adsabs/AstroCLIMB huggingface dataset:
Paper: SDSS-IV MaNGA: pyPipe3D Analysis Release for 10,000 Galaxies
Authors: S. F. Sánchez, J. K. Barrera-Ballesteros, E. Lacerda, A. Mejía-Narvaez, A. Camps-Fariña, Gustavo Bruzual, C. Espinosa-Ponce, A. Rodríguez-Puebla, A. R. Calette, H. Ibarra-Medel, V. Avila-Reese, H. Hernandez-Toledo, M. A. Bershady, M. Cano-Diaz, A. M. Munguia-Cordova
Figure caption: Comparison between the derivation of the SFR based on the dust-corrected Hα luminosity (SFRHα ) and four different estimates based on the stellar population decomposition, performed as part of the pyPipe3D analysis (SFRssp), for three time ranges: 10 Myr (top left panel), 32 Myr (top right panel), and 100 Myr (bottom left panel), as well as the average of the three (bottom right panel). We exclude those galaxies without evidence of SF activity from this comparison by excluding those ones for which the ∣EW(Hα)∣ < 3 Å at the effective radius. We adopt the same format as described in Figure 19. Details of the compared quantities are given in Section 5.3.
Paper: SDSS-IV MaNGA: pyPipe3D Analysis Release for 10,000 Galaxies
Authors: S. F. Sánchez, J. K. Barrera-Ballesteros, E. Lacerda, A. Mejía-Narvaez, A. Camps-Fariña, Gustavo Bruzual, C. Espinosa-Ponce, A. Rodríguez-Puebla, A. R. Calette, H. Ibarra-Medel, V. Avila-Reese, H. Hernandez-Toledo, M. A. Bershady, M. Cano-Diaz, A. M. Munguia-Cordova
Figure caption: Comparison between a set of ionized gas properties derived using pyPipe3D and those distributed as part of the MaNGA DAP DR for the full DR17 sample. We adopt the same format as in Figure 19. From left to right, and from top to bottom: Hα flux (F Hα,cen), equivalent width of Hα (EWHα,cen), Hα-to-Hβ line ratio, [O III]-to-Hα line ratio, [N II]-to-Hα line ratio, and [S II]-to-Hα line ratio, all of them derived for the central aperture. For details of these quantities, see Section 5.3 and the MaNGA DAP presentation article (Westfall et al. 2019).
Paper: SDSS-IV MaNGA: pyPipe3D Analysis Release for 10,000 Galaxies
Authors: S. F. Sánchez, J. K. Barrera-Ballesteros, E. Lacerda, A. Mejía-Narvaez, A. Camps-Fariña, Gustavo Bruzual, C. Espinosa-Ponce, A. Rodríguez-Puebla, A. R. Calette, H. Ibarra-Medel, V. Avila-Reese, H. Hernandez-Toledo, M. A. Bershady, M. Cano-Diaz, A. M. Munguia-Cordova
Figure caption: Comparison between the LW age (﹩{{ \mathcal A }}_{\star ,L}﹩) and metallicity (﹩{{ \mathcal Z }}_{\star ,L}﹩) at the effective radius, together with their corresponding radial gradients (∇﹩{{ \mathcal A }}_{\star ,L}﹩ and ∇﹩{{ \mathcal Z }}_{\star ,L}﹩), derived by us and the FIREFLY MaNGA DR17 VAC values. We adopt the same format as Figure 19. The details of the compared quantities are given in Section 5.3 and in the FIREFLY presentation article (Goddard et al. 2017).
Paper: SDSS-IV MaNGA: pyPipe3D Analysis Release for 10,000 Galaxies
Authors: S. F. Sánchez, J. K. Barrera-Ballesteros, E. Lacerda, A. Mejía-Narvaez, A. Camps-Fariña, Gustavo Bruzual, C. Espinosa-Ponce, A. Rodríguez-Puebla, A. R. Calette, H. Ibarra-Medel, V. Avila-Reese, H. Hernandez-Toledo, M. A. Bershady, M. Cano-Diaz, A. M. Munguia-Cordova
Figure caption: Stellar-mass function derived using the the volume corrections included in our final catalog (solid white circles) compared with the one derived using a volume-complete sample extracted from the NSA catalog (Blanton et al. 2017), shifted in mass, to correct for the different adopted IMFs, and in density, to match our adopted cosmology (i.e., h = 0.73). The agreement is particularly good for the mass range above 109 M ⊙.
Paper: SDSS-IV MaNGA: pyPipe3D Analysis Release for 10,000 Galaxies
Authors: S. F. Sánchez, J. K. Barrera-Ballesteros, E. Lacerda, A. Mejía-Narvaez, A. Camps-Fariña, Gustavo Bruzual, C. Espinosa-Ponce, A. Rodríguez-Puebla, A. R. Calette, H. Ibarra-Medel, V. Avila-Reese, H. Hernandez-Toledo, M. A. Bershady, M. Cano-Diaz, A. M. Munguia-Cordova
Figure caption: Comparison between the morphological classification included in our final catalog (Section 4.3) and the values reported by DS22. The violin plots show the distribution of the DS22 T-type parameter for each of our morphological classes. The upper panel shows a histogram of the number of galaxies in each morphological bin in a logarithmic scale.
Paper: An Atlas of Convection in Main-sequence Stars
Authors: Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello
Figure caption: The Eddington ratios with the full luminosity ΓEdd (left) and the radiative luminosity (right) are shown in terms of ﹩\mathrm{log}{T}_{\mathrm{eff}}﹩/spectral type and ﹩\mathrm{log}L﹩ for stellar models with FeCZs and Milky Way metallicity Z = 0.014. Note that while ΓEdd is an input parameter and does not depend on a specific theory of convection, ﹩{{\rm{\Gamma }}}_{\mathrm{Edd}}^{\mathrm{rad}}﹩ is an output of such a theory. Regions with Ra < Racrit are stable to convection and shaded in gray.
Paper: An Atlas of Convection in Main-sequence Stars
Authors: Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello
Figure caption: The Rossby number Ro (left) and turnover time t conv (right) are shown in terms of ﹩\mathrm{log}{T}_{\mathrm{eff}}﹩/spectral type and ﹩\mathrm{log}L﹩ for stellar models with FeCZs and Milky Way metallicity Z = 0.014. Note that both Ro and t conv are outputs of a theory of convection. Regions with Ra < Racrit are stable to convection and shaded in gray.
Paper: An Atlas of Convection in Main-sequence Stars
Authors: Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello
Figure caption: The convection optical depth τ CZ (left) and the optical depth to the surface τ outer (right) are shown in terms of ﹩\mathrm{log}{T}_{\mathrm{eff}}﹩/spectral type and ﹩\mathrm{log}L﹩ for stellar models with FeCZs and Milky Way metallicity Z = 0.014. Note that both of these are input parameters and do not depend on a specific theory of convection. Regions with Ra < Racrit are stable to convection and shaded in gray.
Paper: An Atlas of Convection in Main-sequence Stars
Authors: Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello
Figure caption: The Péclet number Pe (left ) and F conv/F (right) are shown in terms of ﹩\mathrm{log}{T}_{\mathrm{eff}}﹩/spectral type and ﹩\mathrm{log}L﹩ for stellar models with FeCZs and Milky Way metallicity Z = 0.014. Note that both Pe and F conv/F are outputs of a theory of convection. Regions with Ra < Racrit are stable to convection and shaded in gray.
Paper: An Atlas of Convection in Main-sequence Stars
Authors: Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello
Figure caption: The Rayleigh number Ra (left) and Reynolds number Re (right) are shown in terms of ﹩\mathrm{log}{T}_{\mathrm{eff}}﹩/spectral type and ﹩\mathrm{log}L﹩ for stellar models with FeCZs and Milky Way metallicity Z = 0.014. Note that while the Rayleigh number is an input parameter and does not depend on a specific theory of convection, the Reynolds number is an output of such a theory. Regions with Ra < Racrit are stable to convection and shaded in gray.
Paper: An Atlas of Convection in Main-sequence Stars
Authors: Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello
Figure caption: The Prandtl number Pr (left) and magnetic Prandtl number Pm (right) are shown in terms of ﹩\mathrm{log}{T}_{\mathrm{eff}}﹩/spectral type and ﹩\mathrm{log}L﹩ for stellar models with FeCZs and Milky Way metallicity Z = 0.014. Note that both Pr and Pm are input parameters and so do not depend on a specific theory of convection. Regions with Ra < Racrit are stable to convection and shaded in gray.
Paper: An Atlas of Convection in Main-sequence Stars
Authors: Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello
Figure caption: The Ekman number Ek is shown in terms of ﹩\mathrm{log}{T}_{\mathrm{eff}}﹩/spectral type and ﹩\mathrm{log}L﹩ for stellar models with FeCZs and Milky Way metallicity Z = 0.014. Note that the Ekman number is an input parameter and does not depend on a specific theory of convection. Regions with Ra < Racrit are stable to convection and shaded in gray.
Paper: An Atlas of Convection in Main-sequence Stars
Authors: Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello
Figure caption: The radiation pressure ratio β rad is shown in terms of ﹩\mathrm{log}{T}_{\mathrm{eff}}﹩/spectral type and ﹩\mathrm{log}L﹩ for stellar models with FeCZs and Milky Way metallicity Z = 0.014. Note that this ratio is an input parameter and does not depend on a specific theory of convection. Regions with Ra < Racrit are stable to convection and shaded in gray.
Paper: An Atlas of Convection in Main-sequence Stars
Authors: Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello
Figure caption: The Mach number computed with the isothermal sound speed is shown in terms of ﹩\mathrm{log}{T}_{\mathrm{eff}}﹩ and ﹩\mathrm{log}L﹩ for stellar models with FeCZs and Milky Way metallicity Z = 0.014.
Paper: Mass and Age Determination of the LAMOST Data with Different Machine-learning Methods
Authors: Qi-Da Li, Hai-Feng Wang, Yang-Ping Luo, Qing Li, Li-Cai Deng, Yuan-Sen Ting
Figure caption: The predicted results of our test data sets using RF. Different rows represent different groups of samples, and different columns show the dispersion (M ☉, Gyr), absolute error (M ☉, Gyr), and relative error, respectively. Dispersion means the standard deviation of the predicted ages/mass minus the true values in the catalog we used, the absolute error is the predicted value minus the true value, and the relative error is the predicted value minus the true value divided by the true value; notice in this work we use the median relative error for the final precision uniformly The dispersion, mean, and median values of the data are marked in the upper-left corner of each figure, and the final number of features we adopt to train each model is marked at the top of each panel.
Paper: Mass and Age Determination of the LAMOST Data with Different Machine-learning Methods
Authors: Qi-Da Li, Hai-Feng Wang, Yang-Ping Luo, Qing Li, Li-Cai Deng, Yuan-Sen Ting
Figure caption: The results of feature extraction using random forest. The different panels are three different training samples that we have selected, the top one is for the large sample containing a different type of stars, for which we only estimate mass, and the middle and bottom ones are for RC stars, for which we could estimate mass and age. The importance represents the contribution of the stellar parameter to our prediction model, and it is actually the relative importance.
Paper: Mass and Age Determination of the LAMOST Data with Different Machine-learning Methods
Authors: Qi-Da Li, Hai-Feng Wang, Yang-Ping Luo, Qing Li, Li-Cai Deng, Yuan-Sen Ting
Figure caption: The distribution of RC-age on the R.A. vs. decl. plane is shown in the left panel colored by age, the number and fraction for decl. beyond 20° or 30° are denoted at the top of the panel. The middle panel is for density distribution in the longitude and latitude planes and star counts; fractions beyond 20° or 30° for latitude are labeled at the top of this panel. The right panel is the R and Z planes in cylindrical Galactic coordinates colored by density/star counts; fractions larger than 10 or 15  kpc for radial distance are also denoted at the top.
Paper: Mass and Age Determination of the LAMOST Data with Different Machine-learning Methods
Authors: Qi-Da Li, Hai-Feng Wang, Yang-Ping Luo, Qing Li, Li-Cai Deng, Yuan-Sen Ting
Figure caption: The relations between the number of training features and the mean relative error of the test data set in our prediction model. Different panels represent different samples. The blue line represents the training data set, the orange line represents the test data set, and the green dotted lines guide the eyes to the minimum value used for the stable pattern. The minimum values are also labeled at the top of each panel. Notice that it is the minimum value, but not the final feature, that we adopt; we choose final features empirically and accordingly.
Paper: Mass and Age Determination of the LAMOST Data with Different Machine-learning Methods
Authors: Qi-Da Li, Hai-Feng Wang, Yang-Ping Luo, Qing Li, Li-Cai Deng, Yuan-Sen Ting
Figure caption: A mass distribution of the stars we use to create a convex hull and train prediction models on the T eff and log g plane. Different colors represent different masses.
Paper: Mass and Age Determination of the LAMOST Data with Different Machine-learning Methods
Authors: Qi-Da Li, Hai-Feng Wang, Yang-Ping Luo, Qing Li, Li-Cai Deng, Yuan-Sen Ting
Figure caption: The distribution of predicted mass (age) in celestial sphere coordinates. The top panel is the mass distribution of the large sample, the middle panel is the mass distribution of RC stars, and the bottom one is the age distribution of RC stars.
Paper: Mass and Age Determination of the LAMOST Data with Different Machine-learning Methods
Authors: Qi-Da Li, Hai-Feng Wang, Yang-Ping Luo, Qing Li, Li-Cai Deng, Yuan-Sen Ting
Figure caption: The final training sample distribution for mass and age on the T eff–log g plane.
Paper: Mass and Age Determination of the LAMOST Data with Different Machine-learning Methods
Authors: Qi-Da Li, Hai-Feng Wang, Yang-Ping Luo, Qing Li, Li-Cai Deng, Yuan-Sen Ting
Figure caption: Distribution of the LAMOST data for mass estimation we use (left) and the RC distribution we use for age and mass (right).
Paper: Osaka Feedback Model. II. Modeling Supernova Feedback Based on High-resolution Simulations
Authors: Yuri Oku, Kengo Tomida, Kentaro Nagamine, Ikkoh Shimizu, Renyue Cen
Figure caption: Cooling curves used in this work at different metallicities from Sutherland & Dopita (1993) combined with those of Koyama & Inutsuka (2002). The dashed colored lines show approximations given in Equation (3) for the same metallicities given in the legend. The horizontal line indicates the constant photoelectric heating function Γ = 1 × 10−26(n  H /1 cm−3) erg s−1 at T < 104 K.
Paper: Osaka Feedback Model. II. Modeling Supernova Feedback Based on High-resolution Simulations
Authors: Yuri Oku, Kengo Tomida, Kentaro Nagamine, Ikkoh Shimizu, Renyue Cen
Figure caption: Phase diagram of gas in computational box, weighted for its cooling time (left panel) and mass (right panel), at the same time as in Figure 2. In the left panel, one can see that the cooling time is short in the shell of T ∼ 104 K and n H ∼ 10 cm−3.
Paper: Osaka Feedback Model. II. Modeling Supernova Feedback Based on High-resolution Simulations
Authors: Yuri Oku, Kengo Tomida, Kentaro Nagamine, Ikkoh Shimizu, Renyue Cen
Figure caption: Evolution of superbubble as a function of radius, weighted for gas number density (left panel) and gas temperature (right panel), for the case of n H = 1 cm−3, Z = Z ⊙, Δt SN = 0.1 Myr. One can see the power-law expansion of the hot bubble. The horizontal features indicate the intermittent SN injection.
Paper: Osaka Feedback Model. II. Modeling Supernova Feedback Based on High-resolution Simulations
Authors: Yuri Oku, Kengo Tomida, Kentaro Nagamine, Ikkoh Shimizu, Renyue Cen
Figure caption: Slice plot of gas number density and temperature distribution at t = 0.53 Myr for the case of n H = 1 cm−3, Z = Z ⊙, Δt SN = 0.1 Myr. The inner red circle shows R hot, while the outer cyan circle shows R bub.
Paper: Characteristics of Kepler Eclipsing Binaries Displaying a Significant O’Connell Effect
Authors: Matthew F. Knote, Saida M. Caballero-Nieves, Vayujeet Gokhale, Kyle B. Johnston, Eric S. Perlman
Figure caption: Histogram comparing the periods for all 212 core sample targets (solid red) and all 2915 entries in the KEBC (dashed gray) with a period below 1000 days. Note the logarithmic scale of the x-axis. Our core sample’s orbital periods range from 0.234 (KIC 6050116) to 9.752 days (KIC 6197038). The distributions are similar under 0.5 days, but our sample’s long-period population decreases much more quickly than the KEBC’s.
Paper: Characteristics of Kepler Eclipsing Binaries Displaying a Significant O’Connell Effect
Authors: Matthew F. Knote, Saida M. Caballero-Nieves, Vayujeet Gokhale, Kyle B. Johnston, Eric S. Perlman
Figure caption: Eclipse timing variation of KIC 6791604 (left) and KIC 11924311 (right). KIC 6791604 was flagged with an interesting ETV while KIC 11924311 was flagged as a sinusoidal ETV, but both are consistent with a roughly parabolic ETV as well.
Paper: Characteristics of Kepler Eclipsing Binaries Displaying a Significant O’Connell Effect
Authors: Matthew F. Knote, Saida M. Caballero-Nieves, Vayujeet Gokhale, Kyle B. Johnston, Eric S. Perlman
Figure caption: Plot comparing the OER to the OES. Core sample targets are shown in red diamonds and noncore sample systems in black circles. KICs 9935311 and 11347875 are excluded for clarity.
Paper: Characteristics of Kepler Eclipsing Binaries Displaying a Significant O’Connell Effect
Authors: Matthew F. Knote, Saida M. Caballero-Nieves, Vayujeet Gokhale, Kyle B. Johnston, Eric S. Perlman
Figure caption: Eclipse timing variation of KIC 7696778 (left) and KIC 8904448 (right). KIC 7696778's ETV has a parabolic shape, consistent with a linear change in period caused by mass transfer between components, while KIC 8904448's ETV has a sinusoidal shape, consistent with LTTEs caused by a third body orbiting the binary.
Paper: Characteristics of Kepler Eclipsing Binaries Displaying a Significant O’Connell Effect
Authors: Matthew F. Knote, Saida M. Caballero-Nieves, Vayujeet Gokhale, Kyle B. Johnston, Eric S. Perlman
Figure caption: Plot comparing the LCA to the OES. Core sample targets are shown in red diamonds and noncore sample systems in black circles. KICs 9935311 and 11347875 are excluded for clarity.
Paper: Characteristics of Kepler Eclipsing Binaries Displaying a Significant O’Connell Effect
Authors: Matthew F. Knote, Saida M. Caballero-Nieves, Vayujeet Gokhale, Kyle B. Johnston, Eric S. Perlman
Figure caption: Averaged light curves of KIC 9164694 (left) and KIC 9717924 (right) showing an asymmetric primary and secondary minimum, respectively.
Paper: Characteristics of Kepler Eclipsing Binaries Displaying a Significant O’Connell Effect
Authors: Matthew F. Knote, Saida M. Caballero-Nieves, Vayujeet Gokhale, Kyle B. Johnston, Eric S. Perlman
Figure caption: 10 day slices of phased Kepler data for KIC 7433513 separated by several months, showing the significant changes in the light curve over time. The number in each time slice’s subplot is the BJD for the midpoint of that time slice.
Paper: Characteristics of Kepler Eclipsing Binaries Displaying a Significant O’Connell Effect
Authors: Matthew F. Knote, Saida M. Caballero-Nieves, Vayujeet Gokhale, Kyle B. Johnston, Eric S. Perlman
Figure caption: Color-magnitude diagram showing the 212 systems in the core sample overplotting the rest of the KEBC systems. The Gaia color is plotted against the absolute magnitude in Gaia's G band. The systems are color coded according to their light-curve class. Most of our sample are solar-type main-sequence stars.
Paper: Characteristics of Kepler Eclipsing Binaries Displaying a Significant O’Connell Effect
Authors: Matthew F. Knote, Saida M. Caballero-Nieves, Vayujeet Gokhale, Kyle B. Johnston, Eric S. Perlman
Figure caption: Kepler light curve of KIC 7433513 showing significant scatter caused by temporal variation.
Paper: An Atlas of Convection in Main-sequence Stars
Authors: Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello
Figure caption: The Rossby number Ro (left) and turnover time t conv (right) are shown in terms of ﹩\mathrm{log}{T}_{\mathrm{eff}}﹩/spectral type and ﹩\mathrm{log}L﹩ for stellar models with He II CZs and Milky Way metallicity Z = 0.014. Note that both Ro and t conv are outputs of a theory of convection. Regions with Ra < Racrit are stable to convection and shaded in gray. Note that the turnover time exhibits numerical noise related to the model mesh resolution because the integrand 1/v  c  diverges toward the convective boundaries.
Paper: An Atlas of Convection in Main-sequence Stars
Authors: Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello
Figure caption: The density ratio D (left) and Mach number Ma (right) are shown in terms of ﹩\mathrm{log}{T}_{\mathrm{eff}}﹩/spectral type and ﹩\mathrm{log}L﹩ for stellar models with FeCZs and Milky Way metallicity Z = 0.014. Note that while the density ratio is an input parameter and does not depend on a specific theory of convection, the Mach number is an output of such a theory. Regions with Ra < Racrit are stable to convection and shaded in gray.
Paper: An Atlas of Convection in Main-sequence Stars
Authors: Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello
Figure caption: The Eddington ratios with the full luminosity ΓEdd (left) and the radiative luminosity (right) are shown in terms of ﹩\mathrm{log}{T}_{\mathrm{eff}}﹩/spectral type and ﹩\mathrm{log}L﹩ for stellar models with He II CZs and Milky Way metallicity Z = 0.014. Note that while ΓEdd is an input parameter and does not depend on a specific theory of convection, ﹩{{\rm{\Gamma }}}_{\mathrm{Edd}}^{\mathrm{rad}}﹩ is an output of such a theory. Regions with Ra < Racrit are stable to convection and shaded in gray.
Paper: An Atlas of Convection in Main-sequence Stars
Authors: Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello
Figure caption: The Péclet number Pe (left) and F conv/F (right) are shown in terms of ﹩\mathrm{log}{T}_{\mathrm{eff}}﹩/spectral type and ﹩\mathrm{log}L﹩ for stellar models with He II CZs and Milky Way metallicity Z = 0.014. Note that both Pe and F conv/F are outputs of a theory of convection. Regions with Ra < Racrit are stable to convection and shaded in gray.
Paper: An Atlas of Convection in Main-sequence Stars
Authors: Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello
Figure caption: The Prandtl number Pr (left) and magnetic Prandtl number Pm (right) are shown in terms of ﹩\mathrm{log}{T}_{\mathrm{eff}}﹩/spectral type and ﹩\mathrm{log}L﹩ for stellar models with He II CZs and Milky Way metallicity Z = 0.014. Note that both Pr and Pm are input parameters and so do not depend on a specific theory of convection. Regions with Ra < Racrit are stable to convection and shaded in gray.
Paper: An Atlas of Convection in Main-sequence Stars
Authors: Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello
Figure caption: The stiffnesses of the inner (left) and outer (right) convective boundaries are shown in terms of ﹩\mathrm{log}{T}_{\mathrm{eff}}﹩ and ﹩\mathrm{log}L﹩ for stellar models with He II CZs and Milky Way metallicity Z = 0.014. Note that the stiffness is an output of a theory of convection. Regions with Ra < Racrit are stable to convection and shaded in gray.
Paper: An Atlas of Convection in Main-sequence Stars
Authors: Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello
Figure caption: The Ekman number Ek is shown in terms of ﹩\mathrm{log}{T}_{\mathrm{eff}}﹩/spectral type and ﹩\mathrm{log}L﹩ for stellar models with He II CZs and Milky Way metallicity Z = 0.014. Note that the Ekman number is an input parameter and does not depend on a specific theory of convection. Regions with Ra < Racrit are stable to convection and shaded in gray.
Paper: An Atlas of Convection in Main-sequence Stars
Authors: Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello
Figure caption: The radiation pressure ratio β rad is shown in terms of ﹩\mathrm{log}{T}_{\mathrm{eff}}﹩/spectral type and ﹩\mathrm{log}L﹩ for stellar models with He II CZs and Milky Way metallicity Z = 0.014. Note that this ratio is an input parameter and does not depend on a specific theory of convection. Regions with Ra < Racrit are stable to convection and shaded in gray.
Paper: An Atlas of Convection in Main-sequence Stars
Authors: Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello
Figure caption: The aspect ratio A is shown in terms of ﹩\mathrm{log}{T}_{\mathrm{eff}}﹩/spectral type and ﹩\mathrm{log}L﹩ for stellar models with FeCZs and Milky Way metallicity Z = 0.014. Note that the aspect ratio is an input parameter and does not depend on a specific theory of convection. Regions with Ra < Racrit are stable to convection and shaded in gray.