ALMA-QUARKS Survey. III. Clump-to-core Fragmentation and Searches for High-mass Starless Cores
Authors: Dongting Yang, Hong-Li Liu, Tie Liu, Xunchuan Liu, Fengwei Xu, Sheng-Li Qin, Anandmayee Tej, Guido Garay, Lei Zhu, Xiaofeng Mai, Wenyu Jiao, Siju Zhang, Sami Dib, Amelia M. Stutz, Aina Palau, Patricio Sanhueza, Annie Zavagno, A. Y. Yang, Xindi Tang, Mengyao Tang, Yichen Zhang, Pablo García, Tianwei Zhang, Anindya Saha, Shanghuo Li, Paul F. Goldsmith, Leonardo Bronfman, Chang Won Lee, Kotomi Taniguchi, Swagat Ranjan Das, Prasanta Gorai, Ariful Hoque, Li Chen, Zhiping Kou, Jianjun Zhou, Yankun Zhang, L. Viktor Tóth, Tapas Baug, Xianjin Shen, Chuanshou Li, Jiahang Zou, Ankan Das, Hafiz Nazeer, L. K. Dewangan, Jihye Hwang, James O. Chibueze
Figure caption: Distribution of the median linear core separation within each clump against the source distance. The Spearman’s rank correlation coefficient (ρs = 0.9) and the probability value (p ≪ 0.01) of the distribution are shown on the top. The upper x-axis also displays the spatial resolution at the corresponding distance.
Paper: The ALMA-QUARKS Survey. III. Clump-to-core Fragmentation and Searches for High-mass Starless Cores
Authors: Dongting Yang, Hong-Li Liu, Tie Liu, Xunchuan Liu, Fengwei Xu, Sheng-Li Qin, Anandmayee Tej, Guido Garay, Lei Zhu, Xiaofeng Mai, Wenyu Jiao, Siju Zhang, Sami Dib, Amelia M. Stutz, Aina Palau, Patricio Sanhueza, Annie Zavagno, A. Y. Yang, Xindi Tang, Mengyao Tang, Yichen Zhang, Pablo García, Tianwei Zhang, Anindya Saha, Shanghuo Li, Paul F. Goldsmith, Leonardo Bronfman, Chang Won Lee, Kotomi Taniguchi, Swagat Ranjan Das, Prasanta Gorai, Ariful Hoque, Li Chen, Zhiping Kou, Jianjun Zhou, Yankun Zhang, L. Viktor Tóth, Tapas Baug, Xianjin Shen, Chuanshou Li, Jiahang Zou, Ankan Das, Hafiz Nazeer, L. K. Dewangan, Jihye Hwang, James O. Chibueze
Figure caption: Distribution of the ratio between the observed core separation to the predicted thermal Jeans length. The black dashed line shows the peak of the distribution. The red contour represents the distribution with the spatial projection correction applied (see text).
Paper: The ALMA-QUARKS Survey. III. Clump-to-core Fragmentation and Searches for High-mass Starless Cores
Authors: Dongting Yang, Hong-Li Liu, Tie Liu, Xunchuan Liu, Fengwei Xu, Sheng-Li Qin, Anandmayee Tej, Guido Garay, Lei Zhu, Xiaofeng Mai, Wenyu Jiao, Siju Zhang, Sami Dib, Amelia M. Stutz, Aina Palau, Patricio Sanhueza, Annie Zavagno, A. Y. Yang, Xindi Tang, Mengyao Tang, Yichen Zhang, Pablo García, Tianwei Zhang, Anindya Saha, Shanghuo Li, Paul F. Goldsmith, Leonardo Bronfman, Chang Won Lee, Kotomi Taniguchi, Swagat Ranjan Das, Prasanta Gorai, Ariful Hoque, Li Chen, Zhiping Kou, Jianjun Zhou, Yankun Zhang, L. Viktor Tóth, Tapas Baug, Xianjin Shen, Chuanshou Li, Jiahang Zou, Ankan Das, Hafiz Nazeer, L. K. Dewangan, Jihye Hwang, James O. Chibueze
Figure caption: CO molecular outflow overlaid on the 1.3 mm dust continuum map of two protocluster clumps containing high-mass starless core candidates. The velocity offset is set around VLSR  ±  10–30 km s−1. The red and blue contours start at 3 rms (∼0.2 mJy beam−1 km s−1) and follow as [6, 12, 24, 48] rms. High-mass starless core candidates in this study are shown as green circles. ALMA-QUARKS TM2+ACA 1.3 mm dust continuum contour levels are [3, 6, 12, 24, 48] rms, with an rms of ∼0.6 mJy beam−1.
Paper: The ALMA-QUARKS Survey. III. Clump-to-core Fragmentation and Searches for High-mass Starless Cores
Authors: Dongting Yang, Hong-Li Liu, Tie Liu, Xunchuan Liu, Fengwei Xu, Sheng-Li Qin, Anandmayee Tej, Guido Garay, Lei Zhu, Xiaofeng Mai, Wenyu Jiao, Siju Zhang, Sami Dib, Amelia M. Stutz, Aina Palau, Patricio Sanhueza, Annie Zavagno, A. Y. Yang, Xindi Tang, Mengyao Tang, Yichen Zhang, Pablo García, Tianwei Zhang, Anindya Saha, Shanghuo Li, Paul F. Goldsmith, Leonardo Bronfman, Chang Won Lee, Kotomi Taniguchi, Swagat Ranjan Das, Prasanta Gorai, Ariful Hoque, Li Chen, Zhiping Kou, Jianjun Zhou, Yankun Zhang, L. Viktor Tóth, Tapas Baug, Xianjin Shen, Chuanshou Li, Jiahang Zou, Ankan Das, Hafiz Nazeer, L. K. Dewangan, Jihye Hwang, James O. Chibueze
Figure caption: (a) Distribution of the core mass against the source distance. The red circle symbols display the minimum core mass detected within each clump. The black dashed curve fits the lower envelope of the minimum core masses as a function of the distance. (b) Distribution of the surface density of the core count within each clump against the source distance. The top of both panels shows the Spearman’s rank correlation coefficient (ρs) and the probability value (p) of the distribution. The upper x-axis of both panels also displays the spatial resolution at the corresponding distance.
Paper: The ALMA-QUARKS Survey. III. Clump-to-core Fragmentation and Searches for High-mass Starless Cores
Authors: Dongting Yang, Hong-Li Liu, Tie Liu, Xunchuan Liu, Fengwei Xu, Sheng-Li Qin, Anandmayee Tej, Guido Garay, Lei Zhu, Xiaofeng Mai, Wenyu Jiao, Siju Zhang, Sami Dib, Amelia M. Stutz, Aina Palau, Patricio Sanhueza, Annie Zavagno, A. Y. Yang, Xindi Tang, Mengyao Tang, Yichen Zhang, Pablo García, Tianwei Zhang, Anindya Saha, Shanghuo Li, Paul F. Goldsmith, Leonardo Bronfman, Chang Won Lee, Kotomi Taniguchi, Swagat Ranjan Das, Prasanta Gorai, Ariful Hoque, Li Chen, Zhiping Kou, Jianjun Zhou, Yankun Zhang, L. Viktor Tóth, Tapas Baug, Xianjin Shen, Chuanshou Li, Jiahang Zou, Ankan Das, Hafiz Nazeer, L. K. Dewangan, Jihye Hwang, James O. Chibueze
Figure caption: Core-averaged spectra of two high-mass starless core candidates from four QUARKS SPWs. Note that the zoom-in of the CO line spectrum in the bottom panel presents many artificial absorption dips caused by missing flux of extended gas structures, which, however, do not affect our classification of candidate starless cores. These artifacts can be addressed in the future by combing our QUARKS and single-dish observations.
Paper: The ALMA-QUARKS Survey. III. Clump-to-core Fragmentation and Searches for High-mass Starless Cores
Authors: Dongting Yang, Hong-Li Liu, Tie Liu, Xunchuan Liu, Fengwei Xu, Sheng-Li Qin, Anandmayee Tej, Guido Garay, Lei Zhu, Xiaofeng Mai, Wenyu Jiao, Siju Zhang, Sami Dib, Amelia M. Stutz, Aina Palau, Patricio Sanhueza, Annie Zavagno, A. Y. Yang, Xindi Tang, Mengyao Tang, Yichen Zhang, Pablo García, Tianwei Zhang, Anindya Saha, Shanghuo Li, Paul F. Goldsmith, Leonardo Bronfman, Chang Won Lee, Kotomi Taniguchi, Swagat Ranjan Das, Prasanta Gorai, Ariful Hoque, Li Chen, Zhiping Kou, Jianjun Zhou, Yankun Zhang, L. Viktor Tóth, Tapas Baug, Xianjin Shen, Chuanshou Li, Jiahang Zou, Ankan Das, Hafiz Nazeer, L. K. Dewangan, Jihye Hwang, James O. Chibueze
Figure caption: Venn diagram of crossmatched ATOMS “unknown” sources with QUARKS TM2+ACA cores at different evolutionary stages. The number in each circle represents the crossmatched result.
Paper: The ALMA-QUARKS Survey. III. Clump-to-core Fragmentation and Searches for High-mass Starless Cores
Authors: Dongting Yang, Hong-Li Liu, Tie Liu, Xunchuan Liu, Fengwei Xu, Sheng-Li Qin, Anandmayee Tej, Guido Garay, Lei Zhu, Xiaofeng Mai, Wenyu Jiao, Siju Zhang, Sami Dib, Amelia M. Stutz, Aina Palau, Patricio Sanhueza, Annie Zavagno, A. Y. Yang, Xindi Tang, Mengyao Tang, Yichen Zhang, Pablo García, Tianwei Zhang, Anindya Saha, Shanghuo Li, Paul F. Goldsmith, Leonardo Bronfman, Chang Won Lee, Kotomi Taniguchi, Swagat Ranjan Das, Prasanta Gorai, Ariful Hoque, Li Chen, Zhiping Kou, Jianjun Zhou, Yankun Zhang, L. Viktor Tóth, Tapas Baug, Xianjin Shen, Chuanshou Li, Jiahang Zou, Ankan Das, Hafiz Nazeer, L. K. Dewangan, Jihye Hwang, James O. Chibueze
Figure caption: (a) Thermal Jeans length vs. protocluster clumps distance. (b) Ratio of core separation to thermal Jeans length vs. protocluster clumps distance.
Paper: The ALMA-QUARKS Survey. III. Clump-to-core Fragmentation and Searches for High-mass Starless Cores
Authors: Dongting Yang, Hong-Li Liu, Tie Liu, Xunchuan Liu, Fengwei Xu, Sheng-Li Qin, Anandmayee Tej, Guido Garay, Lei Zhu, Xiaofeng Mai, Wenyu Jiao, Siju Zhang, Sami Dib, Amelia M. Stutz, Aina Palau, Patricio Sanhueza, Annie Zavagno, A. Y. Yang, Xindi Tang, Mengyao Tang, Yichen Zhang, Pablo García, Tianwei Zhang, Anindya Saha, Shanghuo Li, Paul F. Goldsmith, Leonardo Bronfman, Chang Won Lee, Kotomi Taniguchi, Swagat Ranjan Das, Prasanta Gorai, Ariful Hoque, Li Chen, Zhiping Kou, Jianjun Zhou, Yankun Zhang, L. Viktor Tóth, Tapas Baug, Xianjin Shen, Chuanshou Li, Jiahang Zou, Ankan Das, Hafiz Nazeer, L. K. Dewangan, Jihye Hwang, James O. Chibueze
Figure caption: Mass–radius relation between ATOMS “unknown” sources and candidate starless cores of the QUARKS TM2+ACA catalog.
Paper: The ALMA-QUARKS Survey. III. Clump-to-core Fragmentation and Searches for High-mass Starless Cores
Authors: Dongting Yang, Hong-Li Liu, Tie Liu, Xunchuan Liu, Fengwei Xu, Sheng-Li Qin, Anandmayee Tej, Guido Garay, Lei Zhu, Xiaofeng Mai, Wenyu Jiao, Siju Zhang, Sami Dib, Amelia M. Stutz, Aina Palau, Patricio Sanhueza, Annie Zavagno, A. Y. Yang, Xindi Tang, Mengyao Tang, Yichen Zhang, Pablo García, Tianwei Zhang, Anindya Saha, Shanghuo Li, Paul F. Goldsmith, Leonardo Bronfman, Chang Won Lee, Kotomi Taniguchi, Swagat Ranjan Das, Prasanta Gorai, Ariful Hoque, Li Chen, Zhiping Kou, Jianjun Zhou, Yankun Zhang, L. Viktor Tóth, Tapas Baug, Xianjin Shen, Chuanshou Li, Jiahang Zou, Ankan Das, Hafiz Nazeer, L. K. Dewangan, Jihye Hwang, James O. Chibueze
Figure caption: Distribution of physical parameters of the QUARKS TM2+ACA dense cores against evolutionary stage. Panels (a)–(e) present the parameter of total flux, mass, radius, number density, and surface density, respectively.
Paper: An Iterative, Deep Learning Approach for Switchback Classification in Parker Solar Probe Data
Authors: Dona Chathuni P. Kuruppuaratchi, Jacob R. Gruesbeck
Figure caption: The CM (top left) and PRC (top right) for model performance vs. J. Huang et al. (2023) catalog without considering specific regions or quality flags. The CM (bottom left) and PRC (bottom right) for F. Pecora et al. (2022) catalog. ParkerNet performs better than the baseline skill model for both J. Huang et al. (2023) and F. Pecora et al. (2022) catalogs. CM and PRC made for E8 (2021 April 26 10:00–2021 April 27 15:15).
Paper: An Iterative, Deep Learning Approach for Switchback Classification in Parker Solar Probe Data
Authors: Dona Chathuni P. Kuruppuaratchi, Jacob R. Gruesbeck
Figure caption: J. Huang et al. (2023) switchback candidates (top panel) from LQP start to TQP end, F. Pecora et al. (2022) switchback candidates (middle panel) for 10 s magnetic field resolution data with a 6 hr window input for their online catalog, and ParkerNet predictions (bottom panel) made at a threshold of 0.6, plotted on top of the radial magnetic field signal. ParkerNet’s predictions match the J. Huang et al. (2023) catalog data very well, especially the highest quality (quality 3) data. While ParkerNet predictions pick up most of the F. Pecora et al. (2022) data, they do not match in duration. The period for this figure is from 2021 April 26 10:00 to 2021 April 27 15:59.
Paper: An Iterative, Deep Learning Approach for Switchback Classification in Parker Solar Probe Data
Authors: Dona Chathuni P. Kuruppuaratchi, Jacob R. Gruesbeck
Figure caption: The CM (top left) and PRC (top right) for model performance vs. J. Huang et al. (2023) catalog without considering specific regions or quality flags. The CM (bottom left) and PRC (bottom right) for F. Pecora et al. (2022) catalog. ParkerNet performs better than the baseline skill model for J. Huang et al. (2023), and its selective nature is reflected in 0 false positives. CM and PRC made for E6 (2020 September 18 full day).