adsabs/AstroCLIMB huggingface dataset:
Paper: Investigating the Impact of Metallicity on Star Formation in the Outer Galaxy. I. VLT/KMOS Survey of Young Stellar Objects in Canis Major
Authors: Dominika Itrich, Agata Karska, Marta Sewiło, Lars E. Kristensen, Gregory J. Herczeg, Suzanne Ramsay, William J. Fischer, Benoît Tabone, Will R. M. Rocha, Maciej Koprowski, Ngân Lê, Beata Deka-Szymankiewicz
Figure caption: Comparison between the total visual extinction A V,total (Sewiło et al. 2019) and the extinction within the envelope A V,env. The latter was estimated assuming spherical envelope and a power-law density profile. See the text for details.
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: Snapshots of the colliding rubber rings at different times. The particle speed as a percentage of the sound speed c 0 is color coded. The speed distributions can be compared to Schäfer et al. (2016) with good agreement.
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: Width, height, and distance of the two rubber rings in Figure 1 as a function of time.
Paper: Data-constrained Solar Modeling with GX Simulator
Authors: Gelu M. Nita, Gregory D. Fleishman, Alexey A. Kuznetsov, Sergey A. Anfinogentov, Alexey G. Stupishin, Eduard P. Kontar, Samuel J. Schonfeld, James A. Klimchuk, Dale E. Gary
Figure caption: Snapshots of the AR11520 model generated by the script presented in Appendix A, illustrating different stages of the AMPP process. (a) The photospheric LOS magnetic field map, located at the bottom of a rectangular box coaligned with the observer’s LOS (blue lines). The inscribed rectangular box (red lines), which is aligned with the direction normal to the solar surface, defines the 3D volume in which the magnetic field extrapolation is performed. (b) The elements in (a) plus B  z , obtained by projecting the photospheric vector magnetic field HMI map onto the bottom boundary. Two projection options are provided by AMPP: the default cylindrical area projection (CEA), or a simple parallel projection (TOP), selected by using the “Geometrical Projection” radio button shown in Figure 1. (c) A subset of model field lines that do, or do not, close within the 3D model boundaries (green and yellow lines, respectively), illustrating the magnetic connectivity in the model. (d) The coronal temperature distribution along the closed field lines, which corresponds to the parameterized magneto-thermal model defined by the AMPP script (refer to Section 3.5 for a detailed description). A user-specified hydrostatic model is used for the volume outside these closed field lines.
Paper: Data-constrained Solar Modeling with GX Simulator
Authors: Gelu M. Nita, Gregory D. Fleishman, Alexey A. Kuznetsov, Sergey A. Anfinogentov, Alexey G. Stupishin, Eduard P. Kontar, Samuel J. Schonfeld, James A. Klimchuk, Dale E. Gary
Figure caption: The automated CHMP architecture and illustrative example. Left panel: The CHMP iterative flowchart. The outer loop steps through a user-defined grid of {a, b} parameter pairs for which the inner loop employs the gx_search4bestq.pro core macro to search for the optimal heating rate Q 0 by attempting to bring the absolute value of 〈χ 2〉 metrics below a predefined maximum threshold ϵ. Right panel: The output of the automated CHMP search using NoRH 17 GHz observational data for the same AR11520 GX Simulator model as previously tuned by Fleishman et al. (2021a). The image displays, in logarithmic scale, the 〈χ 2〉 corresponding to each investigated grid point. As indicated by the right-side color bar, the 〈χ 2〉 metrics range in this case between 6.70 and 215.3. The red rectangle marks the grid point corresponding to the best found metrics (a = 0.4, b = 1.4; 〈χ〉2 = 6.70), which may be compared with the 〈χ 2〉 = 13.98 metrics corresponding to the best parameter combination, (a = 1.0, b = 0.75), found by Fleishman et al. (2021a) by minimizing the σ 2 metrics.
Paper: Data-constrained Solar Modeling with GX Simulator
Authors: Gelu M. Nita, Gregory D. Fleishman, Alexey A. Kuznetsov, Sergey A. Anfinogentov, Alexey G. Stupishin, Eduard P. Kontar, Samuel J. Schonfeld, James A. Klimchuk, Dale E. Gary
Figure caption: GX Simulator Automatic Model Production Pipeline workflow.
Paper: Data-constrained Solar Modeling with GX Simulator
Authors: Gelu M. Nita, Gregory D. Fleishman, Alexey A. Kuznetsov, Sergey A. Anfinogentov, Alexey G. Stupishin, Eduard P. Kontar, Samuel J. Schonfeld, James A. Klimchuk, Dale E. Gary
Figure caption: Model-to-data comparison for the AR11520 model (reproduced from Fleishman et al. 2021a). Top left panel: The [12, 30, 80]% contours of the 5.7 GHz SSRT observational map (yellow) and the synthetic map (black) are shown on top of synthetic 5.7 GHz map background image. The alignment shifts, Δx and Δy, and the heating parameters, Q 0, a, and b, are indicated in the figure inset. Top right panel: corresponding model-to-data residual map normalized by the observed SSRT brightness temperature. Pearson cross-correlation coefficient, r, the averaged normalized residual, ρ, and the squared residual metrics, ρ 2, are indicated in the figure inset. Bottom row: the same data-to-model comparison as in the top row, between the 17 GHz synthetic image and the NoRH 17 GHz image.
Paper: Data-constrained Solar Modeling with GX Simulator
Authors: Gelu M. Nita, Gregory D. Fleishman, Alexey A. Kuznetsov, Sergey A. Anfinogentov, Alexey G. Stupishin, Eduard P. Kontar, Samuel J. Schonfeld, James A. Klimchuk, Dale E. Gary
Figure caption: Comparison of thermal electron densities and temperature pairs computed from the moments of the DDM distributions (Equation (6)) vs. those computed from moments of the DEM distributions (Equation (5)) for the ebtel_scale = 5_alpha = −2.5.sav table. Top row: n DDM vs. n DEM (panel (a)) and T DDM vs. T DEM (panel (b)) correlation plots indicating a good linear correlation quantified by the correlation coefficients indicated in each panel. Bottom row: comparison between the DEM-derived distributions (black histograms) and the corresponding DDM-derived distribution (gray filled histograms) indicating a systematic shift toward lower values of the latter.
Paper: Data-constrained Solar Modeling with GX Simulator
Authors: Gelu M. Nita, Gregory D. Fleishman, Alexey A. Kuznetsov, Sergey A. Anfinogentov, Alexey G. Stupishin, Eduard P. Kontar, Samuel J. Schonfeld, James A. Klimchuk, Dale E. Gary
Figure caption: GX Simulator CHMP Graphical User Interface (GX_CHMP). Panel (a): Search setup input fields tab. Panel (b): Output solution display area tab. Top left plot: Best 〈χ 2〉 (or ﹩{\sigma }_{\rho }^{2}﹩) metrics corresponding to each grid point. The intersection of dotted vertical and horizontal lines indicates a grid point selected by the user, which, in this case, corresponds to the best metrics found. Top right plot: model-convolved map (background) and model (black) and data (yellow) user-defined contours delimiting the ROI area. Bottom right plot: map of the ﹩{\sigma }_{\rho }^{2}﹩ metrics corresponding to the selected grid point in the top left panel. Bottom left plot: map of the χ 2 metrics corresponding the selected grid point in the top left pane. The regions outside the ROI area are set to neutral uniform values in both metrics maps.
Paper: Data-constrained Solar Modeling with GX Simulator
Authors: Gelu M. Nita, Gregory D. Fleishman, Alexey A. Kuznetsov, Sergey A. Anfinogentov, Alexey G. Stupishin, Eduard P. Kontar, Samuel J. Schonfeld, James A. Klimchuk, Dale E. Gary
Figure caption: Brightness temperature Stokes I (top row) and V (bottom row) at 5.7 GHz (left column) and 17 GHz (right column) produced by the IDL script listed in Appendix B.
Paper: Data-constrained Solar Modeling with GX Simulator
Authors: Gelu M. Nita, Gregory D. Fleishman, Alexey A. Kuznetsov, Sergey A. Anfinogentov, Alexey G. Stupishin, Eduard P. Kontar, Samuel J. Schonfeld, James A. Klimchuk, Dale E. Gary
Figure caption: Snapshot of the gx_ampp GUI application displaying a set of default settings and corresponding runtime execution messages that match the demo script create_box_20160220.pro included in the demo subfolder of the GX Simulator distribution.
Paper: Data-constrained Solar Modeling with GX Simulator
Authors: Gelu M. Nita, Gregory D. Fleishman, Alexey A. Kuznetsov, Sergey A. Anfinogentov, Alexey G. Stupishin, Eduard P. Kontar, Samuel J. Schonfeld, James A. Klimchuk, Dale E. Gary
Figure caption: Flowchart of the production of initial and boundary conditions. Gray boxes represent individual steps of the pipeline, while intermediate data products are shown as arrows with labels.
Paper: Data-constrained Solar Modeling with GX Simulator
Authors: Gelu M. Nita, Gregory D. Fleishman, Alexey A. Kuznetsov, Sergey A. Anfinogentov, Alexey G. Stupishin, Eduard P. Kontar, Samuel J. Schonfeld, James A. Klimchuk, Dale E. Gary
Figure caption: Illustration of the workflow from observation to simulated images. (1) The multifrequency EOVSA images for one time frame in the 2017 September 4 flare overlaid on an AIA 131 A image, showing two widely separated footpoints. (2) The HMI vector magnetogram (background gray scale is B  z ) is used to calculate an NLFFF magnetic model from which a flux tube central field line is identified. (3) Other imaging data such as this comparison with RHESSI HXRs are used to refine the choice of central field line. (4) The model atmosphere parameters are extracted along the chosen field line. (5) The model parameters are used with a 1D Fokker–Planck code, starting from some assumed injected distribution and adjustable diffusion coefficients, to calculate (6) the energy distribution as a function of position and time. The energy distributions along the loop are then used by GX Simulator to calculate (7) the microwave emission maps, which are then (8) convolved with the EOVSA's PSF for quantitative comparison with EOVSA data.
Paper: Data-constrained Solar Modeling with GX Simulator
Authors: Gelu M. Nita, Gregory D. Fleishman, Alexey A. Kuznetsov, Sergey A. Anfinogentov, Alexey G. Stupishin, Eduard P. Kontar, Samuel J. Schonfeld, James A. Klimchuk, Dale E. Gary
Figure caption: Left panel: OSPEX fit X-ray model spectrum for the same instance of the 2017 September 4 flare used for illustration purposes in Figure 12, showing observed photon spectrum (symbols), total OSPEX fit spectrum (green), direct OSPEX fit spectrum (blue), and OSPEX Albedo contribution (red). The parameters estimated from this fit are thermal electrons emission measure EM = 3 × 1048 cm−3, plasma temperature T = 1.51 keV = 1.75 × 107 K, nonthermal electrons density n th = 2.3 × 1034 electrons s−1, nonthermal electron energy index δ = 3.64, and nonthermal electron minimum cutoff energy ﹩{E}_{\min }=19.0\,\mathrm{keV}﹩. Right panel: The model X-ray spectrum generated by GX Simulator from a flaring loop model corresponding to the OSPEX fit parameters.
Paper: Data-constrained Solar Modeling with GX Simulator
Authors: Gelu M. Nita, Gregory D. Fleishman, Alexey A. Kuznetsov, Sergey A. Anfinogentov, Alexey G. Stupishin, Eduard P. Kontar, Samuel J. Schonfeld, James A. Klimchuk, Dale E. Gary
Figure caption: Dynamic 3D modeling with GX Simulator for the peak of the SOL2015-06-22T17:50 flare observed with EOVSA. Top row: Analytically defined nonthermal electrons distributions in a system formed by four loops. Middle row: Model (lines) to EOVSA data (symbols) spectral comparisons and relative residuals, corresponding to the selected time frames illustrated in the top row. Bottom row: EOVSA Dynamic spectrum during the fitting interval (2.4–18 GHz, 4 s time resolution), with vertical red lines showing the times illustrated in the upper rows. (Adapted from Fleishman et al. 2018.)
Paper: Data-constrained Solar Modeling with GX Simulator
Authors: Gelu M. Nita, Gregory D. Fleishman, Alexey A. Kuznetsov, Sergey A. Anfinogentov, Alexey G. Stupishin, Eduard P. Kontar, Samuel J. Schonfeld, James A. Klimchuk, Dale E. Gary
Figure caption: Comparison between the brightness temperature maps for AR 11520 computed using the full DEM/DDM treatment and the isothermal approximation, (shown on the top row in Figure 5), at frequencies 5.7 GHz (left column) and 17 GHz (right column). Top row: maps based on the multithermal DEM/DDM treatment. Bottom row: the respective difference maps. The color bars are in units of megakelvin.
Paper: A Catalog of Young Stellar Objects from the LAMOST and ZTF Surveys
Authors: Jingyi Zhang, Yanxia Zhang, Zihan Kang, Changhua Li, Yongheng Zhao
Figure caption: The self-paced ensemble pipeline from Liu et al. (2020). Instead of simply balancing the data or directly adjusting class weights, classification hardness is taken into account over the data set, and the most informative majority data samples are iteratively selected according to the hardness distribution. The undersampling strategy is controlled by a self-paced procedure, which enables SPE to gradually focus on the harder data samples but still retain the information of the majority sample to prevent overfitting.
Paper: A Catalog of Young Stellar Objects from the LAMOST and ZTF Surveys
Authors: Jingyi Zhang, Yanxia Zhang, Zihan Kang, Changhua Li, Yongheng Zhao
Figure caption: The spectrum of LAMOST spec-56639-GAC061N32B1_sp15-079. It shows strong Hα emission at 6562.8 Å and TiO absorption lines at 7050–7150 Å. Strongly accreting objects may produce emission lines other than Hα, such as [He I] emission lines at 6677.6 Å and 7064.6 Å, depicted in the small enlarged panel. It is concluded that these lines are a sign of accretion.
Paper: A Catalog of Young Stellar Objects from the LAMOST and ZTF Surveys
Authors: Jingyi Zhang, Yanxia Zhang, Zihan Kang, Changhua Li, Yongheng Zhao
Figure caption: The spectrum of LAMOST spec-56617-VB081S05V1_sp06-026. It shows strong Hα emission at 6562.8 Å and TiO absorption lines at 7050–7150 Å. The spectrum also shows the [N II] doublet at 6548.4 Å and 6583.4 Å, in the small enlarged panel. These lines are a sign of ionized gas, so this source may be related to Herbig–Haro objects.
Paper: A Catalog of Young Stellar Objects from the LAMOST and ZTF Surveys
Authors: Jingyi Zhang, Yanxia Zhang, Zihan Kang, Changhua Li, Yongheng Zhao
Figure caption: Confusion matrix of the SPE classifier.
Paper: A Catalog of Young Stellar Objects from the LAMOST and ZTF Surveys
Authors: Jingyi Zhang, Yanxia Zhang, Zihan Kang, Changhua Li, Yongheng Zhao
Figure caption: The distribution of different kinds of objects in the 2d feature space using t-distributed stochastic neighbor embedding (t-SNE). Here no hyperparameter optimization was done for t-SNE.
Paper: A Catalog of Young Stellar Objects from the LAMOST and ZTF Surveys
Authors: Jingyi Zhang, Yanxia Zhang, Zihan Kang, Changhua Li, Yongheng Zhao
Figure caption: The distribution of 238 new YSOs and 1740 known YSOs in an H-R diagram.
Paper: A Catalog of Young Stellar Objects from the LAMOST and ZTF Surveys
Authors: Jingyi Zhang, Yanxia Zhang, Zihan Kang, Changhua Li, Yongheng Zhao
Figure caption: Normalized probability distribution of 15 subclasses for 868,371 ZTF sources. The red lines denote the positions of the median probability for each class and the green lines denote the 5th and 95th percentiles.
Paper: A Catalog of Young Stellar Objects from the LAMOST and ZTF Surveys
Authors: Jingyi Zhang, Yanxia Zhang, Zihan Kang, Changhua Li, Yongheng Zhao
Figure caption: The YSO candidates in Galactic coordinates. The yellow points represent 8210 YSO candidates from ZTF and the dark blue points represent 833 YSO candidates from ZTF–LAMOST.
Paper: LSST Survey Strategy in the Galactic Plane and Magellanic Clouds
Authors: R. A. Street, X. Li, S. Khakpash, E. Bellm, L. Girardi, L. Jones, N. S. Abrams, Y. Tsapras, M. P. G. Hundertmark, E. Bachelet, P. Gandhi, P. Szkody, W. I. Clarkson, R. Szabó, L. Prisinzano, R. Bonito, D. A. H. Buckley, J. P. Marais, R. Di Stefano
Figure caption: Heatmaps of the “%ofPriority” footprint area monitored at sufficient cadence to characterize variability τ var = 11 days, for simulations in the vary_gp (top) and plane_priority (bottom) families. In the bottom panels, the left plot summarizes the “pbf” simulations without the pencil-beam fields, while the right plot shows the “pbt” simulations that include them.
Paper: LSST Survey Strategy in the Galactic Plane and Magellanic Clouds
Authors: R. A. Street, X. Li, S. Khakpash, E. Bellm, L. Girardi, L. Jones, N. S. Abrams, Y. Tsapras, M. P. G. Hundertmark, E. Bachelet, P. Gandhi, P. Szkody, W. I. Clarkson, R. Szabó, L. Prisinzano, R. Bonito, D. A. H. Buckley, J. P. Marais, R. Di Stefano
Figure caption: Left: heatmap of the VisitIntervalMetric calculated for the Galactic Bulge region for variability τ var = 10 days, for a range of rolling cadence strategies. Right: heatmap of the SVGM calculated for all regions of interest for variability timescales τ var = 100 days.
Paper: Ionized-gas Metallicity of the Strong [O III]λ5007 Emission-line Compact Galaxies in the LAMOST Survey
Authors: Siqi Liu, 思琦 刘, A-Li Luo, 阿理 罗, Wei Zhang, 伟 张, Xiao Kong, 啸 孔, Yan-Xia Zhang, 彦霞 张, Shi-Yin Shen, 世银 沈, Yong-Heng Zhao, 永恒 赵
Figure caption: The Hγ and [O III]λ4363 emission lines fitted with the Gaussian profiles (left panel) and histogram of the measurement of each realization (right panel). In the left panel, the flux of each realization is marked with a black line, and the fitted Gaussian profile is marked with an orange curve. In the right panel, the mean value of the distribution is marked with the black solid line, and the 1σ region is marked with the dashed lines.
Paper: LSST Survey Strategy in the Galactic Plane and Magellanic Clouds
Authors: R. A. Street, X. Li, S. Khakpash, E. Bellm, L. Girardi, L. Jones, N. S. Abrams, Y. Tsapras, M. P. G. Hundertmark, E. Bachelet, P. Gandhi, P. Szkody, W. I. Clarkson, R. Szabó, L. Prisinzano, R. Bonito, D. A. H. Buckley, J. P. Marais, R. Di Stefano
Figure caption: Maps of the proposed revised survey regions in the Galactic Plane and Magellanic Clouds in Mollweide projections. Left: wider region selected with a projected pixel priority threshold of 1.5. Right: smaller region selected with a threshold of 2.0.
Paper: Ionized-gas Metallicity of the Strong [O III]λ5007 Emission-line Compact Galaxies in the LAMOST Survey
Authors: Siqi Liu, 思琦 刘, A-Li Luo, 阿理 罗, Wei Zhang, 伟 张, Xiao Kong, 啸 孔, Yan-Xia Zhang, 彦霞 张, Shi-Yin Shen, 世银 沈, Yong-Heng Zhao, 永恒 赵
Figure caption: BPT diagram for the selected sources. Only “SF” and “comp” sources are kept for further analysis.
Paper: Ionized-gas Metallicity of the Strong [O III]λ5007 Emission-line Compact Galaxies in the LAMOST Survey
Authors: Siqi Liu, 思琦 刘, A-Li Luo, 阿理 罗, Wei Zhang, 伟 张, Xiao Kong, 啸 孔, Yan-Xia Zhang, 彦霞 张, Shi-Yin Shen, 世银 沈, Yong-Heng Zhao, 永恒 赵
Figure caption: An example spectrum of the strong [O III]λ5007 emission-line compact galaxy. The emission lines used for following metallicity and kinematics measurements and discussions are marked at different wavelengths. The gray-shaded regions mark the flux error.
Paper: Ionized-gas Metallicity of the Strong [O III]λ5007 Emission-line Compact Galaxies in the LAMOST Survey
Authors: Siqi Liu, 思琦 刘, A-Li Luo, 阿理 罗, Wei Zhang, 伟 张, Xiao Kong, 啸 孔, Yan-Xia Zhang, 彦霞 张, Shi-Yin Shen, 世银 沈, Yong-Heng Zhao, 永恒 赵
Figure caption: The distribution of the flux ratio of Hα/Hβ (left panel) and the E(B − V) (right panel).
Paper: Ionized-gas Metallicity of the Strong [O III]λ5007 Emission-line Compact Galaxies in the LAMOST Survey
Authors: Siqi Liu, 思琦 刘, A-Li Luo, 阿理 罗, Wei Zhang, 伟 张, Xiao Kong, 啸 孔, Yan-Xia Zhang, 彦霞 张, Shi-Yin Shen, 世银 沈, Yong-Heng Zhao, 永恒 赵
Figure caption: The distribution of the rest-frame EWs of the [O III]λ5007 emission lines.
Paper: Minor Mergers Are Not Enough: The Importance of Major Mergers during Brightest Cluster Galaxy Assembly
Authors: Matthias Kluge, Ralf Bender
Figure caption: Evolution of simulated SB profiles (Hilz et al. 2013; continuous lines) for the minor and major-merger scenarios, compared to the observed profiles (dashed lines). The SB profile of the simulated pre-merger galaxy is shown by continuous black line. It is scaled to match the average profile of the luminous normal Es (black dashed line): r e = 7 kpc; ﹩{\mathrm{SB}}_{{\rm{e}}}=23\,g^{\prime} ﹩ mag arcsec−2; n = 3.9. The green line is the SB profile after five generations of minor mergers with mass ratio 1:5 (r e = 33 kpc;  ﹩{\mathrm{SB}}_{{\rm{e}}}=26.1\,g^{\prime} ﹩ mag arcsec−2; n = 9.5). The orange line shows the resulting SB profile after one major merger with equal mass (r e = 13.2 kpc;  ﹩{\mathrm{SB}}_{{\rm{e}}}=23.8\,g^{\prime} ﹩ mag arcsec−2; n = 5.7). Here, the accreted mass is deposited also at smaller radii compared to the minor-merger scenario. The continuous red line is the extrapolated result after three generations of equal-mass mergers (r e = 45.6 kpc;  ﹩{\mathrm{SB}}_{{\rm{e}}}=25.1\,g^{\prime} ﹩ mag arcsec−2; 6.9 < n < 8.1; L tot = 7.8L seed). This agrees well with the mean SB profile of the BCGs (dashed red line).
Paper: Minor Mergers Are Not Enough: The Importance of Major Mergers during Brightest Cluster Galaxy Assembly
Authors: Matthias Kluge, Ralf Bender
Figure caption: Lick indices against central velocity dispersion for BCGs (red) and normal Es (gray). The gray contours mark the locations where the kernel density estimate drops to 50% and 10% relative to the peak. The uncorrected dispersion σ 0 instead of σ e8 is used to enlarge the sample size to 1908 galaxies. The wavelength intervals for the indices can be found at https://mfouesneau.github.io/pyphot/licks.html.
Paper: Minor Mergers Are Not Enough: The Importance of Major Mergers during Brightest Cluster Galaxy Assembly
Authors: Matthias Kluge, Ralf Bender
Figure caption: All BCG (red) and luminous normal E (black) SB profiles along the effective axis ﹩r=\sqrt{{ab}}﹩. For each luminous normal E exists at least one BCG with similar central stellar velocity dispersion. The SB profiles of the luminous normal Es are extrapolated beyond ﹩\mathrm{SB}\gt 27\,g\,\mathrm{mag}\,{\mathrm{arcsec}}^{-2}﹩, using the best-fit Sérsic function. The inner region is marked with gray shading, where the profiles are not comparable. Contrary to the BCGs, the luminous normal E profiles are measured on seeing-limited data. The dashed lines are the mean SB profiles.
Paper: Minor Mergers Are Not Enough: The Importance of Major Mergers during Brightest Cluster Galaxy Assembly
Authors: Matthias Kluge, Ralf Bender
Figure caption: Mgb absorption line strength vs. total luminosity relation for BCGs (red) and normal Es (black). The best-fit lines are analogous to those in Figure 9. The axes are not spaced equally, because the dynamic range is much smaller in Mgb than in L tot.
Paper: Minor Mergers Are Not Enough: The Importance of Major Mergers during Brightest Cluster Galaxy Assembly
Authors: Matthias Kluge, Ralf Bender
Figure caption: Radial EL fraction of all BCGs (black lines) according to Equation (18). The reference luminous normal Es are selected based on their central velocity dispersion (left panel) or central Mgb absorption line strength (right panel). The orange line is the median profile, and the orange shades encompass the 16th and 84th percentiles.
Paper: Minor Mergers Are Not Enough: The Importance of Major Mergers during Brightest Cluster Galaxy Assembly
Authors: Matthias Kluge, Ralf Bender
Figure caption: LRS2 uv and orange channel spectra of 75 BCGs. The LRS2 spectra are available.(The data used to create this figure are available.)