adsabs/AstroCLIMB huggingface dataset:
Paper: S-type Stars from LAMOST DR10: Classification of Intrinsic and Extrinsic Stars
Authors: Jing Chen, Yin-Bi Li, A-Li Luo, Xiao-Xiao Ma, Shuo Li
Figure caption: A medium-resolution spectrum example before (top) and after (bottom) spectral preprocessing.
Paper: Chemical Differentiation around Five Massive Protostars Revealed by ALMA: Carbon-chain Species and Oxygen/Nitrogen-bearing Complex Organic Molecules
Authors: Kotomi Taniguchi, Liton Majumdar, Paola Caselli, Shigehisa Takakuwa, Tien-Hao Hsieh, Masao Saito, Zhi-Yun Li, Kazuhito Dobashi, Tomomi Shimoikura, Fumitaka Nakamura, Jonathan C. Tan, Eric Herbst
Figure caption: Comparison between the observed molecular abundances with respect to CH3CN at each core (horizontal color lines) and the modeled ones (black solid lines). The red dashed lines indicate the temperature evolution.
Paper: Chemical Differentiation around Five Massive Protostars Revealed by ALMA: Carbon-chain Species and Oxygen/Nitrogen-bearing Complex Organic Molecules
Authors: Kotomi Taniguchi, Liton Majumdar, Paola Caselli, Shigehisa Takakuwa, Tien-Hao Hsieh, Masao Saito, Zhi-Yun Li, Kazuhito Dobashi, Tomomi Shimoikura, Fumitaka Nakamura, Jonathan C. Tan, Eric Herbst
Figure caption: Spectra of the HC5N (J = 35−34) line at each core. The red curves show the best-fit results in the CASSIS software.
Paper: Chemical Differentiation around Five Massive Protostars Revealed by ALMA: Carbon-chain Species and Oxygen/Nitrogen-bearing Complex Organic Molecules
Authors: Kotomi Taniguchi, Liton Majumdar, Paola Caselli, Shigehisa Takakuwa, Tien-Hao Hsieh, Masao Saito, Zhi-Yun Li, Kazuhito Dobashi, Tomomi Shimoikura, Fumitaka Nakamura, Jonathan C. Tan, Eric Herbst
Figure caption: Spectra of the CH2CHCN (101,10−91,9) and CH3CH2CN (110,11−100,10) lines at each core. The red curves show the best-fit results in the CASSIS software.
Paper: Chemical Differentiation around Five Massive Protostars Revealed by ALMA: Carbon-chain Species and Oxygen/Nitrogen-bearing Complex Organic Molecules
Authors: Kotomi Taniguchi, Liton Majumdar, Paola Caselli, Shigehisa Takakuwa, Tien-Hao Hsieh, Masao Saito, Zhi-Yun Li, Kazuhito Dobashi, Tomomi Shimoikura, Fumitaka Nakamura, Jonathan C. Tan, Eric Herbst
Figure caption: Comparison of spatial distributions among each species. The color scale shows the continuum image, the same as in Figure 1. The white and black contours show moment 0 maps of each species denoted at the top-left corners in each panel. The contour levels are relative values of the peak intensities, and the contour levels are indicated below each panel. Three panels of the first column, the second column, and the third column show comparisons in G9.62+0.19, G10.47+0.03, and G12.89+0.49, respectively. The ellipses indicate the angular resolution: an open one corresponds to the continuum image, and white and black ones correspond to the moment 0 maps of each molecule. The colors are the same as the contours.
Paper: Chemical Differentiation around Five Massive Protostars Revealed by ALMA: Carbon-chain Species and Oxygen/Nitrogen-bearing Complex Organic Molecules
Authors: Kotomi Taniguchi, Liton Majumdar, Paola Caselli, Shigehisa Takakuwa, Tien-Hao Hsieh, Masao Saito, Zhi-Yun Li, Kazuhito Dobashi, Tomomi Shimoikura, Fumitaka Nakamura, Jonathan C. Tan, Eric Herbst
Figure caption: Warm carbon-chain chemistry (WCCC) and hot carbon-chain chemistry (HCCC). The HCCC mechanism was based on the chemical simulations by Taniguchi et al. (2019a).
Paper: Chemical Differentiation around Five Massive Protostars Revealed by ALMA: Carbon-chain Species and Oxygen/Nitrogen-bearing Complex Organic Molecules
Authors: Kotomi Taniguchi, Liton Majumdar, Paola Caselli, Shigehisa Takakuwa, Tien-Hao Hsieh, Masao Saito, Zhi-Yun Li, Kazuhito Dobashi, Tomomi Shimoikura, Fumitaka Nakamura, Jonathan C. Tan, Eric Herbst
Figure caption: Channel maps of CH3OCH3 in G10.47+0.03. The white contours indicate the continuum emission (from 20σ to 180σ in 40σ step).
Paper: Chemical Differentiation around Five Massive Protostars Revealed by ALMA: Carbon-chain Species and Oxygen/Nitrogen-bearing Complex Organic Molecules
Authors: Kotomi Taniguchi, Liton Majumdar, Paola Caselli, Shigehisa Takakuwa, Tien-Hao Hsieh, Masao Saito, Zhi-Yun Li, Kazuhito Dobashi, Tomomi Shimoikura, Fumitaka Nakamura, Jonathan C. Tan, Eric Herbst
Figure caption: Spectra of the CH3OH (11,0−21,2, v  t  = 1) and CH3OCH3 (41,4−30,3) lines at each core. The red curves show the best-fit results in the CASSIS software.
Paper: Chemical Differentiation around Five Massive Protostars Revealed by ALMA: Carbon-chain Species and Oxygen/Nitrogen-bearing Complex Organic Molecules
Authors: Kotomi Taniguchi, Liton Majumdar, Paola Caselli, Shigehisa Takakuwa, Tien-Hao Hsieh, Masao Saito, Zhi-Yun Li, Kazuhito Dobashi, Tomomi Shimoikura, Fumitaka Nakamura, Jonathan C. Tan, Eric Herbst
Figure caption: Spectra of the HNCO (40,4−30,3) line at each core. The red curves show the best-fit results in the CASSIS software.
Paper: Chemical Differentiation around Five Massive Protostars Revealed by ALMA: Carbon-chain Species and Oxygen/Nitrogen-bearing Complex Organic Molecules
Authors: Kotomi Taniguchi, Liton Majumdar, Paola Caselli, Shigehisa Takakuwa, Tien-Hao Hsieh, Masao Saito, Zhi-Yun Li, Kazuhito Dobashi, Tomomi Shimoikura, Fumitaka Nakamura, Jonathan C. Tan, Eric Herbst
Figure caption: Comparison of chemical compositions with respect to CH3CN.
Paper: Validating Stellar Abundance Measurements from Multiresolution Spectroscopy
Authors: Nathan R. Sandford, Daniel R. Weisz, Yuan-Sen Ting, 源森 丁
Figure caption: A sample order from one reduced archival observations (black points) illustrating the types of masks we apply to the data. The solid red line represents the scaled blaze function, which we use for the zeroth-order continuum determination. Deviations from the observed continuum are accounted for using a polynomial as described in Section 3.2.3. The gray, blue, and purple shaded regions represent the detector boundary mask, the telluric mask, and the bad pixel mask, respectively. Pixels that lie within these observational masks are ignored in the spectral fitting analysis.
Paper: Validating Stellar Abundance Measurements from Multiresolution Spectroscopy
Authors: Nathan R. Sandford, Daniel R. Weisz, Yuan-Sen Ting, 源森 丁
Figure caption: An illustration of the effects of varying spectral resolution on the observational masks using the same sample order and observational masks from Figure 3 (top). The lower panels depict the observed order convolved to lower resolutions by successive factors of 2. As the spectral resolving power decreases, the observational masks (light-gray bands) grow to include pixels impacted by the broadening of masked features. The spectrum is also rebinned as it is convolved to lower resolution to maintain a constant N pix/FWHM. The S/N of the spectrum scales with R −1/2 as a result of this rebinning.
Paper: Validating Stellar Abundance Measurements from Multiresolution Spectroscopy
Authors: Nathan R. Sandford, Daniel R. Weisz, Yuan-Sen Ting, 源森 丁
Figure caption: Forecasted number of stars observed by large spectroscopic surveys by ∼2030 as a function of spectral resolving power. Surveys with very limited wavelength coverage suitable (e.g., RAVE, Gaia-RVS, H3) are excluded. Surveys with fewer than 105 stars are also excluded as they contribute to the figure imperceptibly. Survey overlap is not considered. In 2030, ∼75% of the >50 million observed stellar spectra in the MW and LG will be taken at R < 10,000.
Paper: Validating Stellar Abundance Measurements from Multiresolution Spectroscopy
Authors: Nathan R. Sandford, Daniel R. Weisz, Yuan-Sen Ting, 源森 丁
Figure caption: Gaia DR3 color–magnitude diagram of likely M15 members as identified by Vasiliev & Baumgardt (2021). Stars analyzed in this work are represented by filled circles, which are all located on the upper part of the RGB. The median extinction correction applied to the sample is denoted by the arrow in the upper-left-hand corner of the figure.
Paper: Validating Stellar Abundance Measurements from Multiresolution Spectroscopy
Authors: Nathan R. Sandford, Daniel R. Weisz, Yuan-Sen Ting, 源森 丁
Figure caption: The same sample order and observational mask from Figure 3 convolved to R ∼ 10,000 (top). The lower panels depict the observed order noised up by factors of 4, 8, 16, and 32, respectively. While very little information appears to remain at the lowest S/N, this is only a small portion of the full stellar spectrum.
Paper: Validating Stellar Abundance Measurements from Multiresolution Spectroscopy
Authors: Nathan R. Sandford, Daniel R. Weisz, Yuan-Sen Ting, 源森 丁
Figure caption: Median S/N per pixel of each echelle order in each exposure analyzed in this study before the quality of the data is degraded. The width of the horizontal bars represents the wavelength coverage spanned by the order. The colors denote the observing programs outlined in Table 2.
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: The vortex number 1 as it was detected by the AGM using ks = 7 at the moment t = t 0. The orange plus sign indicates the vortex center, and the orange contour depicts the corresponding boundary. The direction of the horizontal velocity field is indicated by the streamlines and the arrows in blue. The background is colored by (a) the radial velocity component, (b) the tangential velocity component, (c) the vorticity, (d) the angular velocity component, (e) the vertical component of the velocity field, and (f) the divergence of the velocity.
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: A snapshot of the magnetoconvection simulation at t = 5 s, displaying the simulated solar surface colored by the vertical velocity field. The black arrows show the direction of the horizontal velocity field.
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: Vortex detection by the AGM using different kernel sizes (ks). The blue contours (crosses) indicate the vortex boundaries (centers) obtained by applying the AGM with ks = 3. The orange contours (crosses) indicate the vortex boundaries (centers) obtained by applying the AGM with ks = 7.
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: The distributions of the Γ function values obtained by the AGM ((a) and (c)) and CGM ((b) and (d)), which were applied to the photospheric horizontal velocity field, as shown in Figure 1. For both cases, a convolution kernel size ks = 11 was applied. As here, the values of Z 1 and Z 2 for the AGM were equal to 1, and the AGM was unnormalized.
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: (a) Vortex detected with the adaptive AGM. The vortex center and boundary are indicated with the blue cross and contour, respectively. (b) The Γ1 map detected with ks = 3 of panel (a). The blue and red squares indicate the two 3 × 3 kernel domains that were used to calculate Γ1. (c) The Γ1 map detected with ks = 5 of panel (a). The blue and red squares indicate the two 5 × 5 kernel domains that were used to calculate Γ1.
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: The radial and tangential velocity components (averaged over angular directions) as a function of time and radius are shown in panels (a) and (b), correspondingly. The vertical axis indicates the distance from the vortex center to the mean vortex radius calculated at the corresponding time (green dashed line). The horizontal axis indicates the lifetime scale of the vortex shown in Figure 9.
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: The relationship between the estimated Z values and convolution kernel size (ks). Blue squares indicate the mean ratios of C 1 and orange circles correspond to the mean ratios of C 2. The black dashed line is the linear regression between them.
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: The distributions of the ratios between the unnormalized AGM (Figures 3(a) and (c)) and CGM (Figures 3(b) and (d)) for Γ1 (C 1 is shown in blue) and Γ2 (C 2 is shown in red). For both cases, the convolution kernel is equal to 11.
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: Statistical comparison between vortices detected by the AGM (blue) and the Γ method (red) in numerical data. (a) Probability mass function of the lifetimes of detected counterclockwise vortices. (b) Probability mass function of the mean area of detected counterclockwise vortices. (c) Probability mass function of the mean diameter of detected counterclockwise vortices. (d) Probability mass function of the lifetimes of detected clockwise vortices. (e) Probability mass function of the mean area of detected clockwise vortices. (f) Probability mass function of the mean diameter of detected clockwise vortices.
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: The time-dependent behavior of vortex number 1, which is shown in Figure 7. (a) A series of detected centers of the vortex (blue plus signs) and its boundaries (orange contours) are shown for times t 0 − t 7, from the bottom to top. (b) The evolution of the vortex area (blue curve) and mean radius (orange curve). The error bars show the maximum and minimum of the radius at the corresponding time. The dark blue dashed line indicates the mean area of the vortex. (c) The evolution of the vorticity at the center of the vortex (blue curve) and the corresponding evolution of the Γ1 value (orange curve).
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: The instantaneous number of vortices detected by the AGM with the use of different kernel sizes: ks = 3 (green), ks = 7 (yellow), and ks = 11 (purple). The results obtained by the adaptive AGM (ks = 3, 5, 7, 9, and 11) are shown in blue, and those by the Γ method are shown in red.
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: Statistical comparison between vortices detected by the adaptive AGM (blue) and the Γ method (red) in observational data. Three results are shown: lifetime probability mass function of the detected counterclockwise (a) and clockwise (d) vortices; mean area probability mass function of the detected counterclockwise (b) and clockwise (e) vortices; and mean diameter probability mass function of the detected counterclockwise (c) and clockwise (f) vortices.
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: Time evolution of 150 detected vortices (some of them are less visible, due to their short lifetimes; see Figure 12) in simulation data. The size of the analyzed region is 6.4 Mm × 6.4 Mm. The vertical axis indicates the time, and the black arrow indicates the horizontal velocity field at the initial moment of time. The vortices were detected by the AGM using kernel sizes 3, 5, 7, 9, and 11. The orange and blue contours indicate clockwise and counterclockwise rotation vortices, respectively.
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: Results of vortex detections by the adaptive AGM and the Γ method. Both methods were applied to observational (CRISP) data at t = 231 s (a). The blue and red contours indicate vortices detected by the adaptive AGM and the Γ method, respectively. The detected vortex centers from both methods are shown in blue and red colors as well. The black arrows indicate the horizontal velocity field. Three ROIs (R1, R2, and R3) are selected to zoom in on and are presented in panels (b), (c), and (d).
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: Comparison of detected results between the adaptive AGM method and the Γ method. The blue and orange contours indicate vortices detected by using the adaptive AGM and the Γ method, respectively. Blue dots indicate vortex centers identified by the AGM, while orange dots indicate those identified by the Γ method. The black arrow indicates the horizontal velocity field. Three ROIs (R1, R2, and R3) were selected to zoom in on and are presented with the details in (b), (c), and (d). (a) Spatial domain when time =35 s. (b) Zoom-in view of region R1. (c) Zoom-in view of region R2. (d) Zoom-in view of region R3.
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: The instantaneous number of vortices detected in the observational data (CRISP) by the adaptive AGM and the Γ method. The results obtained by the adaptive AGM with kernel sizes ks = 3, 5, 7, 9, and 11 are shown in blue, and those obtained by the Γ method are shown in red.
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: The outer product between column vector  a  and row vector  b .
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: Convolution between column vector  a  and row vector  b . (a) The output of the first row of the convolution between column vector  a  and row vector  b . (b) The output of the second row of the convolution between column vector  a  and row vector  b . (c) The output of the fifth row of the convolution between column vector  a  and row vector  b . (d) The output of the convolution between  a  and  b .
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: The distributions of the Γ function values obtained by the AGM ((a) and (c)) and CGM ((b) and (d)) that were applied to the photospheric horizontal velocity field are shown in Figure 1. For both cases, a convolution kernel size ks = 33 was applied. As here, the values of Z 1 and Z 2 for the AGM were equal to 1 and the AGM was unnormalized.
Paper: Advanced Γ Method for Small-scale Vortex Detection in the Solar Atmosphere
Authors: Yuyang Yuan, Suzana de Souza e Almeida Silva, Viktor Fedun, Irina N. Kitiashvili, Gary Verth
Figure caption: The distribution of the ratios between the unnormalized AGM (Figures 19(a) and (c)) and CGM (Figures 19(b) and (d)) for Γ1 (C 1 is shown in blue) and Γ2 (C 2 is shown in red). For both cases, the convolution kernel is equal to 33.
Paper: Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog
Authors: Sumanth Rotti, Petrus C. Martens
Figure caption: Spatial variations of all the large SEP events in the GSEP data set across the solar disk. The location values are plotted here as a function of the NOAA S-scale.
Paper: Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog
Authors: Sumanth Rotti, Petrus C. Martens
Figure caption: Correlations coefficients of five parameters of source solar eruptions are obtained here for peak proton fluxes and fluences of SEP events. Logarithmic transformations of the CME and SEP variables are considered here. CME speed shows a good correlation with SEP events, while flare intensities are moderately correlated.
Paper: Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog
Authors: Sumanth Rotti, Petrus C. Martens
Figure caption: Ranked results of feature importance in our data set based on (a) random forest and (b) XGBoost models. Both models build on both flare and CME parameters, where the logarithm of CME speed and the flare magnitude are the top two highly significant features.
Paper: Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog
Authors: Sumanth Rotti, Petrus C. Martens
Figure caption: Scatter plot of the log-transformed peak proton fluxes for (a) large and (b) weak SEP events. Similarly, we show a scatter plot of proton event fluences with respect to CME speeds for (c) large and (d) weak SEP events. A linear regression trend is fit to the data, showing the respective variance (R2) of the distribution as an inset within the corresponding figure.
Paper: Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog
Authors: Sumanth Rotti, Petrus C. Martens
Figure caption: Distribution of (a) linear speeds and (b) angular widths of CMEs that are associated with SEP events in the GSEP data set. The two classes of SEP events (strong and weak) are shown in the legend. It can be noticed that halo fast CMEs appear to dominate the occurrence of large SEP events.
Paper: Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog
Authors: Sumanth Rotti, Petrus C. Martens
Figure caption: Distribution of (a) time difference between source solar eruption (flare/CME) and SEP event onset. Up to 79% of SEP events arise from a precursor within 12 hr; (b) SEP event rise time, i.e., the time taken by the proton fluxes to reach peak intensities.
Paper: Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog
Authors: Sumanth Rotti, Petrus C. Martens
Figure caption: Distribution of (a) longitudinal variation of solar flare locations that are associated with SEP events and (b) soft X-ray flare peak intensities based on GOES flare classification for flares associated with SEP events. The two classes of SEP events (strong and weak) are shown in the legend.
Paper: Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog
Authors: Sumanth Rotti, Petrus C. Martens
Figure caption: Spatial variations of all the weak/sub-SEP events in the GSEP data set across the solar disk. The location values are plotted here as a function of peak solar flare intensity. The X-ray flare measurements are obtained from the GOES missions.