adsabs/AstroCLIMB huggingface dataset:
Paper: Properties of Flare Quasiperiodic Pulsations Based on a New TESS Flare Catalog
Authors: Yinpeng Wang, Li-yun Zhang, Tianhao Su, Prabhakar Misra, Xianming L. Han
Figure caption: Distribution of flare energy vs. duration for different flare samples, along with linear regression fits. Panel (a) shows the power-law slope fitting of flare samples obtained using TESS 2 minute cadence light-curve data but with different flare detection methods. Panel (b) compares the flare sample from Figure 9(b) in Z.-L. Tu et al. (2021) (i.e., H. Maehara et al. 2015; K. Namekata et al. 2017; Z. Kövári et al. 2020 and Z.-L. Tu et al. 2020) with the flare sample of solar-like stars in this work, where the latter has been set with a lower-energy limit of 1033 erg. Panel (c) is identical to panel (b), with the only difference being the removal of the energy lower limit. Panels (d), (e), (f), (g), (h), and (i), respectively, compare the three samples from panel (a) across different evolutionary stages and spectral types. In each panel, different symbols and colors are provided to distinguish between the different samples.
Paper: Properties of Flare Quasiperiodic Pulsations Based on a New TESS Flare Catalog
Authors: Yinpeng Wang, Li-yun Zhang, Tianhao Su, Prabhakar Misra, Xianming L. Han
Figure caption: The energy distribution plots of flares categorized by different subclasses in log–log scale. For the entire flare sample, the peak of the distribution corresponds to flare energies between 1033 and 1034 erg. When analyzing by subclasses, it is evident that single stars, main-sequence stars, and KM-type stars contribute the largest number of flares. Additionally, within the same classification, the peak energy of the distribution for different subclasses exhibits certain patterns, which are described in detail in the main text.
Paper: Properties of Flare Quasiperiodic Pulsations Based on a New TESS Flare Catalog
Authors: Yinpeng Wang, Li-yun Zhang, Tianhao Su, Prabhakar Misra, Xianming L. Han
Figure caption: The distribution of flaring stars in the Gaia Gbp_rp vs. MG parameter space. In the top panel, points in different colors represent the distribution density of flaring stars (i.e., the number of flaring stars), with a normalized color bar for mapping. The histograms at the top and right correspond to the distribution of the entire TESS star sample in terms of Gaia Gbp_rp and MG, respectively. These histograms are primarily used to distinguish the regions with higher flaring star density in the Gaia Gbp_rp vs. MG distribution. In the bottom panel, different colors represent the proportion of flare stars relative to the total number of stars in the corresponding region.
Paper: Properties of Flare Quasiperiodic Pulsations Based on a New TESS Flare Catalog
Authors: Yinpeng Wang, Li-yun Zhang, Tianhao Su, Prabhakar Misra, Xianming L. Han
Figure caption: The FFD fitting for different subcategories. Here, the power index value of α was obtained by fitting the energies greater than or equal to the minimum flare energies (i.e., Ebolmin in each panel) represented by the red vertical dashed lines using the MCMC method. The green error bars represent the statistical errors, which were calculated by taking the square root of the total number of flares in the corresponding energy range.
Paper: Properties of Flare Quasiperiodic Pulsations Based on a New TESS Flare Catalog
Authors: Yinpeng Wang, Li-yun Zhang, Tianhao Su, Prabhakar Misra, Xianming L. Han
Figure caption: The distribution of flare amplitude and flare duration. To facilitate inspection, the subplots in the upper-right corner of both panels provide a zoomed-in view of the distribution for larger parameter values. The distribution profiles for the two parameters are similar. In the first interval, where flare amplitude or duration is minimal, the proportion of these flares is not the highest. This is due to observational limitations and the fact that the flare identification method does not detect all smaller flares. Starting from the second interval, the number of flares in each subsequent interval gradually decreases.
Paper: Properties of Flare Quasiperiodic Pulsations Based on a New TESS Flare Catalog
Authors: Yinpeng Wang, Li-yun Zhang, Tianhao Su, Prabhakar Misra, Xianming L. Han
Figure caption: The distribution of flaring and nonflaring stars in the celestial sphere for the TESS sample. For nonflaring stars, we performed a 0.1 sampling and mapped the density of point distribution using a color bar. In regions with repeated observations or longer total observation times, such as the overlap regions near the TESS orbital poles, the distribution of flaring stars is significantly higher compared to other areas.
Paper: Properties of Flare Quasiperiodic Pulsations Based on a New TESS Flare Catalog
Authors: Yinpeng Wang, Li-yun Zhang, Tianhao Su, Prabhakar Misra, Xianming L. Han
Figure caption: The distribution plots of flare rise time and decay time. The curves represent the log-normal fits to the distributions. For the flare rise time, the fitted curve is steeper, indicating that the distribution of rise times is more concentrated, suggesting that most flares reach their flux peak within a relatively short time. In contrast, for the flare decay time, the fitted curve is relatively flatter, showing that the distribution of decay times is more dispersed.
Paper: Properties of Flare Quasiperiodic Pulsations Based on a New TESS Flare Catalog
Authors: Yinpeng Wang, Li-yun Zhang, Tianhao Su, Prabhakar Misra, Xianming L. Han
Figure caption: The stacked diagram of flaring and nonflaring stars under different subclasses. Blue represents nonflaring stars, and red represents flaring stars. In the upper panel, each subclass of flaring and nonflaring stars is labeled with the corresponding red and black numbers. The lower panel provides a zoomed-in view where the y-axis is close to 0. The numbers on each subclass represent the proportion of flaring stars within that specific subclass.
Paper: Properties of Flare Quasiperiodic Pulsations Based on a New TESS Flare Catalog
Authors: Yinpeng Wang, Li-yun Zhang, Tianhao Su, Prabhakar Misra, Xianming L. Han
Figure caption: The top two panels show the light curves and flare distribution of TIC 353011257. In both Sectors 40 and 54, TIC 353011257 experienced a large number of flare events, including several superflares. The bottom panel illustrates flares with energies exceeding 1040 erg in the flare sample of this work, corresponding to the largest flare in Sector 40.
Paper: Foreground Removal in Ground-based CMB Observations Using a Transformer Model
Authors: Ye-Peng Yan, Si-Yu Li, Yang Liu, Jun-Qing Xia, Hong Li
Figure caption: The results of foreground removal using the CMBFSCNN method at the map level. The residual maps indicate the differences between the recovered noisy CMB map and the target map.
Paper: Foreground Removal in Ground-based CMB Observations Using a Transformer Model
Authors: Ye-Peng Yan, Si-Yu Li, Yang Liu, Jun-Qing Xia, Hong Li
Figure caption: Recovered power spectra of the E-mode (EE) and B-mode (BB) of the CMB, incorporating contributions from noise. The simulated power spectra were calculated from the beam-convolved CMB map plus a noise map at a frequency of 95 GHz. The recovered power spectra were derived from the noisy CMB maps, which were obtained through the application of the TCMB method on multiband observational data. ΔDℓ represents the difference between the recovered power spectrum and the target power spectrum, ΔDℓ = Dℓ,recovered − Dℓ,true. The black dashed lines represent the power spectra calculated using the pure CMB map. The length of each ℓ bin is set to be 30.
Paper: Foreground Removal in Ground-based CMB Observations Using a Transformer Model
Authors: Ye-Peng Yan, Si-Yu Li, Yang Liu, Jun-Qing Xia, Hong Li
Figure caption: Similar to Figure 5, this figure presents the results of foreground removal based on the CMBFSCNN method. To facilitate comparison with the TCMB method, we have also included the results obtained using the TCMB method. ﹩{\sigma }_{{D}_{\ell }}﹩ represents the magnitude of the error bars.
Paper: Foreground Removal in Ground-based CMB Observations Using a Transformer Model
Authors: Ye-Peng Yan, Si-Yu Li, Yang Liu, Jun-Qing Xia, Hong Li
Figure caption: Similar to Figure 6, this figure presents the results of foreground removal based on the CMBFSCNN method. To facilitate comparison with the TCMB method, we have also included the results obtained using the TCMB method.
Paper: Foreground Removal in Ground-based CMB Observations Using a Transformer Model
Authors: Ye-Peng Yan, Si-Yu Li, Yang Liu, Jun-Qing Xia, Hong Li
Figure caption: Map-level foreground removal results obtained using the TCMB and CMBFSCNN methods for Test Set 1, where the foreground model in the test data set exhibits a deliberate mismatch with the training data set’s foreground model.
Paper: Foreground Removal in Ground-based CMB Observations Using a Transformer Model
Authors: Ye-Peng Yan, Si-Yu Li, Yang Liu, Jun-Qing Xia, Hong Li
Figure caption: CMB EE and BB power spectra estimated from TCMB foreground cleaning pipelines. The black line represents the power spectra calculated from the pure CMB map with the UNP mask.
Paper: Foreground Removal in Ground-based CMB Observations Using a Transformer Model
Authors: Ye-Peng Yan, Si-Yu Li, Yang Liu, Jun-Qing Xia, Hong Li
Figure caption: Similar to Figure 9, this figure presents the recovered power spectra obtained using the TCMB and CMBFSCNN methods for Test Set 1.
Paper: Foreground Removal in Ground-based CMB Observations Using a Transformer Model
Authors: Ye-Peng Yan, Si-Yu Li, Yang Liu, Jun-Qing Xia, Hong Li
Figure caption: Similar to Figure 9, this figure presents the recovered power spectra obtained using the TCMB and CMBFSCNN methods for Test Set 2.
Paper: Foreground Removal in Ground-based CMB Observations Using a Transformer Model
Authors: Ye-Peng Yan, Si-Yu Li, Yang Liu, Jun-Qing Xia, Hong Li
Figure caption: The CNN method requires the transformation of the HEALPix spherical sky map into a flat map. The regions of the sky that remain unobserved are filled with zeros.
Paper: Foreground Removal in Ground-based CMB Observations Using a Transformer Model
Authors: Ye-Peng Yan, Si-Yu Li, Yang Liu, Jun-Qing Xia, Hong Li
Figure caption: We augmented the training set by incorporating the d10 thermal dust emission model and retrained the network using this augmented data set. We applied this trained network to perform foreground removal on Test Set 2. This figure presents the recovered power spectra obtained through this procedure.
Paper: Foreground Removal in Ground-based CMB Observations Using a Transformer Model
Authors: Ye-Peng Yan, Si-Yu Li, Yang Liu, Jun-Qing Xia, Hong Li
Figure caption: Similar to Figure 6, however, the noise levels in the testing data set have been altered in this figure.
Paper: SIP-IFVM: An Observation-based Magnetohydrodynamic Model of a Coronal Mass Ejection
Authors: Hao P. Wang, Jin H. Guo, S. Poedts, A. Lani, L. Linan, T. Baratashvili, Li P. Yang, H.-J. Jeong, Wen W. Wei, Cai X. Li, Y. Yang, Yu C. Li, Hao Wu, Yang Guo, B. Schmieder
Figure caption: Illustration of the RBSL flux-rope axis path derived from the original observed (a) and the smoothed (b) sample points. The coordinates of the observed sample points are provided in the Appendix. Panels (c), (d), and (e) show selected magnetic field lines of the flux rope and the background corona, viewed from three mutually perpendicular perspectives. The x-axis is defined as the intersection of the solar equator and the meridional plane passing through the midpoint between the two footpoints of the axis path on the solar surface, pointing to the heliocentric. The z-axis is aligned with the Sun’s north rotational axis, and the right-hand rule determines the y-axis. The contour on the solar surface also displays the radial magnetic field strength in gauss.
Paper: SIP-IFVM: An Observation-based Magnetohydrodynamic Model of a Coronal Mass Ejection
Authors: Hao P. Wang, Jin H. Guo, S. Poedts, A. Lani, L. Linan, T. Baratashvili, Li P. Yang, H.-J. Jeong, Wen W. Wei, Cai X. Li, Y. Yang, Yu C. Li, Hao Wu, Yang Guo, B. Schmieder
Figure caption: Distribution of the radial plasma velocity Vr, in units of kilometers per second, and proton number density N, in units of 103 cm−3, at 20 Rs (top). The middle panels show the relative differences in radial velocity and plasma density, denoted by RDVr and RDρ, respectively, between the coronal simulation results calculated by the conventional and extended magnetic field decomposition approaches. The orange lines mean MNLs (Br = 0). The bottom panels present the white-light pB image ranging from 2.5 Rs to 15 Rs synthesized from the simulation results calculated by the extended magnetic field decomposition method (bottom left), and the corresponding observed pB image from COR2/STEREO-A (bottom right).
Paper: SIP-IFVM: An Observation-based Magnetohydrodynamic Model of a Coronal Mass Ejection
Authors: Hao P. Wang, Jin H. Guo, S. Poedts, A. Lani, L. Linan, T. Baratashvili, Li P. Yang, H.-J. Jeong, Wen W. Wei, Cai X. Li, Y. Yang, Yu C. Li, Hao Wu, Yang Guo, B. Schmieder
Figure caption: Distribution of the radial magnetic field at 1.015 Rs in the background corona (top right), and at 0, 1, 2, and 3 hr (middle and bottom) of the CME simulation, alongside the synoptic map of EUV observations from the 193 Å channel of AIA on board SDO for CR 2111 (top left). The white lines overlaid on the radial magnetic field contours indicate the boundaries of the open-field regions.
Paper: SIP-IFVM: An Observation-based Magnetohydrodynamic Model of a Coronal Mass Ejection
Authors: Hao P. Wang, Jin H. Guo, S. Poedts, A. Lani, L. Linan, T. Baratashvili, Li P. Yang, H.-J. Jeong, Wen W. Wei, Cai X. Li, Y. Yang, Yu C. Li, Hao Wu, Yang Guo, B. Schmieder
Figure caption: Evolution of the base-10 logarithms of the minimum values of ﹩{\beta }_{1}=\tfrac{p}{0.5\,| {{\boldsymbol{B}}}_{1}{| }^{2}}﹩ and ﹩\beta =\tfrac{p}{0.5\,| {\boldsymbol{B}}{| }^{2}}﹩ within the entire computational domain, denoted by ﹩{\mathrm{log}}_{10}({\beta }_{1,\min })﹩ and ﹩{\mathrm{log}}_{10}({\beta }_{\min })﹩, respectively. The left and right panels denote the evolution during the quasi-steady-state coronal simulation and the time-varying CME simulation, along with a close-up view of the profile during the first 4 × 10−4 hr of the CME simulation (middle). The solid and dashed lines represent the evolution of ﹩{\mathrm{log}}_{10}({\beta }_{1,\min })﹩ and ﹩{\mathrm{log}}_{10}({\beta }_{{\rm{\min }}})﹩, respectively, while gray and black lines indicate results calculated using the extended and conventional magnetic field decomposition approaches.
Paper: SIP-IFVM: An Observation-based Magnetohydrodynamic Model of a Coronal Mass Ejection
Authors: Hao P. Wang, Jin H. Guo, S. Poedts, A. Lani, L. Linan, T. Baratashvili, Li P. Yang, H.-J. Jeong, Wen W. Wei, Cai X. Li, Y. Yang, Yu C. Li, Hao Wu, Yang Guo, B. Schmieder
Figure caption: 3D view of the magnetic field topology during the CME simulation. The solid lines denote magnetic field lines traced from the magnetic field in the region defined by ﹩r\in \left(1.035\,{R}_{s},\,20{R}_{s}\right)﹩, ﹩\theta \in \left(22\mathop{.}\limits^{^\circ }5,\,157\mathop{.}\limits^{^\circ }5\right)﹩, and ﹩\phi \in \left({\phi }_{\,\rm{FR}}-135^\circ ,{\phi }_{\rm{FR}\,}+135^\circ \right)﹩, which encloses the observation-based flux rope. Here ﹩{\phi }_{\,\rm{FR}\,}=163\mathop{.}\limits^{^\circ }8﹩ denotes the longitude of the midpoint between the two footpoints of the flux-rope axis path. Rows 1–4 correspond to the CME simulation results at 1, 2, 3, and 4 hr, respectively. The magnetic field lines shown in the left, middle, and right panels are viewed from the directions ﹩\left(\theta ,\phi \right)=\left(90^\circ ,{\phi }_{\,\rm{FR}\,}\right)﹩, ﹩\left(\theta ,\phi \right)=\left(90^\circ ,{\phi }_{\,\rm{FR}\,}+90^\circ \right)﹩, and along the z-axis, respectively. These three viewing directions are mutually orthogonal, and the magnetic field lines in each panel are traced from the same set of seed points.
Paper: SIP-IFVM: An Observation-based Magnetohydrodynamic Model of a Coronal Mass Ejection
Authors: Hao P. Wang, Jin H. Guo, S. Poedts, A. Lani, L. Linan, T. Baratashvili, Li P. Yang, H.-J. Jeong, Wen W. Wei, Cai X. Li, Y. Yang, Yu C. Li, Hao Wu, Yang Guo, B. Schmieder
Figure caption: 2D timing diagrams of the base-10 logarithms of the simulated plasma number density ﹩{\mathrm{log}}_{10}(N)﹩ (a), (e), with N in units per cubic centimeter, of the radial velocity Vr (b), (f), in unit of kilometers per second, of the base-10 logarithms of plasma β (c), (g), denoted by ﹩{\mathrm{log}}_{10}(\beta )﹩, and of the temperature T (d), (h), in units of 105 K, along two selected lines ranging from 1.4 Rs to 20 Rs over the first 6 hr the CME simulation. The first row shows the simulation results along the Sun-to-Earth line, while the second row shows the simulation results along 30°N on the Sun–Earth meridian plane.
Paper: SIP-IFVM: An Observation-based Magnetohydrodynamic Model of a Coronal Mass Ejection
Authors: Hao P. Wang, Jin H. Guo, S. Poedts, A. Lani, L. Linan, T. Baratashvili, Li P. Yang, H.-J. Jeong, Wen W. Wei, Cai X. Li, Y. Yang, Yu C. Li, Hao Wu, Yang Guo, B. Schmieder
Figure caption: Evolution of radial velocity Vr (a), (d), (g), in units of kilometers per second, proton number density N (b), (e), (h), in units of 105 cm−3, and temperature T (c), (f), (i), in units of 105 K at 3 Rs. The first, second, and third rows denote the simulation results at 1, 2, and 3 hr of the CME simulation.
Paper: SIP-IFVM: An Observation-based Magnetohydrodynamic Model of a Coronal Mass Ejection
Authors: Hao P. Wang, Jin H. Guo, S. Poedts, A. Lani, L. Linan, T. Baratashvili, Li P. Yang, H.-J. Jeong, Wen W. Wei, Cai X. Li, Y. Yang, Yu C. Li, Hao Wu, Yang Guo, B. Schmieder
Figure caption: White-light pB images observed from COR1 (a), (b) and COR2 (c), (d) on board STEREO-A, corresponding pB images synthesized from simulation results (e)–(h), along with the simulated radial velocity along the same line of sight as STEREO-A ranging from 1 Rs to 4 Rs (i), (j) and from 1 Rs to 15 Rs (k), (l). The black solid lines overlaid on the radial velocity contours represent selected magnetic field lines.
Paper: Foreground Removal in Ground-based CMB Observations Using a Transformer Model
Authors: Ye-Peng Yan, Si-Yu Li, Yang Liu, Jun-Qing Xia, Hong Li
Figure caption: The results of foreground removal using the TCMB method at the map level. The upper panels illustrate the recovery of the Q map, while the lower panels depict the recovery of the U map. The simulated maps are a beam-convolved CMB map plus a noise map at a frequency of 95 GHz. The recovered maps correspond to the noisy CMB maps that were recovered using the TCMB method from multiband observational data. The residual maps indicate the differences between the recovered map and the target map.
Paper: Foreground Removal in Ground-based CMB Observations Using a Transformer Model
Authors: Ye-Peng Yan, Si-Yu Li, Yang Liu, Jun-Qing Xia, Hong Li
Figure caption: Inverse standard deviation of the polarization noise at frequencies of 95 and 150 GHz, calculated at a resolution of Nside = 1024. The gray line indicates the boundaries of the UNP mask.
Paper: Foreground Removal in Ground-based CMB Observations Using a Transformer Model
Authors: Ye-Peng Yan, Si-Yu Li, Yang Liu, Jun-Qing Xia, Hong Li
Figure caption: Orthographic view of the HEALPix partition of the sphere. The resolution parameter of the map is set to NSIDE = 8. The black points and lines represent the center positions and boundaries of the map pixels, respectively. The red lines denote the boundaries of the defined patch, which has its size parameter set to NSIDEP = 2.
Paper: Foreground Removal in Ground-based CMB Observations Using a Transformer Model
Authors: Ye-Peng Yan, Si-Yu Li, Yang Liu, Jun-Qing Xia, Hong Li
Figure caption: The network architecture for TCMB.
Paper: Expanding the Ice Inventory of NGC 1333 IRAS 2A with INDRA Using JWST Observations: Tracing Organic Refractories and Beyond
Authors: Prathap Rayalacheruvu, Liton Majumdar, W. R. M. Rocha, Michael E. Ressler, Pabitra Ranjan Giri, S. Maitrey, Karen Willacy, D. C. Lis, Yuan Chen, P. D. Klaassen
Figure caption: Best-fit decomposition of the MIRI spectrum of IRAS 2A, highlighting the contributions from different ice components. The observed optical depth spectrum is shown in solid black across all panels. Each contributing component is displayed using different colors, markers, and line styles. Top-left panel: contributions from ice components bearing dominant H2O bands. These components have absorption features spanning from 5–20 μm. Their cumulative contribution to the observed spectrum is shown in dashed red. Top-right panel: contributions from ices bearing CO2 bands. These components have absorption features spanning from 14–17 μm. Their cumulative contribution to the observed spectrum is shown in dashed red. Bottom-left panel: contributions from ice components that include organic ice mixtures, ions, and organic residue. These components have significant absorption features spanning from 5–11 μm. Their combined contribution to the overall spectrum is shown in dashed red. Bottom-right panel: a summary panel showing the individual contributions (shown in solid colors) to the observed spectrum from the three categories: H2O band–bearing ices, CO2 band–bearing ices, and organic ice mixtures along with ions and organic residue.
Paper: Expanding the Ice Inventory of NGC 1333 IRAS 2A with INDRA Using JWST Observations: Tracing Organic Refractories and Beyond
Authors: Prathap Rayalacheruvu, Liton Majumdar, W. R. M. Rocha, Michael E. Ressler, Pabitra Ranjan Giri, S. Maitrey, Karen Willacy, D. C. Lis, Yuan Chen, P. D. Klaassen
Figure caption: Global ice-fitting results across the entire MIRI spectrum (5–25 μm). The top panel shows the observed optical depth spectrum (solid black) alongside the modeled optical depth spectrum (solid red), which represents the contribution from 15 best-fit ice components. Each contributing component is displayed using different colors, markers, and line styles and are mentioned in the middle panel as a legend. The zoomed-in version of the contribution from select components (weak components) are shown in the inset plot. The bottom panel presents the residuals corresponding to the fit. Note that organic residue refers to the residue spectra obtained by irradiating H2O:NH3:CH3OH:CO:CO2 ice that shows broad features corresponding to various functional groups, including carbonyl (−C=O), carboxylate (−COO−), amine (–NH2), and –CN containing species along with hexamethylenetetramine (HMT) and polyoxymethylene (POM). Abbreviations: gsc. = grain-shape corrected.
Paper: Expanding the Ice Inventory of NGC 1333 IRAS 2A with INDRA Using JWST Observations: Tracing Organic Refractories and Beyond
Authors: Prathap Rayalacheruvu, Liton Majumdar, W. R. M. Rocha, Michael E. Ressler, Pabitra Ranjan Giri, S. Maitrey, Karen Willacy, D. C. Lis, Yuan Chen, P. D. Klaassen
Figure caption: Continuum determination in the source IRAS 2A. The top panel displays the observed flux (blue) on a logarithmic scale, along with the continuum (green) traced using a guided polynomial fit. The anchor points used for continuum fitting are marked as red dots. The bottom panel presents the corresponding estimated optical depths (green). For comparison, data from W. R. M. Rocha et al. (2024) are also plotted (orange).
Paper: Expanding the Ice Inventory of NGC 1333 IRAS 2A with INDRA Using JWST Observations: Tracing Organic Refractories and Beyond
Authors: Prathap Rayalacheruvu, Liton Majumdar, W. R. M. Rocha, Michael E. Ressler, Pabitra Ranjan Giri, S. Maitrey, Karen Willacy, D. C. Lis, Yuan Chen, P. D. Klaassen
Figure caption: Silicate removal in the source IRAS 2A. The observed optical depth spectrum is shown in blue, while the modeled silicate absorption is represented by the solid red line. The individual silicate components contributing to the overall absorption are olivine (dotted brown) and pyroxene (dotted orange). The final silicate-subtracted spectrum is shown in green.