adsabs/AstroCLIMB huggingface dataset:
Paper: Modeling Solids in Nuclear Astrophysics with Smoothed Particle Hydrodynamics
Authors: I. Sagert, O. Korobkin, I. Tews, B.-J. Tsao, H. Lim, M. Falato, J. Loiseau
Figure caption: Density profiles used to initialize, relax, and evolve the stars in Figure 14. The profiles are altered from the original profile for a 1.4 solar mass star with the polytropic EoS by cutting them off at around 0.5 ρ 0 (gray line). With that, we ensure that the crust shell is located at similar densities for all three neutron star setups.
Paper: Modeling Solids in Nuclear Astrophysics with Smoothed Particle Hydrodynamics
Authors: I. Sagert, O. Korobkin, I. Tews, B.-J. Tsao, H. Lim, M. Falato, J. Loiseau
Figure caption: Velocity distribution after relaxation with coupled crust and core where the crust is described as a solid. The maximal particle velocities typically range up to several times 106 cm s−1.
Paper: Modeling Solids in Nuclear Astrophysics with Smoothed Particle Hydrodynamics
Authors: I. Sagert, O. Korobkin, I. Tews, B.-J. Tsao, H. Lim, M. Falato, J. Loiseau
Figure caption: Pressure, bulk modulus, and shear modulus of neutron star crust material determined via Chrial EFT calculations.
Paper: Modeling Solids in Nuclear Astrophysics with Smoothed Particle Hydrodynamics
Authors: I. Sagert, O. Korobkin, I. Tews, B.-J. Tsao, H. Lim, M. Falato, J. Loiseau
Figure caption: Cross section of the 3D Taylor anvil, initialized by relaxing particles, simulation with the pressure field on the left, and the plastic strain on the right at 80 μs. The dashed outline shows the shape of the rod from Burton et al. (2015) at the same time for the CCH2 (left) and CGR (right) schemes.
Paper: The First AGILE Solar Flare Catalog
Authors: A. Ursi, N. Parmiggiani, M. Messerotti, A. Pellizzoni, C. Pittori, F. Longo, F. Verrecchia, A. Argan, A. Bulgarelli, M. Tavani, P. Tempesta, F. D'Amico
Figure caption: Histograms of the duration (first column), rise time (second column), and decay time (third column) of the solar flares detected by AGILE, divided into class B (first row), class C (second row), class M (third row), and class X (fourth row), together with the same distributions evaluated on the whole sample (fifth row). Duration is estimated on the 2149 flares of the level 2 sample, fully acquired by AGILE, and is available for B, C, M, and X classes. On the other hand, rise and decay are evaluated on the 1983 flares of the level 3 sample, fully acquired and not suffering from count rate saturation, and are available for B, C, and M classes. For each parameter, the median value μ is reported as a vertical black line, and its value is shown in the legend, expressed in minutes. B-class and C-class distributions are reported with 30 and 10 s bin widths, respectively; on the other hand, M- and X-class distributions are shown with 1 and 5 minutes, respectively, due to the poor statistics. The whole sample is evaluated with a 10 s bin width.
Paper: The First AGILE Solar Flare Catalog
Authors: A. Ursi, N. Parmiggiani, M. Messerotti, A. Pellizzoni, C. Pittori, F. Longo, F. Verrecchia, A. Argan, A. Bulgarelli, M. Tavani, P. Tempesta, F. D'Amico
Figure caption: Scatter plots of level 3 solar flares intensity parameters, as revealed in the GOES data (in the SXR range) with respect to the AGILE AC-Lat4 data (in the HXR range). The AGILE estimated peak flux and fluence parameters, labeled on the right y-axes, are evaluated using the calibration process illustrated in Section 5.9. The blue dashed lines represent the bisectors of the two plots, where points would be distributed if the parameters exhibited the same values in both energy ranges. The black dashed lines are the best fit obtained with power-law models. The corresponding power-law indices and cross-correlation factors are reported in the bottom left legends.
Paper: The First AGILE Solar Flare Catalog
Authors: A. Ursi, N. Parmiggiani, M. Messerotti, A. Pellizzoni, C. Pittori, F. Longo, F. Verrecchia, A. Argan, A. Bulgarelli, M. Tavani, P. Tempesta, F. D'Amico
Figure caption: Histograms of the peak counts and integrated counts, for the AGILE solar flares of level 3 sample. The first three rows refer to B-, C-, and M-class events, while the last row includes the whole flare sample. Median values μ are reported as vertical lines. For each distribution, both the peak counts and integrated counts (bottom x-axes), as well as the corresponding estimated peak flux and fluence values in physical units (top x-axes) obtained from the calibration process, are reported.
Paper: The First AGILE Solar Flare Catalog
Authors: A. Ursi, N. Parmiggiani, M. Messerotti, A. Pellizzoni, C. Pittori, F. Longo, F. Verrecchia, A. Argan, A. Bulgarelli, M. Tavani, P. Tempesta, F. D'Amico
Figure caption: Histograms of the offsets between the maximum times, as seen in the AGILE and in the GOES data. The first three rows represent flares belonging to the B, C, and M classes, whereas the last row refers to the whole sample. For each parameter, the median value μ ± c 95 is reported, expressed in minutes. These offsets allow to better understand how the flares behave in the HXR and SXR regimes. The distributions show a skewness on the left-hand side, pointing out that the peak flux in the SXR tends to occur later than in the HXR.
Paper: The First AGILE Solar Flare Catalog
Authors: A. Ursi, N. Parmiggiani, M. Messerotti, A. Pellizzoni, C. Pittori, F. Longo, F. Verrecchia, A. Argan, A. Bulgarelli, M. Tavani, P. Tempesta, F. D'Amico
Figure caption: Schematic representation of the results obtained in this work. Out of the 5003 transients (level 0) obtained by the search algorithm for minute-lasting X-ray transients in the AGILE AC-Lat4 data, 3572 exhibited a unique associated GOES solar flare (level 1). Out of these events, 2149 flares have been fully acquired on board (level 2), and out of them, 1983 did not saturate the AC count rate (level 3). On the right side, the events that did not match any GOES flare are classified into similar levels, on the basis of the same requirements (level n1, level n2, and level n3).
Paper: The First AGILE Solar Flare Catalog
Authors: A. Ursi, N. Parmiggiani, M. Messerotti, A. Pellizzoni, C. Pittori, F. Longo, F. Verrecchia, A. Argan, A. Bulgarelli, M. Tavani, P. Tempesta, F. D'Amico
Figure caption: Occurrence rate of the 3572 level 1 AGILE solar flares, detected by the AGILE AC-Lat4 between 2007 May and 2022 August. The end of the 23rd solar cycle (2007–2008), the whole 24th solar cycle (2008–2019), and the beginning of the current 25th cycle (2019–2022) are clearly revealed. Different colors denote different flare classes, as obtained from GOES.
Paper: The First AGILE Solar Flare Catalog
Authors: A. Ursi, N. Parmiggiani, M. Messerotti, A. Pellizzoni, C. Pittori, F. Longo, F. Verrecchia, A. Argan, A. Bulgarelli, M. Tavani, P. Tempesta, F. D'Amico
Figure caption: Light curve of the AGILE AC-Lat4 for a solar flare detected on UT 2013 April 24 22:09:20. This transient, whose AGILE detection interval and maximum are displayed in orange, is associated with a GOES flare of class C5.7, whose detection interval and maximum are displayed in red. As the AGILE maximum falls inside the GOES interval, the association is considered fulfilled, and the event is classified as a genuine flare.
Paper: The First AGILE Solar Flare Catalog
Authors: A. Ursi, N. Parmiggiani, M. Messerotti, A. Pellizzoni, C. Pittori, F. Longo, F. Verrecchia, A. Argan, A. Bulgarelli, M. Tavani, P. Tempesta, F. D'Amico
Figure caption: Energy spectrum of a typical solar flare, extrapolated from Grigis & Benz (2004), Benz (2008; gray), and the average flux obtained for the total AGILE solar flare sample, in the AC-Lat4 80–200 keV unique energy channel (black).
Paper: The First AGILE Solar Flare Catalog
Authors: A. Ursi, N. Parmiggiani, M. Messerotti, A. Pellizzoni, C. Pittori, F. Longo, F. Verrecchia, A. Argan, A. Bulgarelli, M. Tavani, P. Tempesta, F. D'Amico
Figure caption: Top left: rate of the AGILE 3572 matched solar flares (black) compared to the rate of the 1424 level n1 unmatched transients (blue). The general detection rates are very similar, exhibiting a p-value of 0.43 after a Kolmogorov–Smirnov test. Top right: longitude distribution of the AGILE subsatellite points at the time of the level 1 (black) and level n1 (blue) events. The lack of data corresponds to the South Atlantic Anomaly (SAA). Both distributions exhibit similar profiles, with no excesses along the orbit. Second row: distribution of duration, rise time, and decay time of the 998 level n2 events (blue), together with the corresponding distributions evaluated for the 2149 level 2 solar flare sample (black). For each parameter, the median value μ is shown. Third row: distribution of peak counts and integrated counts of the 994 level n3 candidate transients fully detected by AGILE (blue), together with the corresponding distributions evaluated for the 1983 level 3 solar flare sample (black). For each parameter, median value μ is shown.
Paper: The First AGILE Solar Flare Catalog
Authors: A. Ursi, N. Parmiggiani, M. Messerotti, A. Pellizzoni, C. Pittori, F. Longo, F. Verrecchia, A. Argan, A. Bulgarelli, M. Tavani, P. Tempesta, F. D'Amico
Figure caption: Temporal offset between AC-Lat4 maximum and GOES start time (left) and GOES end time (right), in the matched (black) level 2 and unmatched (blue) level n2 sample. For the matched sample, the AGILE maximum tends to occur after the GOES start time and before the GOES end time, while, for the unmatched sample, a large fraction of events seems to take place after the GOES end time of the nearest solar flare.
Paper: Three-day Forecasting of Solar Wind Speed Using SDO/AIA Extreme-ultraviolet Images by a Deep-learning Model
Authors: Jihyeon Son, Suk-Kyung Sung, Yong-Jae Moon, Harim Lee, Hyun-Jin Jeong
Figure caption: Two good examples of results from our model when there is the enhancement of solar wind speeds caused by coronal holes. The gray line means observation data, and the orange one means prediction values by our model. The red dashed line is a forecast starting time.
Paper: Three-day Forecasting of Solar Wind Speed Using SDO/AIA Extreme-ultraviolet Images by a Deep-learning Model
Authors: Jihyeon Son, Suk-Kyung Sung, Yong-Jae Moon, Harim Lee, Hyun-Jin Jeong
Figure caption: Architecture of our deep-learning model. It consists of two input layers, to which the solar images (Input I) and solar wind speed data (Input II) are respectively entered. The images pass several convolution networks and one LSTM layer, and the solar wind speed data flow into two dense layers. They are concatenated and pass one dense layer at the end of the network, and then the targets (sequential solar wind speed values) are predicted.
Paper: Three-day Forecasting of Solar Wind Speed Using SDO/AIA Extreme-ultraviolet Images by a Deep-learning Model
Authors: Jihyeon Son, Suk-Kyung Sung, Yong-Jae Moon, Harim Lee, Hyun-Jin Jeong
Figure caption: CC and RMSE of our model for given prediction times. The left axis is CC (black) and the right axis is RMSE (red).
Paper: Three-day Forecasting of Solar Wind Speed Using SDO/AIA Extreme-ultraviolet Images by a Deep-learning Model
Authors: Jihyeon Son, Suk-Kyung Sung, Yong-Jae Moon, Harim Lee, Hyun-Jin Jeong
Figure caption: CC and RMSE of our model for each year (2011–2020). The left axis is CC (black) and the right axis is RMSE (red).
Paper: Three-day Forecasting of Solar Wind Speed Using SDO/AIA Extreme-ultraviolet Images by a Deep-learning Model
Authors: Jihyeon Son, Suk-Kyung Sung, Yong-Jae Moon, Harim Lee, Hyun-Jin Jeong
Figure caption: Comparison of the results of the 72 hr prediction by our model (red line) with those of the WSA-ENLIL model (green line) in CR 2209 where the period of Figure 4(a) is included. The gray line indicates the observed solar wind speeds.
Paper: Three-day Forecasting of Solar Wind Speed Using SDO/AIA Extreme-ultraviolet Images by a Deep-learning Model
Authors: Jihyeon Son, Suk-Kyung Sung, Yong-Jae Moon, Harim Lee, Hyun-Jin Jeong
Figure caption: An example of poor prediction by our model. The gray line shows observation data, and the orange one shows prediction values by our model. The red dashed line is a forecast start time. The black solid line indicates a halo CME occurred at 2013 September 29.
Paper: Three-day Forecasting of Solar Wind Speed Using SDO/AIA Extreme-ultraviolet Images by a Deep-learning Model
Authors: Jihyeon Son, Suk-Kyung Sung, Yong-Jae Moon, Harim Lee, Hyun-Jin Jeong
Figure caption: Panel (a) shows examples of input images for Figure 4(a), and panel (b) shows the same for Figure 4(b). Top images of each panel are 193 Å and bottom ones are 211 Å images. The equatorial coronal holes, the source of high-speed streams, are well shown in these input images.
Paper: Three-day Forecasting of Solar Wind Speed Using SDO/AIA Extreme-ultraviolet Images by a Deep-learning Model
Authors: Jihyeon Son, Suk-Kyung Sung, Yong-Jae Moon, Harim Lee, Hyun-Jin Jeong
Figure caption: Two good examples of results from our model when the solar wind speeds decrease. The gray line shows observation data, and the orange one shows prediction values by our model. The red dashed line is a forecast starting time.
Paper: Propagating Uncertainties in the SALT3 Model-training Process to Cosmological Constraints
Authors: M. Dai, D. O. Jones, W. D. Kenworthy, R. Kessler, J. D. R. Pierel, R. J. Foley, S. W. Jha, D. M. Scolnic
Figure caption: Color distributions of the low-z training data from the original baseline simulations (with low-z x 1/c parameters drawn randomly from previously generated populations, blue), the regenerated K21-like simulations (with K21 low-z x 1/c parameters, green), and the K21 data (red). The shaded areas correspond to the standard deviation of the number in each bin for the multiple simulations.
Paper: Propagating Uncertainties in the SALT3 Model-training Process to Cosmological Constraints
Authors: M. Dai, D. O. Jones, W. D. Kenworthy, R. Kessler, J. D. R. Pierel, R. J. Foley, S. W. Jha, D. M. Scolnic
Figure caption: Changes in the distance moduli as a function of redshift for each training variant, with respect to the baseline. Left: distance modulus changes for the following training data variations (from top to bottom): removing the legacy low-z data, removing the u/U bands, including miscalibrated spectra, and removing 50% of the spectral data. Right: distance modulus changes when including 100%, 50%, and 10% host-galaxy contamination, with respect to SN brightness at maximum light, in the input spectral data.
Paper: Propagating Uncertainties in the SALT3 Model-training Process to Cosmological Constraints
Authors: M. Dai, D. O. Jones, W. D. Kenworthy, R. Kessler, J. D. R. Pierel, R. J. Foley, S. W. Jha, D. M. Scolnic
Figure caption: Changes in the distance moduli (with respect to the BYO-STRETCH-COLOR model) as a function of redshift when simulating the input data using the BYOSED model. Top: BYOSED model with an ejecta velocity effect added. Middle: BYOSED model with a constant host-galaxy mass step effect added. Bottom: BYOSED model with a redshift dependent host-galaxy mass effect added.
Paper: Propagating Uncertainties in the SALT3 Model-training Process to Cosmological Constraints
Authors: M. Dai, D. O. Jones, W. D. Kenworthy, R. Kessler, J. D. R. Pierel, R. J. Foley, S. W. Jha, D. M. Scolnic
Figure caption: Difference in the averaged SALT3 model flux (integrated over UBVRI bands) between the two samples described in Appendix B (the original baseline samples and the regenerated K21-like samples) relative to the M0 flux of the original baseline samples.
Paper: Propagating Uncertainties in the SALT3 Model-training Process to Cosmological Constraints
Authors: M. Dai, D. O. Jones, W. D. Kenworthy, R. Kessler, J. D. R. Pierel, R. J. Foley, S. W. Jha, D. M. Scolnic
Figure caption: Schematic overview of our analysis pipeline, from simulation of the underlying SED model to cosmological parameter constraints. The arrows show the flow of the pipeline and how each stage is connected.
Paper: Propagating Uncertainties in the SALT3 Model-training Process to Cosmological Constraints
Authors: M. Dai, D. O. Jones, W. D. Kenworthy, R. Kessler, J. D. R. Pierel, R. J. Foley, S. W. Jha, D. M. Scolnic
Figure caption: Relative changes of model components M 0 and M 1 at peak for different variants of the training data with respect to the baseline M 0 component, with the gray area showing the error of the mean of the baseline M 0 component. Left: model changes for the following training data variations: removing the legacy low-z data, removing the u/U bands, adding calibration offsets to the training spectra, and removing 50% of the spectral data. Right: model changes after including 100%, 50%, and 10% host-galaxy contamination in the spectral data, with contamination scaled relative to the SN brightness at maximum light.
Paper: Propagating Uncertainties in the SALT3 Model-training Process to Cosmological Constraints
Authors: M. Dai, D. O. Jones, W. D. Kenworthy, R. Kessler, J. D. R. Pierel, R. J. Foley, S. W. Jha, D. M. Scolnic
Figure caption: Same as Figure 4 but showing relative changes of integrated model fluxes F 0 and F 1 with respect to the baseline model flux F 0. Individual colors show the model components when integrated over the UBVRI passbands.
Paper: Propagating Uncertainties in the SALT3 Model-training Process to Cosmological Constraints
Authors: M. Dai, D. O. Jones, W. D. Kenworthy, R. Kessler, J. D. R. Pierel, R. J. Foley, S. W. Jha, D. M. Scolnic
Figure caption: Examples of simulated SN and host-galaxy spectra. Blue: simulated final SN spectra with host-galaxy contaminations added; from left to right the host-galaxy fractions are 100%, 50%, and 10% relative to the SN peak brightness. Orange: the simulated host-galaxy spectra for each SN, scaled relative to the SN peak brightness. The uncontaminated SN spectra are simply a subtraction between the final SN spectra and the host-galaxy spectra.
Paper: Propagating Uncertainties in the SALT3 Model-training Process to Cosmological Constraints
Authors: M. Dai, D. O. Jones, W. D. Kenworthy, R. Kessler, J. D. R. Pierel, R. J. Foley, S. W. Jha, D. M. Scolnic
Figure caption: Comparison of light-curve parameter distributions between the average of 40 baseline simulations (Section 3.1) and real K21 data. Blue histograms are the simulated baseline data, with the shaded area being the standard deviation of the number in each bin from the simulations, and the red histograms are the K21 data.
Paper: Propagating Uncertainties in the SALT3 Model-training Process to Cosmological Constraints
Authors: M. Dai, D. O. Jones, W. D. Kenworthy, R. Kessler, J. D. R. Pierel, R. J. Foley, S. W. Jha, D. M. Scolnic
Figure caption: Difference in color law for each training data variation with respect to the baseline. The coefficient 0.1 is chosen to show the change in predicted magnitude as a function of wavelength for a SN with c = 0.1.
Paper: Failures of Minimum Variance Analysis in Diagnosing Planar Structures in Space
Authors: Y. Y. Liu, J. B. Cao, H. S. Fu, Z. Wang, Z. Z. Guo, R. J. He
Figure caption: The overview of Cluster detection of IDs. (a) An instance of discontinuity detected by four Cluster spacecraft. The observational conditions of the 21,978 discontinuities identified from the Cluster data, including (b) the average separation distance between four spacecraft, (c) the measurement residue, and the logarithm of (d) the volume ratio of the Cluster tetrahedron and (e) the condition number of the position matrix.
Paper: Failures of Minimum Variance Analysis in Diagnosing Planar Structures in Space
Authors: Y. Y. Liu, J. B. Cao, H. S. Fu, Z. Wang, Z. Z. Guo, R. J. He
Figure caption: (a) The pass rates, i.e., the proportion of the discontinuities with λ 2/λ 3 larger than the lower limit ﹩{\left({\lambda }_{2}/{\lambda }_{3}\right)}_{L}﹩, of four types of IDs as functions of ﹩{\left({\lambda }_{2}/{\lambda }_{3}\right)}_{L}﹩. (b) The type ratios of the discontinuities. In panel (b), the blue and orange histograms are obtained by timing, taking into account all the discontinuities or only the discontinuities with λ 2/λ 3 > 8; the magenta histogram is based on the MVAB results for the discontinuities with λ 2/λ 3 > 8.
Paper: Failures of Minimum Variance Analysis in Diagnosing Planar Structures in Space
Authors: Y. Y. Liu, J. B. Cao, H. S. Fu, Z. Wang, Z. Z. Guo, R. J. He
Figure caption: The geometric properties of the identified discontinuities. (a) The distribution of the x-component propagation speeds of the discontinuities relative to Cluster in the GSE coordinate system. (b) The PDF of the angular changes of the discontinuities. (c) The event distribution as a function of ﹩\left|{B}_{N}\right|/| {\boldsymbol{B}}| ﹩ and Δ∣ B ∣/∣ B ∣ determined by timing. (d) The PDFs of the angular changes of the in-plane magnetic field across RDs, TDs, EDs, and NDs.
Paper: Failures of Minimum Variance Analysis in Diagnosing Planar Structures in Space
Authors: Y. Y. Liu, J. B. Cao, H. S. Fu, Z. Wang, Z. Z. Guo, R. J. He
Figure caption: The joint distribution of the test results with λ 2/λ 3 > 8, as a function of θ MVA_Tim, ﹩\left|{B}_{N}\right|/| {\boldsymbol{B}}| ﹩, Δ∣ B ∣/∣ B ∣, and the field rotation angle ω. The black and red dots correspond to ﹩\left|{B}_{N}\right|/| {\boldsymbol{B}}| \lt 0.4﹩ and ﹩\left|{B}_{N}\right|/| {\boldsymbol{B}}| ﹩ > 0.4, respectively. Note that ﹩\left|{B}_{N}\right|/| {\boldsymbol{B}}| ﹩ is determined by timing here.
Paper: Failures of Minimum Variance Analysis in Diagnosing Planar Structures in Space
Authors: Y. Y. Liu, J. B. Cao, H. S. Fu, Z. Wang, Z. Z. Guo, R. J. He
Figure caption: The dependence of the MVAB accuracy on the eigenvalue ratio λ 2/λ 3. (a) The PDF of θ MVA_Tim, i.e., the angle between the discontinuity normals determined by MVAB and timing, in difference λ 2/λ 3 intervals. (b) The median θ MVA_Tim and the proportion of MVAB results with θ MVA_Tim < 30°, as a function of λ 2/λ 3. In panel (a), η is defined as the proportion of events with θ MVA_Tim < 30°.
Paper: Failures of Minimum Variance Analysis in Diagnosing Planar Structures in Space
Authors: Y. Y. Liu, J. B. Cao, H. S. Fu, Z. Wang, Z. Z. Guo, R. J. He
Figure caption: The joint distribution of the test results with λ 2/λ 3 > 8, as a function of θ MVA_Tim and the angular change of the in-plane magnetic field ω in–plane. The horizontal coordinates are determined by the timing method for the black and blue dots, and by the MVAB method for the red dots. Panel (b) is a top view of panel (a).
Paper: Failures of Minimum Variance Analysis in Diagnosing Planar Structures in Space
Authors: Y. Y. Liu, J. B. Cao, H. S. Fu, Z. Wang, Z. Z. Guo, R. J. He
Figure caption: The dependence of the MVAB accuracy on the width of the data window to which MVAB is applied. (a) An instance of MVAB application under LW and SW conditions. (b) The number distributions of θ MVA_Tim with λ 2/λ 3>8 under the LW and SW conditions. (c) The median θ MVA_Tim as a function of λ 2/λ 3 under the LW and SW conditions.
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:  M  K  vs.  W RP, BP−RP − W  K, J−K , diagram of S-type stars. The green and purple dots denote 764 S-type stars and carbon stars (the “C-N” type stars from Li et al. 2018), respectively. The black and blue dots show the intrinsic and extrinsic S-type samples from Abia et al. (2022). The solid black curve represents the theoretical boundary between O-rich and C-rich AGB stars. The dashed lines denote the threshold that distinguishes different types of AGB stars. The color bar on the right indicates the probability density of S-type stars.
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: The V–K and J–K diagram for the 2920 S-type stars with 2MASS and Pan-STARRS 1 photometric data. The green dots represent S-type stars, and the seven color lines denote MARCS models with C/O = 0.5, 0.75, 0.9, 0.93, 0.95, 0.97, 0.99, respectively. The color bar on the right denotes the probability density of S-type stars.