adsabs/AstroCLIMB huggingface dataset:
Paper: Calibrating HMI Magnetograms Using an End-to-end Magnetograph Model
Authors: Gordon J. D. Petrie, Julián Blanco Rodríguez, Valentín Martínez Pillet, Han Uitenbroek, Philip H. Scherrer
Figure caption: Example HMI filter profiles for the six wavelength positions from the SOPHISM HMI instrument model, based on calibration data from the HMI archives, including phase maps for the tunable elements dating from 2015 March 25. The transmission functions in this plot were derived using disk-center pixels (0° viewing angle) from the phase and contrast maps for the five Lyot filters (one tunable) and the two Michelson interferometers from 2015 March 25. (The transmission functions for the blocker and front window are excluded here, but are included in the subsequent analysis.) This figure can be compared to Figure 6 of J. Schou et al. (2012). The filter profiles are similar for other viewing angles, derived from other positions on the phase maps, are very similar.
Paper: Calibrating HMI Magnetograms Using an End-to-end Magnetograph Model
Authors: Gordon J. D. Petrie, Julián Blanco Rodríguez, Valentín Martínez Pillet, Han Uitenbroek, Philip H. Scherrer
Figure caption: Output magnetograms BHMI from the SOPHISM HMI simulator for the viewing angles 0° and 41﹩\mathop{.}\limits^{\unicode{x000b0}}﹩4 and the representative spacecraft radial velocities indicated. The ground-truth data BMHD, pixelized, and replicated versions of the first and second images shown in Figure 3 with pixel size matching the HMI pixel size of 0﹩\mathop{.}\limits^{\unicode{x02033}}﹩5, are also shown for comparison.
Paper: Calibrating HMI Magnetograms Using an End-to-end Magnetograph Model
Authors: Gordon J. D. Petrie, Julián Blanco Rodríguez, Valentín Martínez Pillet, Han Uitenbroek, Philip H. Scherrer
Figure caption: Output magnetograms BHMI from the SOPHISM HMI simulator for the viewing angles 60° and 75﹩\mathop{.}\limits^{\unicode{x000b0}}﹩5 and the representative spacecraft radial velocities indicated. The ground-truth data BMHD, pixelized and replicated versions of the third and fourth images shown in Figure 3 with pixel size matching the HMI pixel size of 0﹩\mathop{.}\limits^{\unicode{x02033}}﹩5, are also shown for comparison.
Paper: Calibrating HMI Magnetograms Using an End-to-end Magnetograph Model
Authors: Gordon J. D. Petrie, Julián Blanco Rodríguez, Valentín Martínez Pillet, Han Uitenbroek, Philip H. Scherrer
Figure caption: LOS magnetograms from 2015 April 22 at 12:00 UT derived from the HMI Stokes data by us (top), and with the end-to-end calibration applied (bottom). The increase in magnetic field strength produced by the calibration is indicated by the composite color scale both within the active regions (red/blue) and outside them (yellow/green), at locations near the disk-center and near the limb. The closed dotted curves near the disk-center and near the south pole indicate the regions used in the analysis summarized in Figures 13 and 14.
Paper: Calibrating HMI Magnetograms Using an End-to-end Magnetograph Model
Authors: Gordon J. D. Petrie, Julián Blanco Rodríguez, Valentín Martínez Pillet, Han Uitenbroek, Philip H. Scherrer
Figure caption: Comparison of Stokes I (top panels) and V (bottom panels) profiles for the sunspot umbra (left panels), plage (middle panels), and quiet Sun (right panels) from the four viewing angles, based on the RH synthesis of the HMI Fe I spectral absorption line at 6173 Å averaged over the representative regions from the MHD data cube labeled in Figure 1. All profiles are normalized by the disk-center quiet-Sun intensity value IC. The four viewing angles are represented by solid (0°), dotted (41﹩\mathop{.}\limits^{\unicode{x000b0}}﹩4), dashed (60°), and dotted–dashed (75﹩\mathop{.}\limits^{\unicode{x000b0}}﹩5) lines.
Paper: Calibrating HMI Magnetograms Using an End-to-end Magnetograph Model
Authors: Gordon J. D. Petrie, Julián Blanco Rodríguez, Valentín Martínez Pillet, Han Uitenbroek, Philip H. Scherrer
Figure caption: Surface contour plots of BMHD as a function of BHMI and (spacecraft + surface differential rotation) radial velocity for the four viewing angles, 0° (top row), 41﹩\mathop{.}\limits^{\unicode{x000b0}}﹩4 (second row), 60° (third row), and 75﹩\mathop{.}\limits^{\unicode{x000b0}}﹩5 (bottom row); and for different classes of field, umbral (left column), penumbra (middle column), and quiet Sun (right column). The color table emphasizes the differences between calibration curves for strong and weak fields and for slow and fast radial velocities of both signs. These surfaces are used to construct look-up tables for the HMI magnetogram calibrations that follow.
Paper: SpecTE: Parameter Estimation for LAMOST Low-resolution Stellar Spectra Based on Denoising Pretraining
Authors: Xirong Zhao, Xiangru Li, Hui Li, Xianqi Liu
Figure caption: Summary of stellar parameter distributions and model consistency on the test set. (a) The distribution of stellar parameters in the Teff–﹩{\mathrm log}\,g﹩ space; the left and right subpanels correspond to the APOGEE DR17 catalog and SpecTE predictions, respectively. Point colors indicate [Fe/H] abundances, and the cyan, yellow, and red curves represent MIST stellar evolutionary tracks for an age of 7 Gyr. (b) illustrates the residuals between SpecTE predictions and APOGEE DR17 values, where the horizontal axis denotes APOGEE DR17 results and the vertical axis shows the differences. The dashed line at μ = 0 indicates theoretical consistency, with bias and dispersion labeled in the lower left. Point density is encoded by the color scale.
Paper: SpecTE: Parameter Estimation for LAMOST Low-resolution Stellar Spectra Based on Denoising Pretraining
Authors: Xirong Zhao, Xiangru Li, Hui Li, Xianqi Liu
Figure caption: Correlation analysis of stellar parameters and elemental abundances in the SpecTE-LAMOST Catalog. (a) The joint density distribution of 10,000 randomly selected stars from the SpecTE-LAMOST catalog. The color indicates the density of the samples. (b) The Pearson correlation coefficients between various parameters in the SpecTE-LAMOST catalog, with the numbers marking the specific correlation coefficient.
Paper: SpecTE: Parameter Estimation for LAMOST Low-resolution Stellar Spectra Based on Denoising Pretraining
Authors: Xirong Zhao, Xiangru Li, Hui Li, Xianqi Liu
Figure caption: Distributions of chemical homogeneity for different open clusters across various elements. The plot shows the chemical homogeneity (σ [X/H]) for 15 elements in six open clusters: Melotte 22, NGC 2632, NGC 2682, NGC 2168, NGC 2281, and NGC 1647. Clusters are distinguished by their color and marker shape.
Paper: SpecTE: Parameter Estimation for LAMOST Low-resolution Stellar Spectra Based on Denoising Pretraining
Authors: Xirong Zhao, Xiangru Li, Hui Li, Xianqi Liu
Figure caption: Dependencies of parameter estimation accuracy on spectral S/N and the spectral distribution in the S/N space. The results are computed from the LAMOST DR11 low-resolution spectral library.
Paper: SpecTE: Parameter Estimation for LAMOST Low-resolution Stellar Spectra Based on Denoising Pretraining
Authors: Xirong Zhao, Xiangru Li, Hui Li, Xianqi Liu
Figure caption: The dependencies on S/N of the parameter estimation uncertainty. The dots represent the uncertainty provided by SpecTE, and the length of the line segments centered on the dots indicates the uncertainty estimated from repeated observations (>5 times).
Paper: Hemispheric Distribution of Solar Active Regions during Solar Cycles 23–25
Authors: Yuxia Liu, Tingting Xu, Miao Wan, Linhua Deng, Xinhua Zhao, Shiyang Qi, Nanbin Xiang, Weihong Zhou
Figure caption: Regression fit lines for the monthly values (6 month average) of N-S asymmetry in the AR number across the different cycles. Upper panel: scatter plot of absolute asymmetry values over time; Lower panel: similar to the upper panel but corresponding to normalized asymmetry. Red dots represent actual data points, and the blue line indicates the fitted regression line. The number of data points (n), correlation coefficient (r), and fitted slope (k) are given on the plots.
Paper: Hemispheric Distribution of Solar Active Regions during Solar Cycles 23–25
Authors: Yuxia Liu, Tingting Xu, Miao Wan, Linhua Deng, Xinhua Zhao, Shiyang Qi, Nanbin Xiang, Weihong Zhou
Figure caption: Panel (a): cumulative counts of the AR parameters in the southern (red) and northern (blue) hemispheres during SC23. Panels (b), (c), and (d) are similar to panel (a), but correspond to the cumulative counts for SC24, SC25, and the entire period, respectively.
Paper: Hemispheric Distribution of Solar Active Regions during Solar Cycles 23–25
Authors: Yuxia Liu, Tingting Xu, Miao Wan, Linhua Deng, Xinhua Zhao, Shiyang Qi, Nanbin Xiang, Weihong Zhou
Figure caption: Same as Figures 7 and 8, but showing the regression lines of the N-S asymmetry in the AR flux across the different cycles.
Paper: Hemispheric Distribution of Solar Active Regions during Solar Cycles 23–25
Authors: Yuxia Liu, Tingting Xu, Miao Wan, Linhua Deng, Xinhua Zhao, Shiyang Qi, Nanbin Xiang, Weihong Zhou
Figure caption: Scatter plots of correlation coefficients between pairs of AR parameters across the different solar cycles. From top to bottom, the panels display the correlation between the AR number and area, the AR number and flux, and the AR area and flux, with each subplot labeled with the respective correlation coefficient (r) and the slope of the fitted line (k). Red dots indicate actual observed data points, while the blue line represents the fitted trend line.
Paper: Hemispheric Distribution of Solar Active Regions during Solar Cycles 23–25
Authors: Yuxia Liu, Tingting Xu, Miao Wan, Linhua Deng, Xinhua Zhao, Shiyang Qi, Nanbin Xiang, Weihong Zhou
Figure caption: Monthly distribution of sunspot numbers and areas. Top panel: the monthly average hemispheric sunspot numbers, with the northern (southern) hemisphere show in blue (red). Bottom panel: same as the top panel, but corresponds to sunspot areas, smoothed using a 13 month running average. The gray dashed lines represent the minimum times of the solar cycle.
Paper: Hemispheric Distribution of Solar Active Regions during Solar Cycles 23–25
Authors: Yuxia Liu, Tingting Xu, Miao Wan, Linhua Deng, Xinhua Zhao, Shiyang Qi, Nanbin Xiang, Weihong Zhou
Figure caption: Monthly distribution of the AR parameters (red lines) and sunspot numbers (blue lines), where solid lines represent the northern hemisphere and dashed lines represent the southern hemisphere. The gray dashed lines represent the minimum times of the solar cycle. All data are processed with a 13 month smoothing filter to remove high-frequency fluctuations.
Paper: Hemispheric Distribution of Solar Active Regions during Solar Cycles 23–25
Authors: Yuxia Liu, Tingting Xu, Miao Wan, Linhua Deng, Xinhua Zhao, Shiyang Qi, Nanbin Xiang, Weihong Zhou
Figure caption: Same as Figure 7, but showing the regression lines of the N-S asymmetry in the AR area across the different cycles.
Paper: Hemispheric Distribution of Solar Active Regions during Solar Cycles 23–25
Authors: Yuxia Liu, Tingting Xu, Miao Wan, Linhua Deng, Xinhua Zhao, Shiyang Qi, Nanbin Xiang, Weihong Zhou
Figure caption: Regression fit lines for the monthly values (6 month average) of N-S asymmetry in the three AR parameters during SC24. Left panel: the relationship between the three AR parameter asymmetries and time prior to 2013. Right panel: similar to the left panel, but corresponding to the period after 2014. Red dots represent actual data points, and the blue line indicates the fitted regression line. The number of data points (n), correlation coefficient (r), and fitted slope (k) are given on the plots.
Paper: Hemispheric Distribution of Solar Active Regions during Solar Cycles 23–25
Authors: Yuxia Liu, Tingting Xu, Miao Wan, Linhua Deng, Xinhua Zhao, Shiyang Qi, Nanbin Xiang, Weihong Zhou
Figure caption: Similar to Figure 5, but showing the comparison between the AR parameters and sunspot areas.
Paper: Calibrating HMI Magnetograms Using an End-to-end Magnetograph Model
Authors: Gordon J. D. Petrie, Julián Blanco Rodríguez, Valentín Martínez Pillet, Han Uitenbroek, Philip H. Scherrer
Figure caption: Full-surface synoptic magnetograms for CR 2163 constructed using the LOS magnetograms derived from HMI Stokes data by us (top) and the end-to-end calibrated LOS magnetograms (bottom). The increase in magnetic field strength produced by the calibration is indicated by the composite color scale both within the active regions (red/blue) and outside them (yellow/green), at low and high latitudes.
Paper: Calibrating HMI Magnetograms Using an End-to-end Magnetograph Model
Authors: Gordon J. D. Petrie, Julián Blanco Rodríguez, Valentín Martínez Pillet, Han Uitenbroek, Philip H. Scherrer
Figure caption: The top panel shows the mean polar radial field strength Br over the 3 days 2015 April 21–23 using the same three magnetogram sources as in Figure 13. The region used is indicated by the closed dotted curve in Figure 12 near the south pole. For this calculation, unlike the one shown in Figure 13, this region was fixed on the solar disk over the 3 day period, spanning −65° to −75° latitude and within ±15° of the central meridian. The middle panel shows the relative variation ﹩({B}_{r}-\bar{{B}_{r}})/| \bar{{B}_{r}}| ﹩ over this time. The bottom panel plots the SDO spacecraft radial orbital velocity against time.
Paper: Calibrating HMI Magnetograms Using an End-to-end Magnetograph Model
Authors: Gordon J. D. Petrie, Julián Blanco Rodríguez, Valentín Martínez Pillet, Han Uitenbroek, Philip H. Scherrer
Figure caption: Precalibration (﹩{B}_{{\rm{los}}}^{{\rm{HMI}}}﹩, left panel) and calibrated (﹩{B}_{{\rm{los}}}^{{\rm{cal}}}﹩, middle panel) HMI maps and the SP map (﹩{B}_{{\rm{los}}}^{{\rm{SP}}}﹩, right panel) of a network region at disk-center, all taken on 2015 May 3 at 0805 UT, are plotted on a common color scale for comparison. Overall, the end-to-end calibrated HMI map contains nearly 25% more flux than the SP map, and more than 2 times as much as the precalibration HMI map. Although the fields are increased everywhere in the HMI map by the end-to-end calibration, the SP map contains slightly more pixels of the highest field strengths due to Hinode’s superior spatial resolution.
Paper: Calibrating HMI Magnetograms Using an End-to-end Magnetograph Model
Authors: Gordon J. D. Petrie, Julián Blanco Rodríguez, Valentín Martínez Pillet, Han Uitenbroek, Philip H. Scherrer
Figure caption: The mean absolute (unsigned) radial IMF from the OMNI database is plotted against time. The five curves for the observed radial IMF component at 1 au, from the NASA OMNI database. The solid gray curve shows values of ∣Br∣ derived from hourly averages of signed Br measurements. The blue dashed curve shows 1 day averages of these ∣Br∣, and the red and green dotted–dashed curves show 7 and 27 day averages. The horizontal orange line shows the average ∣Br∣ value over CR 2163, which is 3.54 nT.
Paper: Calibrating HMI Magnetograms Using an End-to-end Magnetograph Model
Authors: Gordon J. D. Petrie, Julián Blanco Rodríguez, Valentín Martínez Pillet, Han Uitenbroek, Philip H. Scherrer
Figure caption: Histograms of the three data sets shown in Figure 18, with 20 G bins: the precalibration (﹩{B}_{{\rm{los}}}^{{\rm{HMI}}}﹩, blue histogram) and calibrated (﹩{B}_{{\rm{los}}}^{{\rm{cal}}}﹩, red histogram) HMI maps and the SP map (﹩{B}_{{\rm{los}}}^{{\rm{SP}}}﹩, green histogram). The precalibration HMI histogram is lower than the other two in every bin, except for the weakest one, because these data are generally the weakest both inside and outside the flux concentrations. The calibrated HMI histogram has highest values in all bins between 20 and 340 G, bar one. Above 340 G, the SP histogram is higher, reflecting the SP’s superior spatial resolution of the highly concentrated flux tubes.
Paper: Calibrating HMI Magnetograms Using an End-to-end Magnetograph Model
Authors: Gordon J. D. Petrie, Julián Blanco Rodríguez, Valentín Martínez Pillet, Han Uitenbroek, Philip H. Scherrer
Figure caption: Coronal PFSS models extrapolated from the two synoptic maps shown in Figure 15. The contours indicate the radial magnetic flux distribution on the outer boundary of the model, here set to the standard radial distance of rSS = 2.5r⊙. Contour labels indicate radial field strengths in gauss, which are generally between 1.5× and 2× higher with the calibration.
Paper: Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web
Authors: Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song, Zesen Lin, Yao Dai, Xu Kong
Figure caption: Schematic diagram of UML clustering process: Upon completing data preprocessing (i.e., Step 1), we employ both the ConvNeXt model and the ViT model to encode the data, thereby extracting compelling features (see Step 2). The encoding results from identical data instances are used to construct positive sample pairs, while the remaining encodings from the ViT model serve to form a negative sample set. We subsequently apply the contrastive loss (CL) method to minimize loss, which helps in identifying critical features (Step 3). For the final determination of labels in Step 4, we adopt a voting mechanism based on a bagging clustering model, supplemented by manual visual adjustments to ensure precision. This approach leverages the strengths of two advanced models, integrating automated learning with human oversight to deliver a precise and reliable feature identification and classification solution.
Paper: Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web
Authors: Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song, Zesen Lin, Yao Dai, Xu Kong
Figure caption: Raincloud plots of Sérsic index (left panel) and effective radius (right panel) for different types of massive galaxies. The “cloud” plots show the distribution of kernel densities in the data; the “rain” plots show the distribution of individual values in the data set. For the box plot, the lower and upper boundaries of the box represent the first and third quartiles, respectively. The black line in the middle of the box represents the median, and the upper and lower whiskers of the box represent the minimum and maximum values. It can be seen that the Sérsic index of galaxies gradually decreases from SPHs to IRRs, while the effective radius gradually increases.
Paper: Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web
Authors: Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song, Zesen Lin, Yao Dai, Xu Kong
Figure caption: Frameworks of the ConvNeXt (left) and ViT (right) models.
Paper: Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web
Authors: Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song, Zesen Lin, Yao Dai, Xu Kong
Figure caption: Example galaxies belonging to the five morphological classes of SPHs, ETDs, LTDs, IRRs, and UNCs, from left to right. SPH galaxies are characterized by a distinct spherical shape and centrally concentrated brightness. ETD galaxies feature a prominent central sphere and a relatively smaller disklike structure. LTD galaxies are defined by their well-defined spiral arms and a compact central sphere. IRR galaxies have an irregular structure, while UNC galaxies are affected by an extremely low signal-to-noise ratio, which makes it difficult to identify the embedded information. Redshifts of individual galaxies are also given.
Paper: Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web
Authors: Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song, Zesen Lin, Yao Dai, Xu Kong
Figure caption: UMAP visualization of the complete classification results. Panel (a) shows the two-dimensional mapping results for all 32,922 data points across 10 classes, while panel (b) displays contour lines corresponding to the 20%, 50%, and 80% well-classified samples for these 10 classes. Panel (c) presents the two-dimensional mapping results for all data points across five classes, with panel (d) also showing contour lines for the 20%, 50%, and 80% well-classified samples for these five classes.
Paper: Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web
Authors: Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song, Zesen Lin, Yao Dai, Xu Kong
Figure caption: Random selection of 100 images from each of the 10 machine clusters for display. We presented all 10 clustering results, which are characterized by small differences in the morphology of galaxies within the group and large differences in the morphology of galaxies outside the group. Therefore, we can visually classify each component into one of the five types (SPH, ETD, LTD, IRR, and UNC). Due to the limited number of categories, the visual efficiency is greatly improved.
Paper: Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web
Authors: Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song, Zesen Lin, Yao Dai, Xu Kong
Figure caption: Raincloud plots of G (left panel) and M20 (right panel) for different types of massive galaxies, with the black lines in the boxes representing the median values for the different types. It can be seen that from SPHs to IRRs, the G of galaxies gradually decreases, while the M20 gradually increases.
Paper: Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web
Authors: Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song, Zesen Lin, Yao Dai, Xu Kong
Figure caption: Recall (left) and precision (right) of the GoogLeNet model. Note that both metrics have an overall average exceeding 92%, indicating the model’s excellent performance in distinguishing between different galaxies.
Paper: Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web
Authors: Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song, Zesen Lin, Yao Dai, Xu Kong
Figure caption: Raincloud plots of C (left panel) and Ψ (right panel) for different types of massive galaxies, with the black lines in the boxes representing the median values for the different types. It can be seen that from SPHs to IRRs, the C of galaxies gradually decreases, while the effective Ψ gradually increases.
Paper: The First Insight-HXMT Localization Catalog of Short-duration Gamma-Ray Bursts: The First Six Years
Authors: Shuo Xiao, Yan-Ting Zhang, Yue Wang, Yan-Qiu Zhang, Shao-Lin Xiong, Shuang-Nan Zhang, Shi-Jie Zheng, Jin-Peng Zhang, Peng Zhang, Cheng-Kui Li, Xiao-Bo Li, Shu-Xu Yi, Zhen Zhang, Bing Li, Hua Feng, Cong-Zhan Liu, Ming-Yu Ge, Xu-Fang Li, Zheng-Wei Li, Yu-Peng Xu, Xiang-Yang Wen, Jian-Chao Sun, Lian Tao, Qi-Bin Yi, Ce Cai, Yi Zhao, Wang-Chen Xue, Jia-Cong Liu, Chao Zheng, Chen-Wei Wang, Wen-Jun Tan, You-Li Tuo, Li-Ming Song, Fang-Jun Lu, Ti-Pei Li
Figure caption: Upper panel: the annulus obtained from the time-delay localization for GRB 230709C (trigger time UTC 2023-07-09T17:38:27.00) by HXMT-GBM, the localization circles obtained by GBM alone, and the Earth shadow of the two satellites are shown, respectively. Lower panel: the final localization map obtained by combining the probability results in HEALPix, the colors from light to dark represent the probability from high to low. The joint localization results of 1σ, 2σ, and 3σ improve the accuracy (size of the localization error area) compared to GBM-alone localization by 49%, 67%, and 81%, respectively.