adsabs/AstroCLIMB huggingface dataset:
Paper: KORG: A Modern 1D LTE Spectral Synthesis Package
Authors: Adam J. Wheeler, Matthew W. Abruzzo, Andrew R. Casey, Melissa K. Ness
Figure caption: The ratio of the absorption coefficient per KORG and per MARCS in the solar atmosphere (indexed by Rosseland mean opacity, τ ros). Agreement is good, except in the blue and ultraviolet where KORG uses more recent metal opacities and the effects of line blanketing (handled separately in KORG) in MARCS become large.
Paper: KORG: A Modern 1D LTE Spectral Synthesis Package
Authors: Adam J. Wheeler, Matthew W. Abruzzo, Andrew R. Casey, Melissa K. Ness
Figure caption: Same as Figure 5, but showing the synthetic spectrum of an HD122563-like star.
Paper: KORG: A Modern 1D LTE Spectral Synthesis Package
Authors: Adam J. Wheeler, Matthew W. Abruzzo, Andrew R. Casey, Melissa K. Ness
Figure caption: Same as Figure 5, but showing the synthetic spectrum of an Arcturus-like star.
Paper: KORG: A Modern 1D LTE Spectral Synthesis Package
Authors: Adam J. Wheeler, Matthew W. Abruzzo, Andrew R. Casey, Melissa K. Ness
Figure caption: Top left: part of the region with high-order Balmer lines for the metal-poor giant, along with the observed spectrum of HD122563. Bottom left: the same at the top left panel, but for solar parameters, alongside the observed solar spectrum. Right: H  α  in the metal-poor giant, along with the observed spectrum of HD122563. None of the codes correctly model the hydrogen lines in the metal-poor regime. Though SME is the closest to the observed high-order Balmer lines in the metal-poor regime, it predicts strong high-order Balmer lines in stars where they are not present. All synthetic spectra have been convolved to the observational resolution.
Paper: KORG: A Modern 1D LTE Spectral Synthesis Package
Authors: Adam J. Wheeler, Matthew W. Abruzzo, Andrew R. Casey, Melissa K. Ness
Figure caption: The time taken by each code for the syntheses in Section 3. Top: average compute time for the optical wavelength regions, which used very dense line lists. Bottom: compute time to synthesize spectra from 15000 to 15500 Å with the APOGEE DR16 line list, a more “realistic” use case. Note that the wavelength regions synthesized were larger than those plotted in Figures 5–8. SME includes a true scattering treatment that cannot be turned off, increasing its execution times.
Paper: KORG: A Modern 1D LTE Spectral Synthesis Package
Authors: Adam J. Wheeler, Matthew W. Abruzzo, Andrew R. Casey, Melissa K. Ness
Figure caption: Left: some lines from a C2 band in the Sun synthesized by the four codes and synthesized by KORG using the prescriptions for the chemical equilibrium constant, ﹩{K}_{{{\rm{C}}}_{2}}﹩, from the other codes. Differences in these prescriptions, primarily arising from differences in the dissociation energy, D 0, drive most of the disagreement. Right: the ratio of ﹩{K}_{{{\rm{C}}}_{2}}﹩ values per Barklem & Collet (2016; KORG's adopted values) and per the treatment used by another code. The dashed lines show the values obtained using the Barklem & Collet (2016) data adjusted for the D 0 used by another code. Uncertainty in D 0 drives most of the disagreement in the amount of C 2 present at the photosphere and most of the disagreement in the synthesized spectra at these wavelengths.
Paper: KORG: A Modern 1D LTE Spectral Synthesis Package
Authors: Adam J. Wheeler, Matthew W. Abruzzo, Andrew R. Casey, Melissa K. Ness
Figure caption: The difference in the rectified spectrum obtained for the Sun using the quadratic Bézier scheme and KORG's default scheme. The purple and brown lines show the result obtained using modified Bézier interpolation and cubic spline interpolation of α  λ , respectively. (This is separate from the interpolation of the source function, which uses Bézier curves in both cases.) The difference between all three methods is small compared to the difference in spectra obtained from different codes, but nevertheless larger than is ideal.
Paper: KORG: A Modern 1D LTE Spectral Synthesis Package
Authors: Adam J. Wheeler, Matthew W. Abruzzo, Andrew R. Casey, Melissa K. Ness
Figure caption: Time required to compute simultaneous derivative of spectrum with regard to N different element abundances, for varying N.
Paper: Kepler-102: Masses and Compositions for a Super-Earth and Sub-Neptune Orbiting an Active Star
Authors: Casey L. Brinkman, James Cadman, Lauren Weiss, Eric Gaidos, Ken Rice, Daniel Huber, Zachary R. Claytor, Aldo S. Bonomo, Lars A. Buchhave, Andrew Collier Cameron, Rosario Cosentino, Xavier Dumusque, Aldo F. Martinez Fiorenzano, Adriano Ghedina, Avet Harutyunyan, Andrew Howard, Howard Isaacson, David W. Latham, Mercedes López-Morales, Luca Malavolta, Giuseppina Micela, Emilio Molinari, Francesco Pepe, David F. Philips, Ennio Poretti, Alessandro Sozzetti, Stéphane Udry
Figure caption: Ternary plot for Kepler-102d (K-102d) showing the range of possible combinations of iron, rock, and ice in increments of 10% for a planet of fixed radius. The color bar shows the resulting mass of such a planet. Masses and volumes of different substances are calculated by solving equations of state (EOS) using assumed internal pressure and temperatures using Burnman 0.9 (Cottaar et al. 2016). Every point corresponds to a planet with physically allowed EOS for that combination of iron, rock, and ice—and all produce values for the mass of the planet within 1σ (large points) and 2σ (small points) of our measured mass value for that planet. The horizontal dotted lines show iron fraction, the dotted lines with negative slope show ice fraction, and the lines with positive slope show silicate fraction. The red circle highlights the composition that produces our best-fit mass of M  d  = 2.5 M ⊕.
Paper: Kepler-102: Masses and Compositions for a Super-Earth and Sub-Neptune Orbiting an Active Star
Authors: Casey L. Brinkman, James Cadman, Lauren Weiss, Eric Gaidos, Ken Rice, Daniel Huber, Zachary R. Claytor, Aldo S. Bonomo, Lars A. Buchhave, Andrew Collier Cameron, Rosario Cosentino, Xavier Dumusque, Aldo F. Martinez Fiorenzano, Adriano Ghedina, Avet Harutyunyan, Andrew Howard, Howard Isaacson, David W. Latham, Mercedes López-Morales, Luca Malavolta, Giuseppina Micela, Emilio Molinari, Francesco Pepe, David F. Philips, Ennio Poretti, Alessandro Sozzetti, Stéphane Udry
Figure caption: Mass (top panel) and density (bottom panel) are given as a function of radius for all known exoplanets with radius R < 4 R ⊕ that have radial velocity mass measurements (shown in gray). The size of the points (excluding Kepler-102d and Kepler-102e) scales inversely with the error of their mass measurement. The black line shows the mass–radius relation from Weiss & Marcy (2014). The density–radius curves for planets of solid iron, solid rock, and an Earth-like composition from Zeng et al. (2019) are shown, as well. Kepler-102d and Kepler-102e (K-102d and K-102e) are shown with their previous mass measurements, and for each model we explore in this paper (individual vs. combined data sets, with and without Gaussian process, GP, stellar noise modeling). For Kepler-102d, the marker for the combined data set without GP, as well as the marker for HARPS-N with GP, fall behind the marker for the full data set with GP.
Paper: Kepler-102: Masses and Compositions for a Super-Earth and Sub-Neptune Orbiting an Active Star
Authors: Casey L. Brinkman, James Cadman, Lauren Weiss, Eric Gaidos, Ken Rice, Daniel Huber, Zachary R. Claytor, Aldo S. Bonomo, Lars A. Buchhave, Andrew Collier Cameron, Rosario Cosentino, Xavier Dumusque, Aldo F. Martinez Fiorenzano, Adriano Ghedina, Avet Harutyunyan, Andrew Howard, Howard Isaacson, David W. Latham, Mercedes López-Morales, Luca Malavolta, Giuseppina Micela, Emilio Molinari, Francesco Pepe, David F. Philips, Ennio Poretti, Alessandro Sozzetti, Stéphane Udry
Figure caption: Corner plot showing the covariance of the semiamplitudes of Kepler-102d and Kepler-102e with the four hyperparameters from the quasiperiodic stellar noise model. The median value of each parameter is shown atop the distribution of values explored through a Markov Chain Monte Carlo algorithm plotted as a function of likelihood. The dashed lines through the distributions show the median value as well as the 1σ bounds on the parameter. The covariance plots show the likelihood of models with the parameters varying as a function of one another, and show the 0.5, 1.0, 1.5, and 2.0σ contours. K  d , K  e , and η 1 are in units of meters per second, while η 2 and η 3 are in units of days.
Paper: Kepler-102: Masses and Compositions for a Super-Earth and Sub-Neptune Orbiting an Active Star
Authors: Casey L. Brinkman, James Cadman, Lauren Weiss, Eric Gaidos, Ken Rice, Daniel Huber, Zachary R. Claytor, Aldo S. Bonomo, Lars A. Buchhave, Andrew Collier Cameron, Rosario Cosentino, Xavier Dumusque, Aldo F. Martinez Fiorenzano, Adriano Ghedina, Avet Harutyunyan, Andrew Howard, Howard Isaacson, David W. Latham, Mercedes López-Morales, Luca Malavolta, Giuseppina Micela, Emilio Molinari, Francesco Pepe, David F. Philips, Ennio Poretti, Alessandro Sozzetti, Stéphane Udry
Figure caption: Lomb–Scargle periodograms (LSPs) of the activity indicators for each radial velocity (RV) data set (S-index for HIRES, FWHM for HARPS-N) show a notable peak at 6.5 days, which is one quarter the rotation period (or third harmonic of the rotation period) of the star. In the HIRES S-index LSP we identify a very tentative signal at 25 days (within one standard deviation of the rotation period of the star), while in the HARPS-N FWHM LSP there is not an identifiable signal at the rotation period. The strong peak at the third harmonic of the rotation period suggests that the rotation period of the star is consistent over the timescale of RV data collection and is consistent with the rotation period derived from the Kepler photometry.
Paper: Kepler-102: Masses and Compositions for a Super-Earth and Sub-Neptune Orbiting an Active Star
Authors: Casey L. Brinkman, James Cadman, Lauren Weiss, Eric Gaidos, Ken Rice, Daniel Huber, Zachary R. Claytor, Aldo S. Bonomo, Lars A. Buchhave, Andrew Collier Cameron, Rosario Cosentino, Xavier Dumusque, Aldo F. Martinez Fiorenzano, Adriano Ghedina, Avet Harutyunyan, Andrew Howard, Howard Isaacson, David W. Latham, Mercedes López-Morales, Luca Malavolta, Giuseppina Micela, Emilio Molinari, Francesco Pepe, David F. Philips, Ennio Poretti, Alessandro Sozzetti, Stéphane Udry
Figure caption: Wavelet analysis for the 4 yr light curve of Kepler-102. The top panel shows the photometry over 17 quarters of Kepler data, where time is measured in Barycentric TESS Julian Date. The central panel shows the time-variable frequency decomposition. The right panel shows the time-integrated wavelet power spectrum of the light curve. The clear period of maximum power is at 27 days, suggesting that this is the rotation period of the star, which we use to inform our GP kernel. There is also significant power from 10 to 32 days, showing that spots form, evolve, and disappear over time. The peak at 13 days is likely a half-period alias from spots appearing on opposite sides of the star (Haywood et al. 2014; Basri & Nguyen 2018).
Paper: Kepler-102: Masses and Compositions for a Super-Earth and Sub-Neptune Orbiting an Active Star
Authors: Casey L. Brinkman, James Cadman, Lauren Weiss, Eric Gaidos, Ken Rice, Daniel Huber, Zachary R. Claytor, Aldo S. Bonomo, Lars A. Buchhave, Andrew Collier Cameron, Rosario Cosentino, Xavier Dumusque, Aldo F. Martinez Fiorenzano, Adriano Ghedina, Avet Harutyunyan, Andrew Howard, Howard Isaacson, David W. Latham, Mercedes López-Morales, Luca Malavolta, Giuseppina Micela, Emilio Molinari, Francesco Pepe, David F. Philips, Ennio Poretti, Alessandro Sozzetti, Stéphane Udry
Figure caption: PDCSAP flux of Kepler-102 over 17 quarters of observation, binned every 10 hr (open boxes). Each panel corresponds to one quarter of photometry. The blue curve shows the Gaussian process (GP) regression of the photometry with the best-fit kernel hyperparameters from Table 5. The gray shaded regions show the uncertainty in the GP model.
Paper: Kepler-102: Masses and Compositions for a Super-Earth and Sub-Neptune Orbiting an Active Star
Authors: Casey L. Brinkman, James Cadman, Lauren Weiss, Eric Gaidos, Ken Rice, Daniel Huber, Zachary R. Claytor, Aldo S. Bonomo, Lars A. Buchhave, Andrew Collier Cameron, Rosario Cosentino, Xavier Dumusque, Aldo F. Martinez Fiorenzano, Adriano Ghedina, Avet Harutyunyan, Andrew Howard, Howard Isaacson, David W. Latham, Mercedes López-Morales, Luca Malavolta, Giuseppina Micela, Emilio Molinari, Francesco Pepe, David F. Philips, Ennio Poretti, Alessandro Sozzetti, Stéphane Udry
Figure caption: Combined Keplerian orbit model for the planets and Gaussian process (GP) stellar noise fit. The best-fit kernel priors were found using the Kepler photometry in Figure 1 before application to the radial velocities (RVs). (a) RV vs. time as observed by HIRES and HARPS-North (pre and post CCD upgrade) are plotted with 1σ uncertainties. The blue line represents the total model of the planets and the GP regressions, with the gray region showing 1σ uncertainties. (b) Residuals of the model subtracted from the RVs. (c) The phase-folded RVs for Kepler-102d with the best-fit semiamplitude model overplotted. The red points are phase-folded weighted-mean RVs and their uncertainties. (d) Same as (c) but for Kepler-102e.
Paper: Kepler-102: Masses and Compositions for a Super-Earth and Sub-Neptune Orbiting an Active Star
Authors: Casey L. Brinkman, James Cadman, Lauren Weiss, Eric Gaidos, Ken Rice, Daniel Huber, Zachary R. Claytor, Aldo S. Bonomo, Lars A. Buchhave, Andrew Collier Cameron, Rosario Cosentino, Xavier Dumusque, Aldo F. Martinez Fiorenzano, Adriano Ghedina, Avet Harutyunyan, Andrew Howard, Howard Isaacson, David W. Latham, Mercedes López-Morales, Luca Malavolta, Giuseppina Micela, Emilio Molinari, Francesco Pepe, David F. Philips, Ennio Poretti, Alessandro Sozzetti, Stéphane Udry
Figure caption: Ternary plot for Kepler-102e (K-102e) solid core compositions, assuming a core radius of R  c  = 1.5 R ⊕. Large points represent planet compositions consistent with our measured mass of the planet to within 1σ, while smaller points represent compositions within 2σ. The planet core composition that most closely matches our measured value of M  e  = 4.7 M ⊕ is one of 40% iron and 60% silicate rock—more dense than the best-fit composition for Kepler-102d.
Paper: A Multilevel Scheduling Framework for Distributed Time-domain Large-area Sky Survey Telescope Array
Authors: Yajie Zhang, Ce Yu, Chao Sun, Zhaohui Shang, Yi Hu, Huiyu Zhi, Jinmao Yang, Shanjiang Tang
Figure caption: Simple representation of the input constraints and optimization objectives involved in the implementation of a telescope-array observation-scheduling algorithm. We propose to divide the constraints into five categories: task disturbances, predictable time disturbances, resource disturbances, real-time disturbances, and feedback disturbances. The scheduling algorithm also needs to support multiple science objectives.
Paper: A Multilevel Scheduling Framework for Distributed Time-domain Large-area Sky Survey Telescope Array
Authors: Yajie Zhang, Ce Yu, Chao Sun, Zhaohui Shang, Yi Hu, Huiyu Zhi, Jinmao Yang, Shanjiang Tang
Figure caption: Architecture of the telescope-array observation scheduler based on the multilevel scheduling model. It is divided into the telescope-array layer, the site layer, the global layer, and the record and display layer. The global layer consists of simulation data preprocessing, global scheduling decision, and observation feedback processing. The site layer mainly includes the generation of a task queue for each telescope while the telescope-array layer is responsible for observation execution. The record and display layer contains the recording of task completion, visualization, and evaluation.
Paper: A Multilevel Scheduling Framework for Distributed Time-domain Large-area Sky Survey Telescope Array
Authors: Yajie Zhang, Ce Yu, Chao Sun, Zhaohui Shang, Yi Hu, Huiyu Zhi, Jinmao Yang, Shanjiang Tang
Figure caption: Spatial distribution of the simulated field data set of size 1000. These are the sky regions where scheduled observations are needed in the sky survey mode of the telescope array. The R.A. range is ﹩\left[0^\circ ,358\buildrel{\circ}\over{.} 48\right]﹩, while the range in decl. is ﹩\left[-89\buildrel{\circ}\over{.} 05,76\buildrel{\circ}\over{.} 55\right]﹩.
Paper: A Multilevel Scheduling Framework for Distributed Time-domain Large-area Sky Survey Telescope Array
Authors: Yajie Zhang, Ce Yu, Chao Sun, Zhaohui Shang, Yi Hu, Huiyu Zhi, Jinmao Yang, Shanjiang Tang
Figure caption: Multilevel model of the scheduling problem of the telescope array in the process of a time-domain large-scale sky survey. The overall survey plan is coordinated and controlled by the global scheduler and the site schedulers (and telescope schedulers if necessary) from different levels.
Paper: A Multilevel Scheduling Framework for Distributed Time-domain Large-area Sky Survey Telescope Array
Authors: Yajie Zhang, Ce Yu, Chao Sun, Zhaohui Shang, Yi Hu, Huiyu Zhi, Jinmao Yang, Shanjiang Tang
Figure caption: Geographical distribution of the observation sites used in the experiments. We select 20 different observatories or telescopes from the Astropy package (Price-Whelan et al. 2018) to simulate the locations of the sites contained in a telescope array, which are marked by triangles.
Paper: A Multilevel Scheduling Framework for Distributed Time-domain Large-area Sky Survey Telescope Array
Authors: Yajie Zhang, Ce Yu, Chao Sun, Zhaohui Shang, Yi Hu, Huiyu Zhi, Jinmao Yang, Shanjiang Tang
Figure caption: Coverage of cumulative observation time (in minutes) for various data sets after the duration of 10 long-term scheduling processes (10 days). According to the objectives of large-scale time-domain survey missions, the scheduler needs to provide a uniform coverage of the visible sky within each field. It can be seen that, with the expansion of the scale of the sky survey, the uniformity performance will be better.
Paper: A Multilevel Scheduling Framework for Distributed Time-domain Large-area Sky Survey Telescope Array
Authors: Yajie Zhang, Ce Yu, Chao Sun, Zhaohui Shang, Yi Hu, Huiyu Zhi, Jinmao Yang, Shanjiang Tang
Figure caption: Coverage of cumulative observation time distributions and histograms of 5σ limiting magnitudes of the input 10 sites and 200 fields under various alt constraints after the duration of 10 long-term scheduling processes. The first column shows the locations of input sites and fields. Fields and times with poor observation quality are removed. Moreover, the fields around the zenith will be prioritized.
Paper: A Multilevel Scheduling Framework for Distributed Time-domain Large-area Sky Survey Telescope Array
Authors: Yajie Zhang, Ce Yu, Chao Sun, Zhaohui Shang, Yi Hu, Huiyu Zhi, Jinmao Yang, Shanjiang Tang
Figure caption: Scheduling performance evaluations on different number of sites and fields. Left: data sets of 10 sites and 50, 100, 200, 500, and 1000 fields, respectively. Right: data sets of 1000 fields and 1, 2, 5, 10, and 20 sites, respectively. With the increase in the data volume, the preprocessing time and global scheduling time increase almost linearly. Meanwhile, the time consumption ratio is also affected by the observation conditions.
Paper: A Multilevel Scheduling Framework for Distributed Time-domain Large-area Sky Survey Telescope Array
Authors: Yajie Zhang, Ce Yu, Chao Sun, Zhaohui Shang, Yi Hu, Huiyu Zhi, Jinmao Yang, Shanjiang Tang
Figure caption: Observation uniformity evaluations over a time span of one year. The first row represents the locations of the input five sites and 100 fields. The second row shows the results of coverage of cumulative observation time (in minutes) and the uniformity metrics. The annual uniformity of observations shows a stable behavior.
Paper: A Multilevel Scheduling Framework for Distributed Time-domain Large-area Sky Survey Telescope Array
Authors: Yajie Zhang, Ce Yu, Chao Sun, Zhaohui Shang, Yi Hu, Huiyu Zhi, Jinmao Yang, Shanjiang Tang
Figure caption: The distributions of the fields and the observation sites used are illustrated in the first row. In the second row, we show the coverage of the cumulative observation time (in minutes) for scenario 1 (left), scenario 2 (middle), and scenario 3 (right) after the duration of a one-month scheduling process.
Paper: A Multilevel Scheduling Framework for Distributed Time-domain Large-area Sky Survey Telescope Array
Authors: Yajie Zhang, Ce Yu, Chao Sun, Zhaohui Shang, Yi Hu, Huiyu Zhi, Jinmao Yang, Shanjiang Tang
Figure caption: Left: distribution of input fields. Middle and right: coverage of the cumulative observation time (in minutes) for scenario 4 and scenario 5, respectively, after the same duration of 10 long-term scheduling processes.
Paper: A Multilevel Scheduling Framework for Distributed Time-domain Large-area Sky Survey Telescope Array
Authors: Yajie Zhang, Ce Yu, Chao Sun, Zhaohui Shang, Yi Hu, Huiyu Zhi, Jinmao Yang, Shanjiang Tang
Figure caption: Histograms of the airmass and 5σ limiting magnitude results for scenario 1 (moon phase <5%), scenario 2 (moon phase 5%–50%), and scenario 3 (moon phase > 50%), after the duration of a one-month scheduling process.
Paper: A Multilevel Scheduling Framework for Distributed Time-domain Large-area Sky Survey Telescope Array
Authors: Yajie Zhang, Ce Yu, Chao Sun, Zhaohui Shang, Yi Hu, Huiyu Zhi, Jinmao Yang, Shanjiang Tang
Figure caption: Distribution of the input observation sites and airmass and 5σ limiting magnitude results after the same duration of 10 long-term scheduling processes for scenario 4 (first row) and scenario 5 (second row).
Paper: A Multilevel Scheduling Framework for Distributed Time-domain Large-area Sky Survey Telescope Array
Authors: Yajie Zhang, Ce Yu, Chao Sun, Zhaohui Shang, Yi Hu, Huiyu Zhi, Jinmao Yang, Shanjiang Tang
Figure caption: Schematic diagram of the lunar phase change of the one-month observation time in the experiments.
Paper: A Multilevel Scheduling Framework for Distributed Time-domain Large-area Sky Survey Telescope Array
Authors: Yajie Zhang, Ce Yu, Chao Sun, Zhaohui Shang, Yi Hu, Huiyu Zhi, Jinmao Yang, Shanjiang Tang
Figure caption: Histograms of the airmass and 5σ limiting magnitude results for scenario 6 (first row), scenario 7 (second row), and scenario 8 (third row), after the same duration of 10 long-term scheduling processes. The distributions of the fields and the observation sites are on the left and right sides, respectively.
Paper: A Multilevel Scheduling Framework for Distributed Time-domain Large-area Sky Survey Telescope Array
Authors: Yajie Zhang, Ce Yu, Chao Sun, Zhaohui Shang, Yi Hu, Huiyu Zhi, Jinmao Yang, Shanjiang Tang
Figure caption: Coverage of the cumulative observation time (in minutes) for scenario 6 (left), scenario 7 (middle), and scenario 8 (right) after the same duration of 10 long-term scheduling processes.
Paper: Unresolved Binaries and Multiples in the Intermediate Mass Range in Open Clusters: Pleiades, Alpha Per, Praesepe, and NGC 1039
Authors: Alina A. Malofeeva, Varvara O. Mikhnevich, Giovanni Carraro, Anton F. Seleznev
Figure caption: (H–W2)–W1 vs. W2–(BP–K) diagram for triple and quadruple unresolved stars with the primary component in the range between 0.5 and 1.8 M ⊙. Blue dots are triple stars. Orange dots are quadruple stars. The blue and orange lines correspond to triple and quadruple unresolved systems with equal components. The thin black lines are lines of equal q values in the following order (from the right to the left): q = 0, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 1.0.
Paper: Unresolved Binaries and Multiples in the Intermediate Mass Range in Open Clusters: Pleiades, Alpha Per, Praesepe, and NGC 1039
Authors: Alina A. Malofeeva, Varvara O. Mikhnevich, Giovanni Carraro, Anton F. Seleznev
Figure caption: (H–W2)–W1 vs. W2–(BP–K) diagram for Pleiades probable members (gray dots) with superimposed lines of constant q values (as in Figure 2), lines for multiple systems with equal components (as in Figure 2), and with the boundaries (thick solid and dashed lines) of the area of star counts.(The data used to create this figure are available.)