adsabs/AstroCLIMB huggingface dataset:
Paper: H-AMR: A New GPU-accelerated GRMHD Code for Exascale Computing with 3D Adaptive Mesh Refinement and Local Adaptive Time Stepping
Authors: M. T. P. Liska, K. Chatterjee, D. Issa, D. Yoon, N. Kaaz, A. Tchekhovskoy, D. van Eijnatten, G. Musoke, C. Hesp, V. Rohoza, S. Markoff, A. Ingram, M. van der Klis
Figure caption: H-AMR shows excellent weak scaling for both simple and complex grids. For simple grids, it shows ∼80% numerical efficiency on 900 OLCF Summit nodes = 5400 GPUs for a single block of 1503 cells per GPU, without using AMR, SMR, or LAT (dashed–dotted orange line). For more complex grids, which use AMR and SMR, with 20 blocks of size 48 × 48 × 64 ∼ 503 cells per GPU, the efficiency at 900 nodes drops to ∼60% (not shown for brevity). While using LAT decreases the raw parallel efficiency to ∼35% (dashed green line), it also effectively speeds up the simulations by a factor of 4 (on one GPU) to 9 (on 5400 GPUs; solid blue line), leading to an effective numerical efficiency of nearly ∼200% on 5400 GPUs.
Paper: H-AMR: A New GPU-accelerated GRMHD Code for Exascale Computing with 3D Adaptive Mesh Refinement and Local Adaptive Time Stepping
Authors: M. T. P. Liska, K. Chatterjee, D. Issa, D. Yoon, N. Kaaz, A. Tchekhovskoy, D. van Eijnatten, G. Musoke, C. Hesp, V. Rohoza, S. Markoff, A. Ingram, M. van der Klis
Figure caption: Relativistic shock tubes taken from Komissarov (1999) with density (ρ) in the left panels and velocity (u  x ) in the right panels. Reconstruction with the MINMOD limiter is shown in black and with the PPM limiter in red. The low-resolution shock tubes with Δx  =  2.2 × 10−2 (solid lines) are less sharp than the high-resolution shock tubes with Δx  =  1.1 × 10−3 (dashed lines); however, they converge to the same solution. This verifies the ability of H-AMR to handle shock waves in relativistic plasma.
Paper: H-AMR: A New GPU-accelerated GRMHD Code for Exascale Computing with 3D Adaptive Mesh Refinement and Local Adaptive Time Stepping
Authors: M. T. P. Liska, K. Chatterjee, D. Issa, D. Yoon, N. Kaaz, A. Tchekhovskoy, D. van Eijnatten, G. Musoke, C. Hesp, V. Rohoza, S. Markoff, A. Ingram, M. van der Klis
Figure caption: Top panels: a density isocontour rendering at two different times for a fast wave. Block boundaries are illustrated using white lines. For our wave tests we start with a grid that has 3 × 3 × 3 blocks and refine the innermost block. Bottom panel: a plot of the L1 error as a function of the linear resolution N  x . H-AMR achieves second-order convergence for all waves irrespective of refinement boundaries.
Paper: H-AMR: A New GPU-accelerated GRMHD Code for Exascale Computing with 3D Adaptive Mesh Refinement and Local Adaptive Time Stepping
Authors: M. T. P. Liska, K. Chatterjee, D. Issa, D. Yoon, N. Kaaz, A. Tchekhovskoy, D. van Eijnatten, G. Musoke, C. Hesp, V. Rohoza, S. Markoff, A. Ingram, M. van der Klis
Figure caption: The computational performance of H-AMR in zone cycles per second on a 1503 uniform grid for various generations of Intel CPUs (Haswell and Skylake) and NVIDIA GPUs (Pascal, Volta, and Ampere). HT indicates that we make use of HyperThreading on Intel CPUs. A zone cycle includes both the predictor and corrector step in our second-order-accurate time-stepping routine.
Paper: H-AMR: A New GPU-accelerated GRMHD Code for Exascale Computing with 3D Adaptive Mesh Refinement and Local Adaptive Time Stepping
Authors: M. T. P. Liska, K. Chatterjee, D. Issa, D. Yoon, N. Kaaz, A. Tchekhovskoy, D. van Eijnatten, G. Musoke, C. Hesp, V. Rohoza, S. Markoff, A. Ingram, M. van der Klis
Figure caption: As previously presented in Liska et al. (2018), the spherical “base” grid in H-AMR has a varying number of blocks in φ. This prevents cell squeezing near the pole, speeding up high-resolution GRMHD simulations by up to 2 orders of magnitude without any loss in accuracy. AMR can increase the resolution of all blocks in this grid, including those adjacent to the polar axis.
Paper: H-AMR: A New GPU-accelerated GRMHD Code for Exascale Computing with 3D Adaptive Mesh Refinement and Local Adaptive Time Stepping
Authors: M. T. P. Liska, K. Chatterjee, D. Issa, D. Yoon, N. Kaaz, A. Tchekhovskoy, D. van Eijnatten, G. Musoke, C. Hesp, V. Rohoza, S. Markoff, A. Ingram, M. van der Klis
Figure caption: A code diagram of H-AMR. On the left (blue) are all tasks that are performed using OpenMP parallelized C functions. On the right (orange) are all tasks that are performed using GPU-accelerated CUDA kernels. Practically all of the computation is GPU accelerated, and the nonaccelerated parts in H-AMR typically do not form a performance bottleneck.
Paper: Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine-learning Methods
Authors: Hewei Zhang, Qin Li, Yanxing Yang, Ju Jing, Jason T. L. Wang, Haimin Wang, Zuofeng Shang
Figure caption: Performance of FI prediction using different models, i.e., random forest (a), LASSO (b), Gaussian process regression (c), linear regression (d), spline regression (e), and spline regression with SMOGN (f), is estimated by plotting dots at coordinates (predicted FI, observed FI). The reference line (yellow) highlights the ideal case wherein the prediction is strictly the same as the observation. Inner panels show the predicted FI without inverse processing vs. observed FI by power transformation.
Paper: Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine-learning Methods
Authors: Hewei Zhang, Qin Li, Yanxing Yang, Ju Jing, Jason T. L. Wang, Haimin Wang, Zuofeng Shang
Figure caption: Boxplots of the RMSE (orange) and MAE (blue) of the predictions on the (a) strong FIs (FI > 10), (b) weak FIs, and (c) all FIs. (d) The RMSE and MAE of the predictions on the strong FIs in 2017. The vertical dashed lines highlight the minimum values of the RMSE and MAE.
Paper: Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine-learning Methods
Authors: Hewei Zhang, Qin Li, Yanxing Yang, Ju Jing, Jason T. L. Wang, Haimin Wang, Zuofeng Shang
Figure caption: Correlation matrix that displays correlation coefficients for 25 SHARP parameters. The upper and lower triangles are correspondingly filled with colors and numeric values. Positive correlations are displayed in green and negative correlations in yellow. Color intensity of the cell is proportional to the correlation coefficients.
Paper: Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine-learning Methods
Authors: Hewei Zhang, Qin Li, Yanxing Yang, Ju Jing, Jason T. L. Wang, Haimin Wang, Zuofeng Shang
Figure caption: RMSE of predicted FI results from resampled data sets by SMOGN algorithm with fixed sampling ratios of minority to majority (i.e., 30:70, 35:75, 40:60, and 50:50), respectively. Standard deviations of RMSE are represented by red error bars.
Paper: Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine-learning Methods
Authors: Hewei Zhang, Qin Li, Yanxing Yang, Ju Jing, Jason T. L. Wang, Haimin Wang, Zuofeng Shang
Figure caption: The corresponding Borda count for each SHARP parameter based on the nine feature ranking results.
Paper: LSST Survey Strategies and Brown Dwarf Parallaxes
Authors: John E. Gizis, Peter Yoachim, R. Lynne Jones, Dylan Hilligoss, Jinbiao Ji
Figure caption: The parallax factors sampled in z and y bands at a representative location (0h, −20d) in the WFD survey. This point receives 345 visits in z and y. The shift in position at each epoch for each star will be its parallax (one over its distance in parsecs) times the parallax factor. Observations near +1 and −1 are necessary to fit the parallax. Top: the observations phased by year. Bottom: the observations as a function of time.
Paper: LSST Survey Strategies and Brown Dwarf Parallaxes
Authors: John E. Gizis, Peter Yoachim, R. Lynne Jones, Dylan Hilligoss, Jinbiao Ji
Figure caption: The surveyed volume in cubic parsecs and corresponding number of L and T dwarfs with parallax S/N ≥ 10 using the Best et al. (2021) space densities for the V1/7 baseline case.
Paper: LSST Survey Strategies and Brown Dwarf Parallaxes
Authors: John E. Gizis, Peter Yoachim, R. Lynne Jones, Dylan Hilligoss, Jinbiao Ji
Figure caption:  MAF histogram of the correlation between the parallax signal and differential color refraction (DCR) due to the atmosphere. Only z and y observations are used. Cases where the correlation is greater than 0.8 are a concern for separating differential color refraction and the parallax signal for very red sources, but the simulations show this is a not a major problem for current survey strategies.
Paper: LSST Survey Strategies and Brown Dwarf Parallaxes
Authors: John E. Gizis, Peter Yoachim, R. Lynne Jones, Dylan Hilligoss, Jinbiao Ji
Figure caption:  MAF sky map of the limiting distance for the new V2.0 baseline. Note the shift of WFD to low-extinction regions, including into the northern hemisphere, and the changed coverage of the Galactic bulge and plane.
Paper: H-AMR: A New GPU-accelerated GRMHD Code for Exascale Computing with 3D Adaptive Mesh Refinement and Local Adaptive Time Stepping
Authors: M. T. P. Liska, K. Chatterjee, D. Issa, D. Yoon, N. Kaaz, A. Tchekhovskoy, D. van Eijnatten, G. Musoke, C. Hesp, V. Rohoza, S. Markoff, A. Ingram, M. van der Klis
Figure caption: An illustration of a cell in the (x 1, x 2)-plane in H-AMR. The conserved quantities U are stored at cell centers, the fluxes F 1,2 and staggered magnetic field components B 1,2 are stored at cell faces, and the electric fields E 3 are stored at cell edges. The corresponding metric components g  μ ν  are stored for cell centers, cell faces, and cell corners. The conserved states are calculated directly from the primitive variables. The fluxes are calculated from the reconstructed primitive variables in a Riemann solver. The electric fields are calculated (in their simplest form) by taking an average of the neighboring fluxes. Typically, H-AMR modifies the calculation of the electric fields with a velocity upwinding routine as described in Gardiner & Stone (2005).
Paper: Improving the Spatial Resolution of Solar Images Based on an Improved Conditional Denoising Diffusion Probability Model
Authors: Wei Song, Wen Ma, Ying Ma, Xiaobing Zhao, Ganghua Lin
Figure caption: The resolution of the SR image in (a) is blurred to approximate the resolution of the HMI image. (a) The histogram of the residual image of the blurred SR image and HMI image. (b) The histogram of the prediction residual image of the SR image and the GST image. The green and red curves in (a) and (b) represent the residual values obtained from our results and the results of SAGAN, respectively.
Paper: Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine-learning Methods
Authors: Hewei Zhang, Qin Li, Yanxing Yang, Ju Jing, Jason T. L. Wang, Haimin Wang, Zuofeng Shang
Figure caption: The histogram on the left shows p-values of 25 SHARP parameters with respect to the significance test. The histogram on the right gives the correlation coefficient value of the rest 18 SHARP variables. The vertical dashed lines highlight critical values used to define parameters that are statistically significant (p-value = 0.01 in left panel) and linearly correlated (r = 0.5 in right panel).
Paper: Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine-learning Methods
Authors: Hewei Zhang, Qin Li, Yanxing Yang, Ju Jing, Jason T. L. Wang, Haimin Wang, Zuofeng Shang
Figure caption: Evolution of FI with respect to eight SHARP parameters, i.e., TOTUSJH, ABSNJZH, SAVNCPP, MEANPOT (first and second columns) and SHRGT45, MEANGAM, MEANALP, TOTFZ (third and fourth columns). The effect of power transformation is characterized by plotting the original data (blue) as well as the corresponding transformed data (red) in the same row.
Paper: Stellar Flyby Analysis for Spiral Arm Hosts with Gaia DR3
Authors: Linling Shuai, 琳玲 帅, Bin B. Ren, 彬 任, Ruobing Dong, 若冰 董, Xingyu Zhou, 星宇 周, Laurent Pueyo, Robert J. De Rosa, Taotao Fang, 陶陶 方, Dimitri Mawet
Figure caption: Gallery of spiral systems studied here for flyby analysis (color bar in log scale). The targets are sorted by decreasing physical extent from left to right, and top to bottom.
Paper: Stellar Flyby Analysis for Spiral Arm Hosts with Gaia DR3
Authors: Linling Shuai, 琳玲 帅, Bin B. Ren, 彬 任, Ruobing Dong, 若冰 董, Xingyu Zhou, 星宇 周, Laurent Pueyo, Robert J. De Rosa, Taotao Fang, 陶陶 方, Dimitri Mawet
Figure caption: Flowchart of the flyby framework. For a pair of stars, three conditions have to be satisfied for MCMC quantification of the time and distance of the closest approach.
Paper: Stellar Flyby Analysis for Spiral Arm Hosts with Gaia DR3
Authors: Linling Shuai, 琳玲 帅, Bin B. Ren, 彬 任, Ruobing Dong, 若冰 董, Xingyu Zhou, 星宇 周, Laurent Pueyo, Robert J. De Rosa, Taotao Fang, 陶陶 方, Dimitri Mawet
Figure caption: Relative distance as a function of past time for systems with recent close-in flyby events in Section 3.1. Top: Gaia DR3 6049152203067495808 (i.e., EM* SR 21 B) seen from EM* SR 21: the moderate uncertainty growth in (a) allows for the posterior calculation of the time of closest approach in (b), which shares the same vertical axis with the approach distance in (c). Bottom: Gaia DR3 3237512159787015936 (i.e., HD 34700 C) seen from HD 34700: the uncertainty expansion in (d) cannot give conclusive answers to the nearest-approach time in maximum likelihood in (e), in which the MCMC samples drift toward the prior limit of −104 yr. Note: The negative distances in (c) are physically implausible, yet they are the framework limit when sampling from normal distributions.
Paper: Stellar Flyby Analysis for Spiral Arm Hosts with Gaia DR3
Authors: Linling Shuai, 琳玲 帅, Bin B. Ren, 彬 任, Ruobing Dong, 若冰 董, Xingyu Zhou, 星宇 周, Laurent Pueyo, Robert J. De Rosa, Taotao Fang, 陶陶 方, Dimitri Mawet
Figure caption: Sketch of bivariate normal distribution in Appendix B. Point H is the position of the spiral host in its reference frame (i.e., origin). Point N is the relative location of a star neighbor seen from H, which is located at the center of the bivariate normal distribution in Equation (B4). The distribution along line ﹩\overleftrightarrow{{HN}}﹩ is a one-dimensional normal distribution (see Equation (B13)).
Paper: Stellar Flyby Analysis for Spiral Arm Hosts with Gaia DR3
Authors: Linling Shuai, 琳玲 帅, Bin B. Ren, 彬 任, Ruobing Dong, 若冰 董, Xingyu Zhou, 星宇 周, Laurent Pueyo, Robert J. De Rosa, Taotao Fang, 陶陶 方, Dimitri Mawet
Figure caption: Flyby of RW Aur B from RW Aur A with Gaia DR3. (a) Relative distance between the binary stars in the past 1000 yr. Panels (b) and (c) are the posterior distributions of the on-sky closest-approach time and separation, respectively. The closest approach is ﹩{d}_{{\rm{c}}.{\rm{a}}.}^{\left(B\right)}={148}_{-20}^{+20}﹩ au at ﹩{t}_{{\rm{c}}.{\rm{a}}.}^{\left(B\right)}=-{418}_{-38}^{+26}﹩ yr.
Paper: A Catalog of Quasar Properties from Sloan Digital Sky Survey Data Release 16
Authors: Qiaoya Wu, Yue Shen
Figure caption: Distribution of quasars in the redshift and luminosity space. The DR16Q sample is shown as black contours. The blue contours are for the SDSS DR7 quasars compiled in Shen et al. (2011). Enclosed percentiles are marked for each contour level.
Paper: A Catalog of Quasar Properties from Sloan Digital Sky Survey Data Release 16
Authors: Qiaoya Wu, Yue Shen
Figure caption: Upper panels: comparison between single-epoch virial BH masses estimated using the broad Mg II line and broad Hβ line. The mean (μ) and dispersion (σ) of the mass-difference distribution are noted on the top left of the right panel. Lower panels: comparison between virial BH masses estimated from C IV and Mg II. The contour levels correspond to the enclosed percentiles of the sample. While the BH masses are consistent between two line estimators for DR7 quasars (Shen et al. 2011), there are minor offsets in the mean BH mass between two estimators when extrapolating the same mass recipes to lower-luminosity DR16Q quasars.
Paper: A Catalog of Quasar Properties from Sloan Digital Sky Survey Data Release 16
Authors: Qiaoya Wu, Yue Shen
Figure caption: An example of a quality assessment plot for our global spectral-fitting approach. The top panel shows the continuum (orange) and Fe II (sky blue) model components; the red line is the global model combining all continuum and emission-line components. The light-blue crosses are pixels within the continuum and emission-line-fitting windows that are masked as absorption and excluded from the model fit. The gray line segments near the top of the panel indicate the windows used for the continuum + Fe II fit. The bottom panels present the emission-line fits for several line complexes, where the green (blue) lines represent broad (narrow) Gaussian components within that line complex.
Paper: A Catalog of Quasar Properties from Sloan Digital Sky Survey Data Release 16
Authors: Qiaoya Wu, Yue Shen
Figure caption: Redshift distributions of DR16Q quasars with large discrepancies between z DR16Q and z sys for the three subsamples shown in Figure 6. They roughly follow the same redshift distribution of the full DR16Q sample, suggesting no particular redshift failures (of z DR16Q) in certain regime. The numbers of quasars in these subsamples are shown in the top-right legend.
Paper: A Catalog of Quasar Properties from Sloan Digital Sky Survey Data Release 16
Authors: Qiaoya Wu, Yue Shen
Figure caption: Median composite spectra around several emission/stellar absorption lines of DR16Q quasars with large discrepancies between the default DR16Q redshift and the new systemic redshift, generated using the default DR16Q redshifts (left panels) and the improved systemic redshift (right panels). The new systemic redshifts perform much better than z DR16Q in the statistical sense.
Paper: A Catalog of Quasar Properties from Sloan Digital Sky Survey Data Release 16
Authors: Qiaoya Wu, Yue Shen
Figure caption: Median composite spectra around several emission/stellar absorption lines of the DR16Q sample, generated using the default DR16Q redshifts (left panels) and the improved systemic redshift (right panels), as a function of quasar luminosity. The improved systemic redshifts make the narrow-line features in the composite spectrum sharper, and the peaks of broad lines closer to their expected wavelengths (can be shifted from the systemic velocity).
Paper: A Catalog of Quasar Properties from Sloan Digital Sky Survey Data Release 16
Authors: Qiaoya Wu, Yue Shen
Figure caption: Distribution of quasars in the redshift and BH mass space. The DR16Q sample is shown in black contours; the SDSS DR7 quasars are shown in blue contours; the SDSS-RM sample (Shen et al. 2019) is plotted in pink points. The apparent discontinuity around z ∼ 2 is caused by the switch from Mg II BH masses to C IV masses, as discussed in Section 5.1.
Paper: A Catalog of Quasar Properties from Sloan Digital Sky Survey Data Release 16
Authors: Qiaoya Wu, Yue Shen
Figure caption: Distribution of quasars in the BH mass and bolometric luminosity space. The DR16Q sample is shown as black contours. The blue contours are for the SDSS DR7 quasars compiled in Shen et al. (2011). Enclosed percentiles are marked for each contour level. We caution that quasars with apparent Eddington ratios λ Edd > 1 are likely caused by uncertainties in the BH mass estimates.
Paper: A Catalog of Quasar Properties from Sloan Digital Sky Survey Data Release 16
Authors: Qiaoya Wu, Yue Shen
Figure caption: Distribution of DR16Q quasars in the plane of optical Fe II strength (denoted by R FeII  ≡ EWFeII 4434−4684/EWHβ,br ) vs. broad Hβ FWHM.
Paper: A Catalog of Quasar Properties from Sloan Digital Sky Survey Data Release 16
Authors: Qiaoya Wu, Yue Shen
Figure caption: Probability distribution (black) and cumulative distribution (red) of the velocity uncertainty of z sys for the DR16Q sample.
Paper: In the Trenches of the Solar–Stellar Connection. VI. Total EclipSS
Authors: Thomas Ayres, Derek Buzasi
Figure caption: Left panel: X-ray flux ratios, relative to the reference 0.1–2.4 keV, for other representative energy bands, according to the legend. Right panel: temperature-dependent energy conversion factors (ECFs) for the 0.1–2.4 keV band for several X-ray instruments relevant to this work. In the simplest limit, the observed flux is the product of the ECF, at a single coronal ﹩\mathrm{log}T﹩, and the X-ray count rate. More complex realizations convolve the ECF with a temperature-dependent coronal model. Diamonds refer to the two imagers on the historical 1990s ROSAT mission. Crossed dots are for the three contemporary Chandra camera systems utilized in the present study. Responses for the two HRC chips are for epoch 2020, while that of the ACIS-S CCD is for early in the Chandra mission prior to the recent severe decline in the soft sensitivity, caused by long-term buildup of contaminants on the cold optical blocking filter.
Paper: In the Trenches of the Solar–Stellar Connection. VI. Total EclipSS
Authors: Thomas Ayres, Derek Buzasi
Figure caption: H–R diagram of the Total EclipSS sample. Gray shaded band is the ZAMS along the lower edge and 50% MS lifetime along the upper edge. Solid curves are MS/post-MS evolutionary tracks, beginning at the Sun, and extending to hotter temperatures in steps of 0.1 M ⊙ (annotations are M/M ⊙). Dashed curves are pre-MS Hayashi tracks, shown for the Sun and several lower-mass models. Sample members are color coded by spectral type according to the legend at lower left. In the legend at the upper right, “EclipSS+” refers to the original EclipSS sample (the subsample dots are in colors with external dark ticks; the other EclipSS members are gray) plus the five related G-type dwarfs (unadorned, but color-coded dots). Reference sample members ξ Boo and 70 Oph are self-explanatory. Other reference stars are “AB” = α Cen, “P” = Procyon, and “ϵ” = ϵ Eri. The Sun is marked by an ⊙ symbol, partly obscured by several of the other points.
Paper: In the Trenches of the Solar–Stellar Connection. VI. Total EclipSS
Authors: Thomas Ayres, Derek Buzasi
Figure caption: Determination of optimum ECFs for Total EclipSS ROSAT and Chandra X-ray observations. Each curve represents a calculation of the stellar X-ray surface flux (on the y-axis) given the observed count rate (vertical width represents uncertainty in the CR) and a set of ECF values based on convolving temperature-dependent ECF curves through Wood EMD models. The latter are associated with specific X-ray surface fluxes (arrayed on the x-axis). The intersection with dark dashed 1:1 line indicates an optimum, self-consistent ﹩\mathrm{log}{F}_{{\rm{X}}}﹩, which in turn implies an optimum ECF value (in practice, interpolated in the ﹩\mathrm{log}{F}_{{\rm{X}}}﹩-parameterized model grid). Most of the slightly sloping curves are for Chandra HRC-I. Steeper relations are for ACIS-S and HRC-S. Several ROSAT PSPC and HRI examples are also interspersed.
Paper: In the Trenches of the Solar–Stellar Connection. VI. Total EclipSS
Authors: Thomas Ayres, Derek Buzasi
Figure caption: Normalized Wood et al. (2018) relative emission-measure distributions (EMDs) ordered by stellar X-ray surface flux ﹩\mathrm{log}{F}_{{\rm{X}}}﹩, and offset along the y-axis for clarity. EMDs highlighted in blue are the original coronal models. Those highlighted in red are extrapolated/interpolated versions.