adsabs/AstroCLIMB huggingface dataset:
Paper: Chemical Cartography with APOGEE: Mapping Disk Populations with a 2-process Model and Residual Abundances
Authors: David H. Weinberg, Jon A. Holtzman, Jennifer A. Johnson, Christian Hayes, Sten Hasselquist, Matthew Shetrone, Yuan-Sen Ting, 源森 丁, Rachael L. Beaton, Timothy C. Beers, Jonathan C. Bird, Dmitry Bizyaev, Michael R. Blanton, Katia Cunha, José G. Fernández-Trincado, Peter M. Frinchaboy, D. A. García-Hernández, Emily Griffith, James W. Johnson, Henrik Jönsson, Richard R. Lane, Henry W. Leung, J. Ted Mackereth, Steven R. Majewski, Szabolcs Mészáros, Christian Nitschelm, Kaike Pan, Ricardo P. Schiavon, Donald P. Schneider, Mathias Schultheis, Verne Smith, Jennifer S. Sobeck, Keivan G. Stassun, Guy S. Stringfellow, Fiorenzo Vincenzo, John C. Wilson, Gail Zasowski
Figure caption: Top: distribution of stars in the plane of component amplitudes D 3, D 4. Black dots show a 25% random sampling of stars in the 2%–98% range of each distribution, while colored points show stars outside the listed 2% and 98% boundaries. A value of D 3 = 1 corresponds to a deviation of D 3 r 3 = 0.0304 dex for the most sensitive component element (Ce) and 0.0065 dex for the least sensitive (Al). A value of D 4 = 1 corresponds to a deviation of D 4 r 4 = 0.0239 dex for the most sensitive component element (V) and 0.0087 dex for the least sensitive (Ni). Bottom: distributions of D 3 (black) and D 4 (red) for the full sample (solid histograms) and for the subset of stars with R < 7 kpc and ﹩{Z}_{\max }\lt 0.2\,\mathrm{kpc}﹩ (dashed curves). Other geometric cuts produce distributions similar to the solid histograms, but the inner thin disk stars tend to have slightly higher values of D 3 and D 4.
Paper: Chemical Cartography with APOGEE: Mapping Disk Populations with a 2-process Model and Residual Abundances
Authors: David H. Weinberg, Jon A. Holtzman, Jennifer A. Johnson, Christian Hayes, Sten Hasselquist, Matthew Shetrone, Yuan-Sen Ting, 源森 丁, Rachael L. Beaton, Timothy C. Beers, Jonathan C. Bird, Dmitry Bizyaev, Michael R. Blanton, Katia Cunha, José G. Fernández-Trincado, Peter M. Frinchaboy, D. A. García-Hernández, Emily Griffith, James W. Johnson, Henrik Jönsson, Richard R. Lane, Henry W. Leung, J. Ted Mackereth, Steven R. Majewski, Szabolcs Mészáros, Christian Nitschelm, Kaike Pan, Ricardo P. Schiavon, Donald P. Schneider, Mathias Schultheis, Verne Smith, Jennifer S. Sobeck, Keivan G. Stassun, Guy S. Stringfellow, Fiorenzo Vincenzo, John C. Wilson, Gail Zasowski
Figure caption: Examples of 4-process fits to element abundance ratios, in a format similar to Figure 9. In each panel, filled circles with error bars show the APOGEE abundance measurements, and open circles show the abundances predicted by the best 2-process model fit. Red triangles show the abundances predicted after fitting a component of amplitude D 3 to the residual abundances of Ca, Na, Al, Cr, and Ce. Blue triangles show the predicted abundances after fitting a component of amplitude D 4 to the residual abundances of Ni, V, Mn, and Co. The change in an element’s predicted abundance in dex is the product of D  μ  with the corresponding value of r  μ,j  in Table 2 or 3. Each panel lists the χ 2 values for the 2-process and 4-process fits. The first two stars have unusually low values of D 3; the next two have unusually high values of D 3; the next two have unusually low and high values of D 4; and the final two have unusually low values of both D 3 and D 4.
Paper: Chemical Cartography with APOGEE: Mapping Disk Populations with a 2-process Model and Residual Abundances
Authors: David H. Weinberg, Jon A. Holtzman, Jennifer A. Johnson, Christian Hayes, Sten Hasselquist, Matthew Shetrone, Yuan-Sen Ting, 源森 丁, Rachael L. Beaton, Timothy C. Beers, Jonathan C. Bird, Dmitry Bizyaev, Michael R. Blanton, Katia Cunha, José G. Fernández-Trincado, Peter M. Frinchaboy, D. A. García-Hernández, Emily Griffith, James W. Johnson, Henrik Jönsson, Richard R. Lane, Henry W. Leung, J. Ted Mackereth, Steven R. Majewski, Szabolcs Mészáros, Christian Nitschelm, Kaike Pan, Ricardo P. Schiavon, Donald P. Schneider, Mathias Schultheis, Verne Smith, Jennifer S. Sobeck, Keivan G. Stassun, Guy S. Stringfellow, Fiorenzo Vincenzo, John C. Wilson, Gail Zasowski
Figure caption: Covariance matrix of deviations between values and 2-process model predictions. In each panel, filled and open circles denote positive and negative values, respectively, with area proportional to the magnitude of the matrix element and consistent scaling used across all panels. (a) Residual covariance for the APOGEE sample, with A cc and A Ia fit using the six elements denoted in blue on the axes. The diagonal elements are the squares of the rms deviations shown by filled circles in Figure 13. For visual scaling, note that the diagonal elements (Na, Na) and (Ce, Ce) have values of about (0.09)2, (S, S) has a value of about (0.05)2, and (O, O) has a value of about (0.01)2. (b) Residual covariance when A cc and A Ia are fit using Mg and Fe only, which increases the artificial correlations induced by measurement aberration. (c) Residual covariance for the simulated data set in which all stars lie on the 2-process model prediction and have Gaussian observational errors at the level of the reported ASPCAP uncertainties. Off-diagonal elements arise from measurement aberration, but they are small compared to the observed covariances. (d) Intrinsic covariance estimated by subtracting (c) from (a).
Paper: Chemical Cartography with APOGEE: Mapping Disk Populations with a 2-process Model and Residual Abundances
Authors: David H. Weinberg, Jon A. Holtzman, Jennifer A. Johnson, Christian Hayes, Sten Hasselquist, Matthew Shetrone, Yuan-Sen Ting, 源森 丁, Rachael L. Beaton, Timothy C. Beers, Jonathan C. Bird, Dmitry Bizyaev, Michael R. Blanton, Katia Cunha, José G. Fernández-Trincado, Peter M. Frinchaboy, D. A. García-Hernández, Emily Griffith, James W. Johnson, Henrik Jönsson, Richard R. Lane, Henry W. Leung, J. Ted Mackereth, Steven R. Majewski, Szabolcs Mészáros, Christian Nitschelm, Kaike Pan, Ricardo P. Schiavon, Donald P. Schneider, Mathias Schultheis, Verne Smith, Jennifer S. Sobeck, Keivan G. Stassun, Guy S. Stringfellow, Fiorenzo Vincenzo, John C. Wilson, Gail Zasowski
Figure caption: Median deviations from the best-fit 2-process model for stars in selected populations. Left panels show the location of the population in [Fe/Mg] vs. [Mg/H], with median low-Ia (red) and high-Ia (blue) sequences for the full sample shown for reference. Points with error bars in the right panels show the median deviation and the 1σ uncertainty in the median computed from 1000 bootstrap resamplings of the population. Top: disk stars with [Mg/H] < −0.5 and eccentricity greater than 0.5, with [Fe/Mg] > −0.18 (black) or [Fe/Mg] < −0.18 (red). Second row: disk stars in the outer Galaxy, with 15 ≤ R ≤ 17 kpc and ∣Z∣ ≤ 2 kpc (black) or 15 ≤ R ≤ 17 kpc and Z = 2–6 kpc (red). Third row: stars that are probable members of the ω Cen cluster based on angular position and radial velocity. Fourth row: stars that are probable members of the LMC. Fifth row: stars that have unusually low (black) or high (red) values of [Mg/Fe]. For all populations, we adopt a S/N cut of 100 and impose our usual ﹩\mathrm{log}\,g﹩ and T eff cuts, except that we retain the standard S/N cut for the fifth row. Note that different panels have different vertical ranges.
Paper: Chemical Cartography with APOGEE: Mapping Disk Populations with a 2-process Model and Residual Abundances
Authors: David H. Weinberg, Jon A. Holtzman, Jennifer A. Johnson, Christian Hayes, Sten Hasselquist, Matthew Shetrone, Yuan-Sen Ting, 源森 丁, Rachael L. Beaton, Timothy C. Beers, Jonathan C. Bird, Dmitry Bizyaev, Michael R. Blanton, Katia Cunha, José G. Fernández-Trincado, Peter M. Frinchaboy, D. A. García-Hernández, Emily Griffith, James W. Johnson, Henrik Jönsson, Richard R. Lane, Henry W. Leung, J. Ted Mackereth, Steven R. Majewski, Szabolcs Mészáros, Christian Nitschelm, Kaike Pan, Ricardo P. Schiavon, Donald P. Schneider, Mathias Schultheis, Verne Smith, Jennifer S. Sobeck, Keivan G. Stassun, Guy S. Stringfellow, Fiorenzo Vincenzo, John C. Wilson, Gail Zasowski
Figure caption: Examples of stars that are poorly fit by the 2-process model, in a format similar to Figure 9. In each panel, filled circles with error bars show the APOGEE abundance measurements, and open circles show the abundances predicted by the best 2-process model fit. Each panel lists the star’s best-fit A cc and A Ia, the χ 2 of this fit, and the (usually lower) modified χ 2 described in the text.
Paper: Chemical Cartography with APOGEE: Mapping Disk Populations with a 2-process Model and Residual Abundances
Authors: David H. Weinberg, Jon A. Holtzman, Jennifer A. Johnson, Christian Hayes, Sten Hasselquist, Matthew Shetrone, Yuan-Sen Ting, 源森 丁, Rachael L. Beaton, Timothy C. Beers, Jonathan C. Bird, Dmitry Bizyaev, Michael R. Blanton, Katia Cunha, José G. Fernández-Trincado, Peter M. Frinchaboy, D. A. García-Hernández, Emily Griffith, James W. Johnson, Henrik Jönsson, Richard R. Lane, Henry W. Leung, J. Ted Mackereth, Steven R. Majewski, Szabolcs Mészáros, Christian Nitschelm, Kaike Pan, Ricardo P. Schiavon, Donald P. Schneider, Mathias Schultheis, Verne Smith, Jennifer S. Sobeck, Keivan G. Stassun, Guy S. Stringfellow, Fiorenzo Vincenzo, John C. Wilson, Gail Zasowski
Figure caption: Properties of the 689 stars with modified-χ 2 values in the top 2% of the cumulative distribution. In all panels, red points represent stars with [Mg/H] < −0.5, blue points represent stars with [Mg/H] > 0.2, and black points represent stars with intermediate [Mg/H]. For context, the upper left panel shows the logg-T eff distribution for a sample of 5000 stars that satisfy the selection criteria for our main disk sample but span a wider range of ﹩\mathrm{log}\,g﹩ and T eff. The cyan box indicates our sample selection. The upper right panel shows ﹩\mathrm{log}\,g﹩ vs. T eff for the high-χ 2 stars, with the background sample represented by green dots. The lower panels plot the A Ia/A cc ratio against A cc (left) and orbital eccentricity (right), with green dots showing a random 10% of the full disk sample.
Paper: Chemical Cartography with APOGEE: Mapping Disk Populations with a 2-process Model and Residual Abundances
Authors: David H. Weinberg, Jon A. Holtzman, Jennifer A. Johnson, Christian Hayes, Sten Hasselquist, Matthew Shetrone, Yuan-Sen Ting, 源森 丁, Rachael L. Beaton, Timothy C. Beers, Jonathan C. Bird, Dmitry Bizyaev, Michael R. Blanton, Katia Cunha, José G. Fernández-Trincado, Peter M. Frinchaboy, D. A. García-Hernández, Emily Griffith, James W. Johnson, Henrik Jönsson, Richard R. Lane, Henry W. Leung, J. Ted Mackereth, Steven R. Majewski, Szabolcs Mészáros, Christian Nitschelm, Kaike Pan, Ricardo P. Schiavon, Donald P. Schneider, Mathias Schultheis, Verne Smith, Jennifer S. Sobeck, Keivan G. Stassun, Guy S. Stringfellow, Fiorenzo Vincenzo, John C. Wilson, Gail Zasowski
Figure caption: (a) Correlation matrix (Equation (32)) corresponding to the intrinsic covariance matrix in Figure 15(d). For visual scaling, note that diagonal elements have a magnitude of 1.0 by definition, the O-S coefficient is 0.37, the Ni–Mn coefficient is 0.26, and the O–V coefficient is 0.10. (b) Correlation matrix corresponding to the simulated-data covariance matrix (Figure 15(c)). Off-diagonal coefficients in this matrix are caused by measurement aberration.
Paper: Chemical Cartography with APOGEE: Mapping Disk Populations with a 2-process Model and Residual Abundances
Authors: David H. Weinberg, Jon A. Holtzman, Jennifer A. Johnson, Christian Hayes, Sten Hasselquist, Matthew Shetrone, Yuan-Sen Ting, 源森 丁, Rachael L. Beaton, Timothy C. Beers, Jonathan C. Bird, Dmitry Bizyaev, Michael R. Blanton, Katia Cunha, José G. Fernández-Trincado, Peter M. Frinchaboy, D. A. García-Hernández, Emily Griffith, James W. Johnson, Henrik Jönsson, Richard R. Lane, Henry W. Leung, J. Ted Mackereth, Steven R. Majewski, Szabolcs Mészáros, Christian Nitschelm, Kaike Pan, Ricardo P. Schiavon, Donald P. Schneider, Mathias Schultheis, Verne Smith, Jennifer S. Sobeck, Keivan G. Stassun, Guy S. Stringfellow, Fiorenzo Vincenzo, John C. Wilson, Gail Zasowski
Figure caption: Correlation of the 2-process amplitude ratio A Ia/A cc with stellar age estimated from the APOGEE spectra by AstroNN, in zones of R (columns) and ∣Z∣ (rows) as labeled. Red and blue points show stars in the low-Ia and high-Ia population, respectively. In the R = 7–9 kpc, ∣Z∣ < 0.5 kpc panel, black circles show the median age in bins of width 0.1 in A Ia/A cc. Black lines repeat this median sequence and are the same in all panels. To improve coverage of the inner Galaxy, this plot uses the SN100 sample.
Paper: Chemical Cartography with APOGEE: Mapping Disk Populations with a 2-process Model and Residual Abundances
Authors: David H. Weinberg, Jon A. Holtzman, Jennifer A. Johnson, Christian Hayes, Sten Hasselquist, Matthew Shetrone, Yuan-Sen Ting, 源森 丁, Rachael L. Beaton, Timothy C. Beers, Jonathan C. Bird, Dmitry Bizyaev, Michael R. Blanton, Katia Cunha, José G. Fernández-Trincado, Peter M. Frinchaboy, D. A. García-Hernández, Emily Griffith, James W. Johnson, Henrik Jönsson, Richard R. Lane, Henry W. Leung, J. Ted Mackereth, Steven R. Majewski, Szabolcs Mészáros, Christian Nitschelm, Kaike Pan, Ricardo P. Schiavon, Donald P. Schneider, Mathias Schultheis, Verne Smith, Jennifer S. Sobeck, Keivan G. Stassun, Guy S. Stringfellow, Fiorenzo Vincenzo, John C. Wilson, Gail Zasowski
Figure caption: Correlation of the abundance residual Δ[X/H] (in dex) with stellar age for eight selected elements as labeled. In the first and third columns, residuals are computed with respect to the median sequences, while in the second and fourth columns residuals are computed with respect to the 2-process model predictions. Red and blue points show stars in the low-Ia and high-Ia population, respectively. To reduce crowding, we plot only 25% of the stars.
Paper: Chemical Cartography with APOGEE: Mapping Disk Populations with a 2-process Model and Residual Abundances
Authors: David H. Weinberg, Jon A. Holtzman, Jennifer A. Johnson, Christian Hayes, Sten Hasselquist, Matthew Shetrone, Yuan-Sen Ting, 源森 丁, Rachael L. Beaton, Timothy C. Beers, Jonathan C. Bird, Dmitry Bizyaev, Michael R. Blanton, Katia Cunha, José G. Fernández-Trincado, Peter M. Frinchaboy, D. A. García-Hernández, Emily Griffith, James W. Johnson, Henrik Jönsson, Richard R. Lane, Henry W. Leung, J. Ted Mackereth, Steven R. Majewski, Szabolcs Mészáros, Christian Nitschelm, Kaike Pan, Ricardo P. Schiavon, Donald P. Schneider, Mathias Schultheis, Verne Smith, Jennifer S. Sobeck, Keivan G. Stassun, Guy S. Stringfellow, Fiorenzo Vincenzo, John C. Wilson, Gail Zasowski
Figure caption: Correlation of the Δ[Ca/H] and Δ[Mn/H] residual abundances with orbital eccentricity (left) and maximum midplane distance ﹩| {Z}_{\max }| ﹩ (right). As in Figure 18, residuals in the first and third columns are computed with respect to the median sequences, while those in the second and fourth columns are computed with respect to the 2-process model predictions. Red and blue points show stars in the low-Ia and high-Ia population, respectively. To reduce crowding, only 25% of the stars are plotted.
Paper: Chemical Cartography with APOGEE: Mapping Disk Populations with a 2-process Model and Residual Abundances
Authors: David H. Weinberg, Jon A. Holtzman, Jennifer A. Johnson, Christian Hayes, Sten Hasselquist, Matthew Shetrone, Yuan-Sen Ting, 源森 丁, Rachael L. Beaton, Timothy C. Beers, Jonathan C. Bird, Dmitry Bizyaev, Michael R. Blanton, Katia Cunha, José G. Fernández-Trincado, Peter M. Frinchaboy, D. A. García-Hernández, Emily Griffith, James W. Johnson, Henrik Jönsson, Richard R. Lane, Henry W. Leung, J. Ted Mackereth, Steven R. Majewski, Szabolcs Mészáros, Christian Nitschelm, Kaike Pan, Ricardo P. Schiavon, Donald P. Schneider, Mathias Schultheis, Verne Smith, Jennifer S. Sobeck, Keivan G. Stassun, Guy S. Stringfellow, Fiorenzo Vincenzo, John C. Wilson, Gail Zasowski
Figure caption: Bivariate distribution of 2-process residuals in [(C+N)/H] vs. [Ce/H]. Small dots show a random 10% subset of the full sample. Crosses show all sample stars that have ∣Δ[(C + N)/H]∣ > 0.1. The inset provides an expanded view of the core of the distribution, from −0.2 to 0.2 in Δ[Ce/H] and −0.1 to 0.1 in Δ[(C + N)/H]. Although residuals in the core of the distribution are anticorrelated, there is a population of rare stars with large positive deviations in (C+N) and Ce. Two examples of such stars are shown in Figure 20 below and discussed in Section 6.
Paper: Can We Detect Coronal Mass Ejections through Asymmetries of Sun-as-a-star Extreme-ultraviolet Spectral Line Profiles?
Authors: Zihao Yang, 子浩 杨, Hui Tian, 晖 田, Xianyong Bai, Yajie Chen, Yang Guo, Yingjie Zhu, 英杰 朱, Xin Cheng, Yuhang Gao, 宇航 高, Yu Xu, 昱 徐, Hechao Chen, Jiale Zhang
Figure caption: Determinations of CME-detectable instrumental parameters using Fe XII 195.12 Å. The two rows present two examples, based on the synthetic line profiles at different CME heights (h 2 = 34.20 Mm and h 9 = 55.30 Mm). The first column (panels (A) and (D)) presents the “flag” maps under solar activity levels of S CME = 1% and S AR = 5%. The color shades represent different values, ranging from 0 (dark) to 200 (white). The solid black lines mark the critical line. For S/N and ﹩\delta {\lambda }_{\mathrm{instr}}﹩ values above the critical line, over 80% of the line profile generations satisfy the 3σ criterion. The red curves are the smoothed curves of the black lines, which are used as the critical curves for CME detectability. The second column (panels (B) and (E)) presents Δv maps related to the accuracy of the obtained CME velocity, with S CME = 1% and S AR = 5%. The color shades represent Δv values. The white curves are the smoothed critical curves determined in panels (A) and (D). Above the critical curves, Δv can generally be accurately recovered. The third column (panels (C) and (F)) presents the δ R  I  maps related to the accuracy of the obtained CME peak intensity from line profiles with S CME = 1% and S AR = 5%. The different colors represent the values of δ R  I  = ∣R obs − R input∣/R input. The white curves are the smoothed critical curves. Generally, the δ R  I  values above the critical curves are small, meaning that the relative intensity can be accurately derived. S CME and S AR here are the initial areas at the height h 0.
Paper: Can We Detect Coronal Mass Ejections through Asymmetries of Sun-as-a-star Extreme-ultraviolet Spectral Line Profiles?
Authors: Zihao Yang, 子浩 杨, Hui Tian, 晖 田, Xianyong Bai, Yajie Chen, Yang Guo, Yingjie Zhu, 英杰 朱, Xin Cheng, Yuhang Gao, 宇航 高, Yu Xu, 昱 徐, Hechao Chen, Jiale Zhang
Figure caption: The overall constraints on instrumental parameters for detecting solar CMEs through line asymmetries using Sun-as-a-star spectral observations. The results are for the Fe XII 195.12 Å line. Rows (A)–(F) correspond to different heights during the propagation of the CME; the velocity and height are denoted in each row. Each column shows the results for different initial CME areas S CME = S CME(h 0). The colored curves in each small panel correspond to different initial S AR at the height of h 0 (e.g., from S AR − S CME = 1% (red) to S AR − S CME = 11% (blue)), and they are the Gaussian-smoothed critical curves determined from the 3σ criterion. The blue crosses mark the critical ﹩\delta {\lambda }_{\mathrm{instr}}﹩ determined when both the CME velocity and peak intensity can be accurately derived, and the red diamonds are those determined when only the accuracy of the CME velocity is evaluated.
Paper: New Fe I Level Energies and Line Identifications from Stellar Spectra. III. Initial Results from UV, Optical, and Infrared Spectra
Authors: Ruth C. Peterson, Robert L. Kurucz
Figure caption: Observations vs. calculations for the continuum-normalized intensity of the Sun and of the flux of Arcturus over 3.515–3.49 μ. On the x-axis appears the wavenumber in cm−1. On the y-axis is the intensity scale for the solar spectrum; the Arcturus spectrum is offset vertically by −0.1 for clarity. The observed spectrum for the Sun is that of ACE (Hase et al. 2010; green line). For Arcturus are shown both the winter (orange line) and summer (purple line) spectra of Hinkle et al. (1995). Superimposed as solid blue lines are spectral synthesis calculations (Section 6.2) for the center of the solar disk and the integrated flux of Arcturus. The positions of selected lines of molecular and neutral atomic species are indicated by × for Fe I and by color-coded + signs for the other species indicated. The diamonds beneath them highlight Fe I lines that arise from s5f jK levels. The s5f line at 2849.5 cm−1 was previously identified by Nave et al. (1994). The remaining five s5f lines, which fall above observed features that are unmatched except for line blends, are new identifications.
Paper: Can We Detect Coronal Mass Ejections through Asymmetries of Sun-as-a-star Extreme-ultraviolet Spectral Line Profiles?
Authors: Zihao Yang, 子浩 杨, Hui Tian, 晖 田, Xianyong Bai, Yajie Chen, Yang Guo, Yingjie Zhu, 英杰 朱, Xin Cheng, Yuhang Gao, 宇航 高, Yu Xu, 昱 徐, Hechao Chen, Jiale Zhang
Figure caption: The variations of R obs and related values for a fixed ﹩\delta {\lambda }_{\mathrm{instr}}﹩ as a function of S/N. The different colors represent the curves obtained for different ﹩\delta {\lambda }_{\mathrm{instr}}﹩. The figures are the results relating to Figure 7(C). (A) For a chosen ﹩\delta {\lambda }_{\mathrm{instr}}﹩, we plot the the value ﹩{\bar{R}}_{\mathrm{obs}}/{R}_{\mathrm{input}}﹩ as a function of S/N. It is clear that ﹩{\bar{R}}_{\mathrm{obs}}/{R}_{\mathrm{input}}﹩ will first go through large oscillations, due to the very low S/N, then generally decrease with increasing S/N, and finally approach a nearly constant value. (B) The value ﹩({\bar{R}}_{\mathrm{obs}}-{R}_{\mathrm{input}})/{R}_{\mathrm{input}}﹩ as a function of S/N. This panel shows the same trend as panel (A). (C) ﹩| {\bar{R}}_{\mathrm{obs}}-{R}_{\mathrm{input}}| /{R}_{\mathrm{input}}﹩ as a function of S/N. Due to the systematic deviations during the calculations, ﹩({\bar{R}}_{\mathrm{obs}}-{R}_{\mathrm{input}})/{R}_{\mathrm{input}}﹩ will decrease to a minimum near the critical S/N (the S/N determined from the critical curve at a given ﹩\delta {\lambda }_{\mathrm{instr}}﹩). This will result in a “reflection” of the ﹩({\bar{R}}_{\mathrm{obs}}-{R}_{\mathrm{input}})/{R}_{\mathrm{input}}﹩–S/N curve, which means that ﹩({\bar{R}}_{\mathrm{obs}}-{R}_{\mathrm{input}})/{R}_{\mathrm{input}}﹩ first decreases to a minimum, then slowly increases to almost a constant. Note that S CME and S AR are the initial areas at the height h 0.
Paper: Can We Detect Coronal Mass Ejections through Asymmetries of Sun-as-a-star Extreme-ultraviolet Spectral Line Profiles?
Authors: Zihao Yang, 子浩 杨, Hui Tian, 晖 田, Xianyong Bai, Yajie Chen, Yang Guo, Yingjie Zhu, 英杰 朱, Xin Cheng, Yuhang Gao, 宇航 高, Yu Xu, 昱 徐, Hechao Chen, Jiale Zhang
Figure caption: The averaged δ R  v  (panel (A)) and δ R  I  (panel (B)) values above the critical curves as functions of ﹩\delta {\lambda }_{\mathrm{instr}}﹩ at h 2 = 34.20 Mm with S CME = 1% using the Fe XII 195.12 Å line. The different black curves correspond to different S AR (ranging from 2% to 13%). The horizontal blue dashed lines represent the 30% level. The red crosses mark the points where δ R  v  and δ R  I  reach this level. The curves are Gaussian-smoothed. S CME and S AR refer to the initial areas at the height h 0.
Paper: Can We Detect Coronal Mass Ejections through Asymmetries of Sun-as-a-star Extreme-ultraviolet Spectral Line Profiles?
Authors: Zihao Yang, 子浩 杨, Hui Tian, 晖 田, Xianyong Bai, Yajie Chen, Yang Guo, Yingjie Zhu, 英杰 朱, Xin Cheng, Yuhang Gao, 宇航 高, Yu Xu, 昱 徐, Hechao Chen, Jiale Zhang
Figure caption: Similar to Figure 9, but for the velocity and height profiles shown in Figure 12. Note that we can hardly detect CME signals at the height of h 1 = 53.25 Mm, with a speed of around only 90 km s−1 (see row (A)), because the velocity is not large enough for the identification of blue-wing enhancement. Here, S CME and S AR are the initial areas at the height h 0.