adsabs/AstroCLIMB huggingface dataset:
Paper: An Iterative, Deep Learning Approach for Switchback Classification in Parker Solar Probe Data
Authors: Dona Chathuni P. Kuruppuaratchi, Jacob R. Gruesbeck
Figure caption: Heatmap visually depicting ParkerNet performance for five metrics (precision, recall, F1 score, F2 score, IoU) for the various regions and quality flags in the J. Huang et al. (2023) catalog and the F. Pecora et al. (2022) catalog for E8 (2021 April 26 10:00–2021 April 27 15:15). All scores are between 0 and 1, with 0 being the lowest performance and 1 being the highest. ParkerNet predictions agree very well with the highest quality spike regions in the J. Huang et al. (2023) data and perform better in the leading regions than the trailing regions.
Paper: An Iterative, Deep Learning Approach for Switchback Classification in Parker Solar Probe Data
Authors: Dona Chathuni P. Kuruppuaratchi, Jacob R. Gruesbeck
Figure caption: Heatmap visually depicting ParkerNet performance for five metrics (precision, recall, F1 score, F2 score, IoU) for the various regions and quality flags in the J. Huang et al. (2023) catalog and the F. Pecora et al. (2022) catalog for E6 (2020 August 18 full day). All scores are between 0 and 1, with 0 being the lowest performance and 1 being the highest. ParkerNet shows good agreement with the highest quality spike region of the J. Huang et al. (2023) data and its performance is slightly better in the leading regions than the trailing regions.
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 are overall very conservative and have decent agreement with the highest quality (quality 3) J. Huang et al. (2023) data. The period for this figure is from E6 (2020 September 18 full day).
Paper: An Iterative, Deep Learning Approach for Switchback Classification in Parker Solar Probe Data
Authors: Dona Chathuni P. Kuruppuaratchi, Jacob R. Gruesbeck
Figure caption: Graphic depicting the targeted-HITL, iterative learning process of ParkerNet version 1.0. The input data set structure is shown
Paper: An Iterative, Deep Learning Approach for Switchback Classification in Parker Solar Probe Data
Authors: Dona Chathuni P. Kuruppuaratchi, Jacob R. Gruesbeck
Figure caption: ParkerNet’s architecture and depiction of the flow of information starting from the CNN layers (feature extraction), BiLSTM layers (sequence learning), to prediction (output layer).
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, but also extend farther. 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 E7 (2021 January 12 full day).
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 the F. Pecora et al. (2022) catalog. ParkerNet performs better than the baseline skill model for J. Huang et al. (2023), and its more lenient nature in this case is reflected in a high number of FPs. CM and PRC made for E7 (2021 January 12 full day).
Paper: An Iterative, Deep Learning Approach for Switchback Classification in Parker Solar Probe Data
Authors: Dona Chathuni P. Kuruppuaratchi, Jacob R. Gruesbeck
Figure caption: Average impact in terms of relative AUC-PRC percentage for each input feature, across all prediction sets from each encounter. The largest contribution during inference comes from the radial component of the magnetic field (BR), for both SPC and SPAN-I data sets. This is followed by modest influence from plasma variables except for the tangential velocity (VT), which appears to be harmful for both data sets. Vmag for SPAN-I is very close to 0.
Paper: An Iterative, Deep Learning Approach for Switchback Classification in Parker Solar Probe Data
Authors: Dona Chathuni P. Kuruppuaratchi, Jacob R. Gruesbeck
Figure caption: Heatmap visually depicting ParkerNet performance for five metrics for the various regions and quality flags in the J. Huang et al. (2023) catalog and the F. Pecora et al. (2022) catalog for E7 (2021 January 12 full day). All scores are between 0 and 1, with 0 being the lowest performance and 1 being the highest. ParkerNet predictions show good agreement with the spike regions of the J. Huang et al. (2023) data and perform better in the leading regions than the trailing regions.
Paper: An Iterative, Deep Learning Approach for Switchback Classification in Parker Solar Probe Data
Authors: Dona Chathuni P. Kuruppuaratchi, Jacob R. Gruesbeck
Figure caption: AUC-PRC for ParkerNet’s predictions on FIELDS (MAG) + SPC data sets (coral) and FIELDS (MAG) + SPAN-I data sets (blue). Model performance in terms of AUC-PRC is comparable for both data sets, suggesting model robustness.
Paper: Rapid Stellar and Binary Population Synthesis with COMPAS: Methods Paper II
Authors: Ilya Mandel, Jeff Riley, Adam Boesky, Adam Brček, Ryosuke Hirai, Veome Kapil, Mike Y. M. Lau, JD Merritt, Nicolás Rodríguez-Segovia, Isobel Romero-Shaw, Yuzhe Song, Simon Stevenson, Avi Vajpeyi, L. A. C. van Son, Alejandro Vigna-Gómez, Reinhold Willcox
Figure caption: Simplified flowchart illustrating one step of COMPAS binary evolution (see Figure 4 of Paper I). It does not include checks for whether to terminate evolution, e.g., on touching/merging stars, double compact objection formation, or unbound binaries (unless relevant flags allow for continuing evolution). The numbers in parentheses refer to the sections of this paper that describe the relevant changes.
Paper: Reconstruction of Solar Extreme-ultraviolet Irradiance Using Ca II K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification
Authors: Haodi Jiang, Qin Li, Jason T. L. Wang, Haimin Wang, Serena Criscuoli
Figure caption: Scatter plots showing that the two EUV channels have similar trends in observed and SEMNet-predicted EUV flux measurements based on the PSPT Ca II K images in the test set. Left panel: scatter plot for the observed EUV flux measurements showing that there is a linear relationship, y = 2.08x, between the two EUV channels. Right panel: scatter plot for the predicted EUV flux measurements showing that there is also a linear relationship, y = 2.10x, between the two EUV channels. The similar trends in the data present another level of validation of SEMNet.
Paper: Reconstruction of Solar Extreme-ultraviolet Irradiance Using Ca II K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification
Authors: Haodi Jiang, Qin Li, Jason T. L. Wang, Haimin Wang, Serena Criscuoli
Figure caption: Observed and SEMNet-predicted EUV flux measurements in the period between 1998 and 2014 based on the PSPT Ca II K images in the test set. A shaded region represents the prediction interval of [μ − 2σ, μ + 2σ]. Top panel: results for the 01–50 nm wavelength range. Bottom panel: results for the 26–34 nm wavelength range.
Paper: Reconstruction of Solar Extreme-ultraviolet Irradiance Using Ca II K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification
Authors: Haodi Jiang, Qin Li, Jason T. L. Wang, Haimin Wang, Serena Criscuoli
Figure caption: Observed and SEMNet-predicted EUV flux measurements during the solar maxima in solar cycles 23 and 24 based on the PSPT Ca II K images in the test set. A shaded region represents the prediction interval of [μ − 2σ, μ + 2σ]. Top two panels: results for the two wavelength ranges under consideration in 2001. Bottom two panels: results for the two wavelength ranges in 2014.
Paper: Reconstruction of Solar Extreme-ultraviolet Irradiance Using Ca II K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification
Authors: Haodi Jiang, Qin Li, Jason T. L. Wang, Haimin Wang, Serena Criscuoli
Figure caption: Illustration of the architecture of SEMNet, which is a ResNet-like model with uncertainty quantification.
Paper: Reconstruction of Solar Extreme-ultraviolet Irradiance Using Ca II K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification
Authors: Haodi Jiang, Qin Li, Jason T. L. Wang, Haimin Wang, Serena Criscuoli
Figure caption: 2D histograms of observed and SEMNet-predicted EUV flux measurements based on the PSPT Ca II K images in the test set. Left panel: histogram for the 0.1–50 nm wavelength range. Right panel: histogram for the 26–34 nm wavelength range.
Paper: Reconstruction of Solar Extreme-ultraviolet Irradiance Using Ca II K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification
Authors: Haodi Jiang, Qin Li, Jason T. L. Wang, Haimin Wang, Serena Criscuoli
Figure caption: Illustration of the testing of SEMNet. The trained SEMNet model takes as input individual 256 × 256 full-disk Ca II K images and produces as output corresponding SEM flux measurements in the 0.1–50 nm and 26–34 nm wavelength ranges, where the shaded areas represent estimated uncertainties.
Paper: Reconstruction of Solar Extreme-ultraviolet Irradiance Using Ca II K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification
Authors: Haodi Jiang, Qin Li, Jason T. L. Wang, Haimin Wang, Serena Criscuoli
Figure caption: Observed and SEMNet-predicted EUV flux measurements in the period between 1996 and 1999 based on the KSO Ca II K images in the test set. Top panel: results for the 01–50 nm wavelength range. Bottom panel: results for the 26–34 nm wavelength range.
Paper: Reconstruction of Solar Extreme-ultraviolet Irradiance Using Ca II K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification
Authors: Haodi Jiang, Qin Li, Jason T. L. Wang, Haimin Wang, Serena Criscuoli
Figure caption: Comparison of model predictions on different data sources. Shown in the figure are observed and SEMNet-predicted EUV flux measurements on the PSPT Ca II K data set, the raw KSO Ca II K data set (before preprocessing), and the preprocessed KSO Ca II K data set with model fine-tuning, in their overlapping period from 1999 March to September. Top panel: results for the 0.1–50 nm wavelength range, with an estimated MRE of approximately 2.4% caused by cross-instrument domain discrepancies. Bottom panel: results for the 26–34 nm wavelength range, with an estimated MRE of 2% from cross-instrument effects.
Paper: Reconstruction of Solar Extreme-ultraviolet Irradiance Using Ca II K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification
Authors: Haodi Jiang, Qin Li, Jason T. L. Wang, Haimin Wang, Serena Criscuoli
Figure caption: Comparison of model predictions for varying FWHM. Shown in the figure are observed and SEMNet-predicted EUV flux measurements on the PSPT Ca II K images (FWHM 0.25 nm), NBC images (FWHM 0.1 nm), and preprocessed KSO images (FWHM 0.5 nm), during the period of 2007 June 9–11. Top panel: results for the 0.1–50 nm wavelength range. Bottom panel: results for the 26–34 nm wavelength range.
Paper: Reconstruction of Solar Extreme-ultraviolet Irradiance Using Ca II K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification
Authors: Haodi Jiang, Qin Li, Jason T. L. Wang, Haimin Wang, Serena Criscuoli
Figure caption: Reconstruction of solar EUV irradiance, more precisely SEM EUV fluxes, in the period between 1950 and 1960 based on the KSO Ca II K images in this period. Shown in the figure are F10.7 values, used as a reference, and SEMNet-predicted EUV flux measurements, averaged over each month for visibility, for the 0.1–50 nm wavelength range (top) and the 26–34 nm wavelength range (bottom). Gaps on the curves are due to missing F10.7 values or KSO images where the two types of data cannot be temporally aligned. The long-term evolution trend of F10.7 is similar to that of reconstructed SEM EUV fluxes.
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 mass. 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: A comparison of the detection fraction of [Ne V] λ3426 in galaxies with and without the presence of an outflow in [O III] λ5007.
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: Dynamic spectra of a simulated FRB in the band between 400 and 800 MHz that was created according to the model in H. Qiu et al. (2023). RFI was injected between 725 and 750 MHz for the duration of the observation (1 s). The signal-to-interference ratio (SIR) is −30 dB, making the FRB signal barely visible between 200 and 400 ms.
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 of the SOI search with MVDR. Green dashed lines indicate the upper and lower bounds on the localization estimate of θ when only the DFT beamforming algorithm is used. Our algorithm searches only within the range defined by the green lines resulting from the DFT step. The results are for the azimuth cut (i.e., elevation ϕ = 0).
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: Formed beams and accuracy of DFT-based beamforming.
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: Circles indicate the DFT beam search locations, the red X is the true location, and the square dashed grid indicates the area where MVDR search is limited.
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: Original FRB pipeline and enhanced pipeline with optimum beamforming (dashed box).
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: (a) Time-domain representation of the simulated FRB signal in one frequency channel, (b) the addition of RFI and thermal noise, and (c) the result of our beamforming/spatial filtering after the spatial location of the simulated FRB was estimated with our DFT/MVDR search algorithm. Voltages of the FRB are normalized to a maximum value of 1 for illustration purposes.
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: Geometry of a URA of antennas modeled in the yz plane. The steering vector toward a source of interest is given by the angular coordinates (ϕ, θ).
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: Dynamic spectra of the FRB illustrated in Figure 1 (with an SIR of −30 dB) after our DFT/MVDR-based algorithm is applied. The FRB is clearly visible now.
Paper: Toward a Robust R2D2 Paradigm for Radio-interferometric Imaging: Revisiting Deep Neural Network Training and Architecture
Authors: Amir Aghabiglou, Chung San Chu, Chao Tang, Arwa Dabbech, Yves Wiaux
Figure caption: Illustration of R2D2’s joint image estimation and uncertainty quantification functionality on selected RI simulations. Panel (a) focuses on epistemic uncertainty across R2D2 realizations utilizing an image of Messier 106. Panel (b) focuses on epistemic uncertainty across variations of the parameter of Briggs weighting (ρbr) utilizing an image of 3C 353. The first row in both panels displays the dirty image (left) and ground-truth image (right). In panel (a) (respectively panel (b)), second and fourth rows show the respective estimated images for worst and best realizations of R2D2﹩{}_{{{ \mathcal A }}_{1},{{ \mathcal T }}_{2}}﹩ (left) and R2D2﹩{}_{{{ \mathcal A }}_{2},{{ \mathcal T }}_{2}}﹩ (right) (respectively estimated images with ρbr = −1 and ρbr = 1). Third and fifth rows in both panels show the corresponding residual dirty images. The sixth row displays the relative uncertainty image ﹩[{\boldsymbol{\sigma }}/{\boldsymbol{\mu }}](\widehat{{\boldsymbol{X}}})﹩. Metrics are reported inside the associated images.
Paper: Toward a Robust R2D2 Paradigm for Radio-interferometric Imaging: Revisiting Deep Neural Network Training and Architecture
Authors: Amir Aghabiglou, Chung San Chu, Chao Tang, Arwa Dabbech, Yves Wiaux
Figure caption: R2D2 core DNN architectures. Panel (a) illustrates the U-Net model architecture (A. Aghabiglou et al. 2024). The second row presents the U-WDSR model: panel (b1) shows the U-WDSR architecture, and panel (b2) depicts its WDSR layer. The WDSR residual body (in green boxes) is interlaced with the convolutional layers of the U-Net. WDSR consists of 16 consecutive residual blocks. At each stage, the spatial size of feature maps is indicated at the lower center of each box. The number of channels is indicated at the outer edge of each box.
Paper: Toward a Robust R2D2 Paradigm for Radio-interferometric Imaging: Revisiting Deep Neural Network Training and Architecture
Authors: Amir Aghabiglou, Chung San Chu, Chao Tang, Arwa Dabbech, Yves Wiaux
Figure caption: Analysis of R2D2’s epistemic uncertainty across R2D2 realizations (first row) and ρbr variation (second row). From left to right: evolution of the reconstruction metrics SNR and logSNR, as well as the mean of the relative uncertainty image MRU, across the iterations of R2D2 models. The shaded area presents the standard deviations at each point.
Paper: Toward a Robust R2D2 Paradigm for Radio-interferometric Imaging: Revisiting Deep Neural Network Training and Architecture
Authors: Amir Aghabiglou, Chung San Chu, Chao Tang, Arwa Dabbech, Yves Wiaux
Figure caption: Reconstruction results of R2D2 models in terms of SNR (left) and logSNR (right) shown as functions of Briggs parameter ρbr. Each point represents the average metric calculated over 1000 inverse problems corresponding to a specific ρbr value. Standard deviation (±std) is reported at each point.
Paper: Toward a Robust R2D2 Paradigm for Radio-interferometric Imaging: Revisiting Deep Neural Network Training and Architecture
Authors: Amir Aghabiglou, Chung San Chu, Chao Tang, Arwa Dabbech, Yves Wiaux
Figure caption: Evolution of the size of the training data set throughout the iterations of R2D2﹩{}_{{{ \mathcal A }}_{1},{{ \mathcal T }}_{2}}﹩ and R2D2﹩{}_{{{ \mathcal A }}_{2},{{ \mathcal T }}_{2}}﹩, shown as a fraction of the size of the initial training data set.
Paper: Toward a Robust R2D2 Paradigm for Radio-interferometric Imaging: Revisiting Deep Neural Network Training and Architecture
Authors: Amir Aghabiglou, Chung San Chu, Chao Tang, Arwa Dabbech, Yves Wiaux
Figure caption: Cygnus A reconstruction results using R2D2 models. The first (respectively second and fourth) row shows the image estimate (left) and corresponding residual dirty image (right) obtained with a given realization of R2D2﹩{}_{{{ \mathcal A }}_{1},{{ \mathcal T }}_{1}}﹩ (respectively R2D2﹩{}_{{{ \mathcal A }}_{1},{{ \mathcal T }}_{2}}﹩ and R2D2﹩{}_{{{ \mathcal A }}_{2},{{ \mathcal T }}_{2}}﹩). The third (respectively fifth) row displays the mean image and associated relative uncertainty image delivered by R2D2﹩{}_{{{ \mathcal A }}_{1},{{ \mathcal T }}_{2}}﹩ (respectively R2D2﹩{}_{{{ \mathcal A }}_{2},{{ \mathcal T }}_{2}}﹩) and computed over R = 25 reconstructions (five different model realizations combined with five different values of ρbr). The red ellipse in the image estimates and the relative uncertainty image highlights the region of faint emission. The middle column visualizes a zoomed-in and contrast-enhanced view of this region. Colored arrows highlight selected features, in blue to point toward a recovered feature, and in red to indicate its location when missing (in black inside the relative uncertainty image). White circles in the residual dirty images indicate the locations of the hot spots. MRU and RDR metrics are reported inside the associated images.
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: Data analysis pipeline: 45 AR emergence areas have been tracked from the SDO/HMI full disk VD, Φm, and Ic maps. The tracked regions were split into smaller tiles, and the timeline data sets were created by averaging the values of each tile. The timelines are used as inputs during the training and validation/testing process of different LSTM architectures, such as Model 8 (M8) in red. The architecture of M8 is presented in the area enclosed by the red dashed line.
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: From left to right: distribution of the acoustic power for 3–4 mHz and 4–5 mHz, the continuum intensity, and magnetograms in the vicinity of the area where AR 11726 forms. The presented 17° × 17° maps are a fraction of the analyzed 30﹩\mathop{.}\limits^{\unicode{x000b0}}﹩66 × 30﹩\mathop{.}\limits^{\unicode{x000b0}}﹩66 area. The upper row corresponds to the time when the prediction of the AR emergence was made. During this time, the increase in magnetic flux corresponds to ≤9.2% of the maximum magnetic flux that will emerge within AR 11726. Maps corresponding to the predicted start of the emergence are shown in the bottom row. The color range for the power maps is adjusted to emphasize the power suppression. The color scale for the magnetic field is saturated at ±600 G.