adsabs/AstroCLIMB huggingface dataset:
Paper: Determining the Core Structure and Nuclear Equation of State of Rotating Core-collapse Supernovae with Gravitational Waves by Convolutional Neural Networks
Authors: Yang-Sheng Chao, 仰生 趙, Chen-Zhi Su, 晨知 蘇, Ting-Yuan Chen, 莛元 陳, Daw-Wei Wang, 道維 王, Kuo-Chuan Pan, 國全 潘
Figure caption: Upper panel: GW strains around the core bounce (t  =  0 ms) with A = 634 km and SFHo EOS. Different colors represent different initial rotational speeds ω 0 (see Equation (1)). Lower panel: GWs with A = 634 km and ω 0 = 2.0 rad s−1. Different colors indicate results obtained by different EOSs. All waveforms are taken from Richers et al. (2017).
Paper: Determining the Core Structure and Nuclear Equation of State of Rotating Core-collapse Supernovae with Gravitational Waves by Convolutional Neural Networks
Authors: Yang-Sheng Chao, 仰生 趙, Chen-Zhi Su, 晨知 蘇, Ting-Yuan Chen, 莛元 陳, Daw-Wei Wang, 道維 王, Kuo-Chuan Pan, 國全 潘
Figure caption: Confusion matrices for the prediction of the core angular rotational speed, ω 0. Thirty-two values of ω 0 are included in the data set, and two different time domains for the GW waveforms are used, as shown in Figure 2. The model accuracy is 95% and 80%, respectively. Color codes are to highlight the significance of the predicted data compared to the ground truth in the data set.
Paper: Determining the Core Structure and Nuclear Equation of State of Rotating Core-collapse Supernovae with Gravitational Waves by Convolutional Neural Networks
Authors: Yang-Sheng Chao, 仰生 趙, Chen-Zhi Su, 晨知 蘇, Ting-Yuan Chen, 莛元 陳, Daw-Wei Wang, 道維 王, Kuo-Chuan Pan, 國全 潘
Figure caption: Confusion matrix for the prediction of the rotational length scale, A. Five values of A are included in the data set, and two different time domains for the GW strain input are used for the model training and testing: the upper panel is for t ∈ (−10, 6) ms, and the lower panel is for t ∈ (−10, 54) ms. The model accuracy is 93% and 85%, respectively. Color codes are to highlight the significance of the predicted data compared to the ground truth in the data set.
Paper: Determining the Core Structure and Nuclear Equation of State of Rotating Core-collapse Supernovae with Gravitational Waves by Convolutional Neural Networks
Authors: Yang-Sheng Chao, 仰生 趙, Chen-Zhi Su, 晨知 蘇, Ting-Yuan Chen, 莛元 陳, Daw-Wei Wang, 道維 王, Kuo-Chuan Pan, 國全 潘
Figure caption: Confusion matrix of our CNN model to classify the four groups of Type I. To facilitate the discussion, the groups are labeled according to the name of dominant EoS inside; see Figure 4.
Paper: To Rain or Not to Rain: Correlating GOES Flare Class and Coronal Rain Statistics
Authors: E. I. Mason, K. L. Kniezewski
Figure caption: Top: relationship between the proportion of post-flare arcade rain occurrences (by GOES class) and the solar cycle as plotted by the annual sunspot number. Bottom left: sunspot number vs. the percentage of flares in each class that produce rain. Linear fit lines have been plotted and their respective r 2 values are shown on the left side of the graph. All are reasonably good fits, ranging from about 0.55 (C class) to 0.74 (M class). These may prove to be useful tracers for the solar cycle. Bottom left: residuals for all three classes of flare; the distribution of all three are random, showing that the linear fit is a good choice for these relationships.
Paper: To Rain or Not to Rain: Correlating GOES Flare Class and Coronal Rain Statistics
Authors: E. I. Mason, K. L. Kniezewski
Figure caption: Graph of flare duration, as determined by the GOES X-ray data, vs. post-flare arcade rain duration. Linear fits for each class show that all are generally a positive relationship, but none is statistically significant. M-class flares have the highest correlation, with an r 2 value of approximately 0.25.
Paper: To Rain or Not to Rain: Correlating GOES Flare Class and Coronal Rain Statistics
Authors: E. I. Mason, K. L. Kniezewski
Figure caption: Top: histogram summarizing the relationship between post-flare arcade rain occurrence and the Hale class of the source active region (the bins along the x-axis), color-coded by GOES flare class. Bottom: analogous histogram showing eruptivity of flares and the source region’s Hale class. Both show a strong preference for β-type active regions.
Paper: To Rain or Not to Rain: Correlating GOES Flare Class and Coronal Rain Statistics
Authors: E. I. Mason, K. L. Kniezewski
Figure caption: Proportions of X-, M-, and C-class flares associated with CMEs and post-flare rain. There are few statistical conclusions that can be drawn between the raining and eruptive categories, due to the low rates of non-raining flares in X- and M-class flares, and the low rates of eruptive flares in C-class flares.
Paper: The Role of Strong Magnetic Fields in Stabilizing Highly Luminous Thin Disks
Authors: Bhupendra Mishra, P. Chris Fragile, Jessica Anderson, Aidan Blankenship, Hui Li, Krzysztof Nalewajko
Figure caption: Mass accretion rate measured near the ISCO, scaled to the Eddington luminosity, i.e., ﹩\dot{m}=\dot{M}{c}^{2}/{L}_{\mathrm{Edd}}﹩ (upper panel); luminosity through a radial shell at r ≈ 15 GM/c 2 (middle-left panel) and r ≈ 20 GM/c 2 (lower-left panel), scaled to the Eddington luminosity; and the radiative efficiency, ﹩\eta =L/\dot{M}{c}^{2}=(L/{L}_{\mathrm{Edd}})/\dot{m}﹩ at each radius (rightmost panels). The horizontal thin, dashed black lines show the target values of ﹩\dot{m}=3﹩, L/L Edd = 0.17, and η = 0.057, respectively. Because the unstable simulations (S3Ed and S3Em) have collapsed to a different disk solution, they settle to a lower ﹩\dot{m}﹩ and L. Note that for these plots we employ moving window averages with window widths equal to three consecutive data to smooth them.
Paper: The Role of Strong Magnetic Fields in Stabilizing Highly Luminous Thin Disks
Authors: Bhupendra Mishra, P. Chris Fragile, Jessica Anderson, Aidan Blankenship, Hui Li, Krzysztof Nalewajko
Figure caption: Profiles of density (top), mass flux (second from top), B  r  (middle), B  θ  (second from bottom), and B  ϕ  (bottom) for the dipole (left), quadrupole (center), and vertical (right) field cases at time t = 20,000 GM/c 3. Overlaid on the density profiles are curves representing the time-averaged disk height (solid, blue), and the absorption (solid, green), effective (dotted–dashed, white), and scattering (dashed, white) photospheres. The dashed, black, vertical line corresponds to R = 15 GM/c 2 (the radius at which we extract Figures 4, 7, and 9).
Paper: The Role of Strong Magnetic Fields in Stabilizing Highly Luminous Thin Disks
Authors: Bhupendra Mishra, P. Chris Fragile, Jessica Anderson, Aidan Blankenship, Hui Li, Krzysztof Nalewajko
Figure caption: Time–space (t−z) plots of the azimuthally averaged toroidal component of magnetic field at r = 15 GM/c 2. From left to right are models S3Ed, S3Eq, and S3Ev, respectively. Models S3Ed and S3Ev show a current sheet at the midplane, whereas S3Eq does not. Both the S3Ed and S3Eq models show field reversals starting at t ≳ 104 GM/c 3.
Paper: The Role of Strong Magnetic Fields in Stabilizing Highly Luminous Thin Disks
Authors: Bhupendra Mishra, P. Chris Fragile, Jessica Anderson, Aidan Blankenship, Hui Li, Krzysztof Nalewajko
Figure caption: Comparisons of the Shakura–Sunyaev disk model (black dashed lines) with profiles from the simulations for the mass accretion rate (upper left), disk height (upper right), gas temperature (lower left), and surface density (lower right), time-averaged over the period t = 18,000 GM/c 3 to the final time of each simulation as reported in Table 1. Our stable disk models, S3Eq and S3Ev, show a height profile somewhat thicker than the model prediction. The temperature remains nearly the same as what we started with, while the surface density finishes slightly higher than its initial configuration. Note that the dashed black curves are computed using the initial disk profiles from the numerical model.
Paper: The Role of Strong Magnetic Fields in Stabilizing Highly Luminous Thin Disks
Authors: Bhupendra Mishra, P. Chris Fragile, Jessica Anderson, Aidan Blankenship, Hui Li, Krzysztof Nalewajko
Figure caption: Vertical profiles of various disk parameters at r = 15 GM/c 2 for the S3Ed (dashed red), S3Eq (solid yellow), and S3Ev (dotted–dashed green) simulations at t = 20,000 GM/c 3. All quantities are azimuthally averaged.
Paper: The Role of Strong Magnetic Fields in Stabilizing Highly Luminous Thin Disks
Authors: Bhupendra Mishra, P. Chris Fragile, Jessica Anderson, Aidan Blankenship, Hui Li, Krzysztof Nalewajko
Figure caption: Spacetime (R−t) plots of the density-weighted, shell-averaged viscosity parameter, α, for the dipole (S3Ed; left), quadrupole (S3Eq; middle), and vertical (S3Ev; right) simulations.
Paper: Millimeter Dust Emission and Planetary Dynamics in the HD 106906 System
Authors: Anna J. Fehr, A. Meredith Hughes, Rebekah I. Dawson, Rachel E. Marino, Matan Ackelsberg, Jamar Kittling, Kevin M. Flaherty, Erika Nesvold, John Carpenter, Sean M. Andrews, Brenda Matthews, Katie Crotts, Paul Kalas
Figure caption: Posterior distribution of planet semimajor axis and eccentricity from Nguyen et al. (2021). The gray-scale histogram and scatter points are as in Figure 8. The pink shading shows the stability criteria Y EK and Y  MA , and the blue shading shows the region of the parameter space where the secular timescale of the planet is too long for dynamical interactions to take effect within the lifetime of the disk. The dashed blue line shows the location where the secular timescale of interaction with the planet is equal to the secular timescale of interaction with the binary at 70 au. Above this line, the morphology of the disk at the radius of peak brightness is primarily controlled by interaction with the planet. The region of the parameter space where the disk is primarily shaped by the planet is also the region of the parameter space where the disk is likely unstable.
Paper: Millimeter Dust Emission and Planetary Dynamics in the HD 106906 System
Authors: Anna J. Fehr, A. Meredith Hughes, Rebekah I. Dawson, Rachel E. Marino, Matan Ackelsberg, Jamar Kittling, Kevin M. Flaherty, Erika Nesvold, John Carpenter, Sean M. Andrews, Brenda Matthews, Katie Crotts, Paul Kalas
Figure caption: Corner plot based on the posterior distribution from Nguyen et al. (2021), showing five orbital parameters for HD 106906b. In order of appearance, the parameters included are the (1) semimajor axis, a; (2) eccentricity, e; (3) mutual inclination i; (4) argument of periastron, ω; and (5) longitude of the ascending node, Ω. The gray-scale probability distributions show the 2D covariances and 1D marginalized posterior distributions derived from astrometric modeling. The colored dots are a random sample of the posterior distribution, with color representing the semimajor axis where the secular timescale of the planet is equal to the secular timescale of the binary (r  c ). The regions of the parameter space where the binary stabilizes the disk, leading to morphology consistent with the ALMA data, are characterized by large a, low e, and low i  m .
Paper: Millimeter Dust Emission and Planetary Dynamics in the HD 106906 System
Authors: Anna J. Fehr, A. Meredith Hughes, Rebekah I. Dawson, Rachel E. Marino, Matan Ackelsberg, Jamar Kittling, Kevin M. Flaherty, Erika Nesvold, John Carpenter, Sean M. Andrews, Brenda Matthews, Katie Crotts, Paul Kalas
Figure caption: Mean particle eccentricity (left), h/r FWHM (center), and χ 2 (right) for simulated disks with a variety of planet orbital parameters after 10 Myr of integration are shown by the colored scatter points. The gray-scale histogram shows the posterior distribution of planet semimajor axis and eccentricity taken from Nguyen et al. (2021). The colored shading is as in Figure 8.
Paper: Millimeter Dust Emission and Planetary Dynamics in the HD 106906 System
Authors: Anna J. Fehr, A. Meredith Hughes, Rebekah I. Dawson, Rachel E. Marino, Matan Ackelsberg, Jamar Kittling, Kevin M. Flaherty, Erika Nesvold, John Carpenter, Sean M. Andrews, Brenda Matthews, Katie Crotts, Paul Kalas
Figure caption: Evolution of disk particles in an example simulation with a single mass representing HD 106906 (in blue) and with the binary (in orange). The left column shows the eccentricity of the particles, where the right column shows the inclination of the particles relative to the disk midplane. The top row is the initial state of the disk, the second row is after 5 Myr of integration, and the bottom row is after 10 Myr of integration. The dashed gray line shows the critical radius, r  c , calculated by comparing the secular timescales of interaction with the binary and the planet. The disk particles are stabilized by the binary interior to that radius, so they have very low eccentricities and mutual inclinations after integration.
Paper: Millimeter Dust Emission and Planetary Dynamics in the HD 106906 System
Authors: Anna J. Fehr, A. Meredith Hughes, Rebekah I. Dawson, Rachel E. Marino, Matan Ackelsberg, Jamar Kittling, Kevin M. Flaherty, Erika Nesvold, John Carpenter, Sean M. Andrews, Brenda Matthews, Katie Crotts, Paul Kalas
Figure caption: Simulated disk morphology of an example simulation after 10 Myr of integration. The top row corresponds to a simulation where HD 106906 is represented by a single body, while the bottom row corresponds to a simulation where HD 106906 is represented by a binary. The left panel of each row shows the ALMA observations, the second panel shows the density distribution of simulated disk particles, the third panel shows a synthetic ALMA image of the simulated disk, and the final image shows the residuals. In the first, third, and fourth panels, the contour levels and symbols are as in Figure 1.
Paper: Millimeter Dust Emission and Planetary Dynamics in the HD 106906 System
Authors: Anna J. Fehr, A. Meredith Hughes, Rebekah I. Dawson, Rachel E. Marino, Matan Ackelsberg, Jamar Kittling, Kevin M. Flaherty, Erika Nesvold, John Carpenter, Sean M. Andrews, Brenda Matthews, Katie Crotts, Paul Kalas
Figure caption: Timescale of secular interaction with the binary ﹩\left(\tfrac{1}{{A}_{{\rm{p}}}}\right)﹩ in blue and the planet ﹩\left(\tfrac{1}{{A}_{{\rm{B}}}}\right)﹩ in orange over the region of the disk for a p = 850 au and e p = 0.44. The dashed gray line represents the radius, r  c , where the two timescales are equal. Interior to that radius, interaction with the binary will dominate the disk’s morphology, while exterior to that radius interaction with the planet will dominate.
Paper: Millimeter Dust Emission and Planetary Dynamics in the HD 106906 System
Authors: Anna J. Fehr, A. Meredith Hughes, Rebekah I. Dawson, Rachel E. Marino, Matan Ackelsberg, Jamar Kittling, Kevin M. Flaherty, Erika Nesvold, John Carpenter, Sean M. Andrews, Brenda Matthews, Katie Crotts, Paul Kalas
Figure caption: Vertical FWHM after 1 Myr (left) and 10 Myr (right) of integration for a range of mutual inclinations between the binary orbital plane and the disk midplane. Each blue scatter point represents the mean FWHM for a set of five simulations with varying planet parameters and with error bars showing the standard deviation.
Paper: Inferring Line-of-sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks
Authors: Haodi Jiang, Qin Li, Yan Xu, Wynne Hsu, Kwangsu Ahn, Wenda Cao, Jason T. L. Wang, Haimin Wang
Figure caption: Comparison between the ME inversion code and SDNN for producing the local LOS velocity maps containing granular patterns (top panels) and a portion of a sunspot penumbra (bottom panels) based on the test image/data set D3 from AR 12665 collected on 2017 July 13 16:20:12 UT, where training data were taken from D1 ∪ D5. The first column shows scatter plots where the X-axis and Y-axis represent the values obtained by the ME code and SDNN, respectively. Pixels on the diagonal line in a scatter plot have identical ME-calculated and SDNN-inferred values. The second and third columns show the local LOS velocity maps produced by the ME code and SDNN, respectively.
Paper: Inferring Line-of-sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks
Authors: Haodi Jiang, Qin Li, Yan Xu, Wynne Hsu, Kwangsu Ahn, Wenda Cao, Jason T. L. Wang, Haimin Wang
Figure caption: Comparison between the ME inversion code and SDNN for producing the LOS velocity maps (top panels) and Doppler width maps (bottom panels) based on the test image/data set D3 from AR 12665 collected on 2017 July 13 16:20:12 UT, where training data were taken from D1 ∪ D5. The first column shows scatter plots where the X-axis and Y-axis represent the values obtained by the ME code and SDNN, respectively. Pixels on the diagonal line in a scatter plot have identical ME-calculated and SDNN-inferred values. The second and third columns show the maps produced by the ME code and SDNN, respectively.
Paper: Inferring Line-of-sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks
Authors: Haodi Jiang, Qin Li, Yan Xu, Wynne Hsu, Kwangsu Ahn, Wenda Cao, Jason T. L. Wang, Haimin Wang
Figure caption: (A) Architecture of our SDNN model. The model contains two types of 1D convolution neural network (1D-CNN), named Type I 1D-CNN and Type II 1D-CNN, respectively. There are four Type I 1D-CNNs, which are stacked on top of a Type II 1D-CNN; hence the name SDNN (Stacked Deep Neural Networks) is used. The input of the SDNN is a sequence of four Stokes I, Q, U, and V components, where each component has 60 wavelength sampling points and the four Stokes components correspond to a pixel. Each Type I 1D-CNN takes as input a Stokes component, and encodes and produces as output a 240-dimensional feature vector. There are four Stokes components, so the four Type I 1D-CNNs output four 240-dimensional feature vectors, which are concatenated to form a 4 × 240 feature vector. The Type II 1D-CNN takes as input the 4 × 240 feature vector and produces as output two numbers representing the estimated Doppler shift and Doppler width of the input pixel. (B) Configuration details of the Type I 1D-CNN and Type II 1D-CNN used in our SDNN model.
Paper: Inferring Line-of-sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks
Authors: Haodi Jiang, Qin Li, Yan Xu, Wynne Hsu, Kwangsu Ahn, Wenda Cao, Jason T. L. Wang, Haimin Wang
Figure caption: Comparison between the ME inversion code and SDNN for producing the total magnetic field strengths (top panels), inclination angles (middle panels), and azimuth angles (bottom panels) of pixels based on the test image D3 from AR 12665 collected on 2017 July 13 16:20:12 UT, where training data were taken from D1 ∪ D5. The first column shows scatter plots where the X-axis and Y-axis represent the values obtained by the ME code and SDNN, respectively. Pixels on the diagonal line in a scatter plot have identical ME-calculated and SDNN-inferred values. The second and third columns show the magnetic field maps produced by the ME code and SDNN, respectively.
Paper: Inferring Line-of-sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks
Authors: Haodi Jiang, Qin Li, Yan Xu, Wynne Hsu, Kwangsu Ahn, Wenda Cao, Jason T. L. Wang, Haimin Wang
Figure caption: Comparison between the ME inversion code and SDNN for producing the LOS velocity maps (top panels) and Doppler width maps (bottom panels) based on the test image/data set D4 from AR 12673 collected on 2017 September 6 19:17:53 UT, where training data were taken from D1 ∪ D5. The first column shows scatter plots where the X-axis and Y-axis represent the values obtained by the ME code and SDNN, respectively. Pixels on the diagonal line in a scatter plot have identical ME-calculated and SDNN-inferred values. The second and third columns show the maps produced by the ME code and SDNN, respectively.
Paper: Inferring Line-of-sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks
Authors: Haodi Jiang, Qin Li, Yan Xu, Wynne Hsu, Kwangsu Ahn, Wenda Cao, Jason T. L. Wang, Haimin Wang
Figure caption: Comparison between the ME inversion code and SDNN for producing the LOS velocity maps (top panels) and Doppler width maps (bottom panels) based on the test image/data set D2 from AR 12371 collected on 2015 June 25 16:55:13 UT, where training data were taken from D1 ∪ D5. The first column shows scatter plots where the X-axis and Y-axis represent the values obtained by the ME code and SDNN, respectively. Pixels on the diagonal line in a scatter plot have identical ME-calculated and SDNN-inferred values. The second and third columns show the maps produced by the ME code and SDNN, respectively.
Paper: Inferring Line-of-sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks
Authors: Haodi Jiang, Qin Li, Yan Xu, Wynne Hsu, Kwangsu Ahn, Wenda Cao, Jason T. L. Wang, Haimin Wang
Figure caption: Comparison of MSVR, MLP, PCNN, and SDNN based on the test image/data set D2 (D3, and D4, respectively) from AR 12371 (AR 12665, and AR 12673, respectively) collected on 2015 June 25 16:55:13 UT (2017 July 13 16:20:12 UT, and 2017 September 6 19:17:53 UT, respectively) where training data were taken from D1 ∪ D5. Left column: performance metric values, displayed by bar charts, that are obtained by the four machine learning (ML) methods in predicting LOS velocities. Right column: performance metric values obtained by the four ML methods in predicting Doppler widths.
Paper: Inferring Line-of-sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks
Authors: Haodi Jiang, Qin Li, Yan Xu, Wynne Hsu, Kwangsu Ahn, Wenda Cao, Jason T. L. Wang, Haimin Wang
Figure caption: Comparison between the inversion results of SDO/HMI and GST/NIRIS in AR 12673. Top panels (from left to right): the LOS velocity map from the HMI Dopplergram collected on 2017 September 6 19:00:00 UT, the LOS velocity map produced by the ME inversion code, and the LOS velocity map produced by SDNN with training data from D1 ∪ D5, where the ME code and SDNN were applied to the test image/data set D6 collected by GST/NIRIS on 2017 September 6 19:01:48 UT. Bottom panels (from left to right): the scatter plot between ME and HMI, the scatter plot between SDNN and HMI, and the scatter plot between SDNN and ME.
Paper: New Coronae and Stellar Associations Revealed by a Clustering Analysis of the Solar Neighborhood
Authors: Leslie Moranta, Jonathan Gagné, Dominic Couture, Jacqueline K. Faherty
Figure caption: Distances between the median position of α Persei and Group 59 (Oh et al. 2017) members as a function of time in their past trajectories around the Galaxy. The left panel shows the distance along the Z-position only, and the right panel shows the 3D distance in XYZ Galactic position. The thick, dark purple line indicates the distances between the median positions, and the associated purple shaded regions around the thick lines indicate the measurement errors on the median distances, which increases over traceback time, especially in the XY-directions given that the locations of individual members become uncertain as the initial measurement uncertainties grow over time. The three additional shaded regions indicate areas of overlap at one, two, and three times the median absolute deviation of the distribution of the α Persei and Group 59 members at the current epoch. This indicates whether the two groups might overlap near the edges of their spatial extent, assuming that the shape of the associations do not change significantly over time. The purple circles indicate the epoch of formation of α Persei. Group 59 of Oh et al. (2017) is likely ≈ 160 Myr old based on the age of Theia 209, outside of the traceback range. Although the two associations crossed each other in the Z-dimension ≈ 38 and ≈ 77 Myr ago, their 3D distributions do not seem to significantly overlap at any point in the past 100 Myr.