adsabs/AstroCLIMB huggingface dataset:
Paper: Inferring Cosmological Parameters on SDSS via Domain-generalized Neural Networks and Light-cone Simulations
Authors: Jun-Young Lee, Ji-hoon Kim, Minyong Jung, Boon Kiat Oh, Yongseok Jo, Songyoun Park, Jaehyun Lee, Yuan-Sen Ting, Ho Seong Hwang
Figure caption: A visualization of the latent space configuration from a typical neural network trained with SHAM L-PICOLA and GADGET mocks with the vanilla scheme (upper panels) and the semantic alignment strategy (lower panels). The 16-dimensional vectors are reduced to two dimensions using the t-SNE algorithm (L. van der Maaten & G. Hinton 2008). L-PICOLA (circle) and GADGET (triangle) samples are colored according to their cosmological parameters: Ωm (left) and σ 8 (right), alongside with SDSS BOSS LOWZ NGC (red star). In the lower panels, where the semantic alignment strategy is applied, two distinct axes are evident. Along one axis, the parameters gradually change in one direction, while remaining almost independent along the other. This pattern indicates that the two cosmological parameters are effectively represented. Moreover, the GADGET samples are effectively integrated and generalized in these two panels, in stark contrast to the upper panels of the vanilla scheme, which show apparent distinction in the distribution. See Section 6.1 for more information.
Paper: Inferring Cosmological Parameters on SDSS via Domain-generalized Neural Networks and Light-cone Simulations
Authors: Jun-Young Lee, Ji-hoon Kim, Minyong Jung, Boon Kiat Oh, Yongseok Jo, Songyoun Park, Jaehyun Lee, Yuan-Sen Ting, Ho Seong Hwang
Figure caption: Prediction on the 2048 MD-PATCHY mocks from 25 independently trained Minkowski-PointNet machines. The left figure displays our results when using the SHAM model, and the right figure displays the results when using the fixed-SHMR model. The machines are trained with L-PICOLA and GADGET mocks with the semantic alignment strategy, a domain adaptation and generalization technique that enables the machines to extract consistent features regardless of their simulation domains (see Section 4.3). Predictions are shown with error bars. A red star shows the result from the Planck 2018 (Planck Collaboration et al. 2020) measurements, a blue star from M. M. Ivanov et al. (2020), and a green star the ground truth values of MD-PATCHY mocks. Individual error bars include the statistical error attributed to the cosmic variance of the 2048 MD-PATCHY mocks, which are calculated from the Gaussian mixture model (GMM). Individual errors are once again combined by the GMM for the elliptic contours, showing the 1σ, 2σ, and 3σ bounds. Our results yield Ωm = 0.327 ± 0.070, σ 8 = 0.822 ± 0.071 (left, SHAM), and Ωm = 0.236 ± 0.046, σ 8 = 0.784 ± 0.038 (right, fixed SHMR). See Section 6.3 for more information.
Paper: Inferring Cosmological Parameters on SDSS via Domain-generalized Neural Networks and Light-cone Simulations
Authors: Jun-Young Lee, Ji-hoon Kim, Minyong Jung, Boon Kiat Oh, Yongseok Jo, Songyoun Park, Jaehyun Lee, Yuan-Sen Ting, Ho Seong Hwang
Figure caption: Architecture of the Minkowski-PointNet in this work. Each input is transformed into a sparse tensor with N galaxies, each with features F, and passed through five consecutive linear layers for feature extraction. Global sum, average, and max pooling are done to extract a 1536-dimensional feature vector. Then, it passes through the regressor consisting of four linear and dropout layers to predict Ωm, σ 8, and their standard deviations. See Section 3.1 for more information.
Paper: Inferring Cosmological Parameters on SDSS via Domain-generalized Neural Networks and Light-cone Simulations
Authors: Jun-Young Lee, Ji-hoon Kim, Minyong Jung, Boon Kiat Oh, Yongseok Jo, Songyoun Park, Jaehyun Lee, Yuan-Sen Ting, Ho Seong Hwang
Figure caption: Comparison of epistemic and aleatoric error in logarithmic scale, from the ensemble prediction of 25 machines, trained with the vanilla scheme (upper panels) and the semantic alignment strategy (lower panels). The machines are trained in the two mock suites L-PICOLA    + GADGET with the SHAM model. The left two columns are the results for Ωm and the right two are the results for σ 8. Blue contours represent the test set samples of L-PICOLA, red contours the MD-PATCHY mocks, dark purple circles the test set samples of GADGET, and red stars the SDSS BOSS LOWZ NGC catalog. Black dotted lines depict the complete match between the two types of errors. The epistemic errors are calculated by the standard deviation of the predictions on a single input data from the ensemble of 25 machines. The aleatoric errors are calculated by the rms of the predicted errors from individual machines. See Section 6.1 for more information.
Paper: Inferring Cosmological Parameters on SDSS via Domain-generalized Neural Networks and Light-cone Simulations
Authors: Jun-Young Lee, Ji-hoon Kim, Minyong Jung, Boon Kiat Oh, Yongseok Jo, Songyoun Park, Jaehyun Lee, Yuan-Sen Ting, Ho Seong Hwang
Figure caption: Pair plot of five features of a galaxy randomly sampled from each mock generated with the fixed-SHMR model: stellar mass of itself, maximum and mean neighbor masses, and maximum and mean neighbor distances, for a single realization of SDSS BOSS LOWZ NGC (red), MD-PATCHY (blue), L-PICOLA fiducial (orange), and GADGE T fiducial (green). The plot shows 1000 randomly sampled galaxies for each mock. Masses are in units of ﹩\mathrm{log}({M}_{\star }/{h}^{-1}{M}_{\odot })﹩ and distances are expressed in terms of the newly assumed metric in redshift space. The distribution exhibits fair consistency across the three mocks and the SDSS BOSS LOWZ NGC catalog. See Sections 3.1 and 3.2 for more information.
Paper: Inferring Cosmological Parameters on SDSS via Domain-generalized Neural Networks and Light-cone Simulations
Authors: Jun-Young Lee, Ji-hoon Kim, Minyong Jung, Boon Kiat Oh, Yongseok Jo, Songyoun Park, Jaehyun Lee, Yuan-Sen Ting, Ho Seong Hwang
Figure caption: Prediction on the actual SDSS BOSS LOWZ NGC catalog from the ensemble of 25 independently trained Minkowski-PointNet machines. The left figure displays our results when using the SHAM model, and the right figure displays the results when using the fixed-SHMR model. The machines are trained with L-PICOLA and MD-PATCHY mocks without domain adaptation strategy (vanilla), a domain adaptation and generalization technique that enables the machines to extract consistent features regardless of their simulation domains (see Section 4.3). Predictions are shown with error bars. A red star shows the result from the Planck 2018 (Planck Collaboration et al. 2020) measurements and a blue star from M. M. Ivanov et al. (2020). Elliptic contours show the 1σ, 2σ, and 3σ bounds, calculated from the GMM to incorporate the individual errors. Our results yield Ωm = 0.343 ± 0.053, σ 8 = 0.796 ± 0.051 (left, SHAM), and Ωm = 0.307 ± 0.035, σ 8 = 0.767 ± 0.035 (left, fixed SHMR). See Section 6.1 for more information.
Paper: Inferring Cosmological Parameters on SDSS via Domain-generalized Neural Networks and Light-cone Simulations
Authors: Jun-Young Lee, Ji-hoon Kim, Minyong Jung, Boon Kiat Oh, Yongseok Jo, Songyoun Park, Jaehyun Lee, Yuan-Sen Ting, Ho Seong Hwang
Figure caption: Pair plot of five features of a galaxy randomly sampled from each mock generated with the fixed-SHMR model, similar to Figure 6, but this time for three realizations of different cosmologies named high (red; Ωm = 0.4772, σ 8 = 0.9639), low (green; Ωm = 0.1185, σ 8 = 0.6163), and fiducial (blue; Ωm = 0.3067, σ 8 = 0.8238). The plot shows 1000 randomly sampled galaxies for each mock. Masses are in units of ﹩\mathrm{log}({M}_{\star }/{h}^{-1}{M}_{\odot })﹩ and distances are expressed in terms of the newly assumed metric in redshift space ﹩(X,\,Y,\,Z)=(z\sin ({\rm{decl}}.)\cos ({\rm{R}}.{\rm{A}}.),\,z\sin ({\rm{decl}}.)\sin ({\rm{R}}.{\rm{A}}.),\,z\cos ({\rm{decl}}.))﹩. See Appendix A for more information.
Paper: Morphology of Molecular Clouds at Kiloparsec Scale in the Milky Way: Shear-induced Alignment and Vertical Confinement
Authors: Yi-Heng Xie, Guang-Xing Li, Bing-Qiu Chen
Figure caption: The mass–size relations and histograms of clouds. (1) Upper panel: mass–size relations from our work in three different cloud size definitions: semimajor axis-based definition (blue scatters), area-based definition (green scatters) and volume-based definition (red scatters), and three previous works (M.-A. Miville-Deschênes et al. 2017, gray scatters; J. Roman-Duval et al. 2010, rosy-brown scatters; B.-Q. Chen et al. 2020, tan scatters). The fitting of our results are ﹩M{\propto 32.00\,{R}_{\max }}^{1.77}﹩ (blue dashed line), ﹩M\propto 20.59\,{{R}_{S}}^{2.04}﹩ (green dashed line), and ﹩M\propto 14.41\,{{R}_{V}}^{2.29}﹩ (red dashed line), respectively. (2) Lower panel: histograms of three different definition-based cloud sizes (left panel) and cloud masses (right panel) from our work.
Paper: Morphology of Molecular Clouds at Kiloparsec Scale in the Milky Way: Shear-induced Alignment and Vertical Confinement
Authors: Yi-Heng Xie, Guang-Xing Li, Bing-Qiu Chen
Figure caption: Shapes and aspect ratios of clouds. (1) Upper left panel: clouds in shape space. The sample clouds are distinguished into ellipsoid-like clouds (number = 121, blue region), disk-like clouds (number = 210, yellow region), and bar-like clouds (number = 219, green region), respectively. The color of the scatters corresponds to the angle between cloud orientations and the Galactic radial direction. The red solid line shows where ﹩{R}_{\mathrm{mid}}={R}_{\min }﹩. The background gray colormap shows the result of kernel density estimation. (2) Upper right panel: ﹩{R}_{\mathrm{mid}}-{R}_{\max }﹩ and ﹩{R}_{\mathrm{mid}}-{R}_{\max }﹩ relation of sample clouds. The fitting results are ﹩{{R}_{\mathrm{mid}}=1.51{R}_{\max }}^{0.74}﹩ (blue dashed line) and ﹩{{R}_{\min }=1.94{R}_{\max }}^{0.57}﹩ (green dashed line), respectively. (3) Lower panel: distribution of clouds in different shapes as a function of cloud size. The red region represents the scale height of the molecular gas disk.
Paper: Inferring Cosmological Parameters on SDSS via Domain-generalized Neural Networks and Light-cone Simulations
Authors: Jun-Young Lee, Ji-hoon Kim, Minyong Jung, Boon Kiat Oh, Yongseok Jo, Songyoun Park, Jaehyun Lee, Yuan-Sen Ting, Ho Seong Hwang
Figure caption: Prediction on the actual SDSS BOSS LOWZ NGC catalog from 25 independently trained Minkowski-PointNet machines, similar to Figure 8, but this time with DANN (Y. Ganin et al. 2016) instead of the semantic alignment strategy. Predictions with error bars are shown and in different colors, indicating the probability that the domain classifier classifies as L-PICOLA, ﹩P({{ \mathcal D }}_{{\rm{L}}-\mathrm{PICOLA}}| {{\boldsymbol{x}}}_{\mathrm{SDSS}})﹩. A red star shows the result from the Planck 2018 (Planck Collaboration et al. 2020) measurements and a blue star from M. M. Ivanov et al. (2020). Elliptic contours show the bounds of 1σ, 2σ, and 3σ bounds. The results yield Ωm = 0.304 ± 0.033 and σ 8 = 0.795 ± 0.057. See Appendix C for more information.
Paper: Morphology of Molecular Clouds at Kiloparsec Scale in the Milky Way: Shear-induced Alignment and Vertical Confinement
Authors: Yi-Heng Xie, Guang-Xing Li, Bing-Qiu Chen
Figure caption: Extension of clouds along different directions. (1) Left panel: extension of clouds along Galactic radial direction vs. cloud size. The green solid line shows the fitting result ﹩{{\rm{\Delta }}r=0.94{R}_{\max }}^{0.94}﹩. The red solid line represents where ﹩{\rm{\Delta }}r={R}_{\max }﹩. (2) Right panel: extension of clouds along Galactic radial direction vs. cloud size. The green solid line shows the fitting result ﹩{{\rm{\Delta }}z=1.74{R}_{\max }}^{0.69}\,({R}_{\max }\lt 100\mathrm{pc})﹩ and ﹩{{\rm{\Delta }}z=8.31{R}_{\max }}^{0.35}\,({R}_{\max }\gt 100\mathrm{pc})﹩. The red solid line represents where ﹩{\rm{\Delta }}z={R}_{\max }﹩. The red region represents the scale height of the molecular gas disk. The background gray colormap shows the result of kernel density estimation.
Paper: Morphology of Molecular Clouds at Kiloparsec Scale in the Milky Way: Shear-induced Alignment and Vertical Confinement
Authors: Yi-Heng Xie, Guang-Xing Li, Bing-Qiu Chen
Figure caption: Mass surface density and mass volume density of clouds. (1) Left panel: surface density (Σ) of sample clouds vs. cloud size. The fitting result is ﹩{{\rm{\Sigma }}\sim 6.75{R}_{\max }}^{0.03}﹩ (green dashed line). The standard deviation of the cloud surface density is σ Σ = 4.66. (2) Volume density (ρ) of sample clouds vs. cloud size. The fitting result is ﹩{\rho \sim 2.60{R}_{\max }}^{-0.55}﹩ (green dashed line). The background gray colormap shows the result of kernel density estimation.
Paper: Morphology of Molecular Clouds at Kiloparsec Scale in the Milky Way: Shear-induced Alignment and Vertical Confinement
Authors: Yi-Heng Xie, Guang-Xing Li, Bing-Qiu Chen
Figure caption: Orientations of clouds in the Milky Way. (1) Left panel: pitch angle of clouds vs. cloud size. (2) Right panel: angles between cloud orientations with the mid-plane of Galactic disk vs. cloud size. The red region represents the scale height of the molecular gas disk. The background gray colormap shows the result of kernel density estimation.
Paper: Morphology of Molecular Clouds at Kiloparsec Scale in the Milky Way: Shear-induced Alignment and Vertical Confinement
Authors: Yi-Heng Xie, Guang-Xing Li, Bing-Qiu Chen
Figure caption: Contours of clouds in different shapes with the dust extinction map projected onto the x–y plane. The clouds are in the shapes of ellipsoid-like (blue contour), disk-like (orange contour), and bar-like (green contour), respectively.
Paper: Full Abundance Study of Two Newly Discovered Barium Giants
Authors: Sara Vitali, Ana Escorza, Ditte Slumstrup, Paula Jofré
Figure caption: Synthesis of the Ba spectral line at 585.366 nm compared with the observed line (black dots) for both targets. The red line represents a synthetic model created with our final parameters and [Ba/Fe] abundance (see Table 1). The blue and pink lines show synthetic spectra with [Ba/Fe] varied by ±0.3 dex, while the gray spectrum is synthesised with the Ba solar abundance value. This comparison highlights the strength of the Ba line. Even though its enhancement makes the variation less obvious, the sensitivity of the line profile is visible, especially in the wings. This behavior demonstrates the complexity of this measurement.
Paper: Full Abundance Study of Two Newly Discovered Barium Giants
Authors: Sara Vitali, Ana Escorza, Ditte Slumstrup, Paula Jofré
Figure caption: Effective temperature–surface gravity diagram (Kiel diagram) including our target stars J04034842+1551272 and J16564223-2108420. We overplot them with the sample of effectively single red giants studied by V24 (circles), and the sample of Ba giants from D. B. de Castro et al. (2016)and M. P. Roriz et al. (2021a, 2021b) (triangles). The three samples are color coded as a function of each target’s metallicity. We also overplotted evolutionary tracks computed with the STAREVOL code (L. Siess et al. 2000; L. Siess 2008) for [Fe/H] = −0.5 and initial masses equal to 1.0 and 1.5 M ⊙.
Paper: Full Abundance Study of Two Newly Discovered Barium Giants
Authors: Sara Vitali, Ana Escorza, Ditte Slumstrup, Paula Jofré
Figure caption: [X/Fe] as a function of metallicity for our targets (red stars). For comparison, we also include the sample of effectively single red giants from V24 (orange circles) and the Ba giants from D. B. de Castro et al. (2016), with Eu values added from M. P. Roriz et al. (2021b) (light blue triangles).
Paper: Full Abundance Study of Two Newly Discovered Barium Giants
Authors: Sara Vitali, Ana Escorza, Ditte Slumstrup, Paula Jofré
Figure caption: Similar plot to Figure 2 but for the Eu II line at 664.51 nm. Synthetic and observed data follow the same colors as for the Ba II line. This time the variation among the three Eu-enhanced spectra corresponds to 0.1 dex. Again, the fourth spectrum is synthesised with the Eu solar value. The red line refers again to the [Eu/Fe] result presented in Table 1. The sensitivity of the line to abundance changes highlights the accuracy of our spectral fit, even with the blend impacting this Eu II line.
Paper: Full Abundance Study of Two Newly Discovered Barium Giants
Authors: Sara Vitali, Ana Escorza, Ditte Slumstrup, Paula Jofré
Figure caption: [La/Fe] as a function of [Eu/Fe] for our target stars to confirm their pure s-process pollution. For comparison, the light blue triangles represent the sample of well-known Ba giants from D. B. de Castro et al. (2016) and M. P. Roriz et al. (2021b) and the purple squares show a sample of r-process-rich CEMP stars studied by T. Masseron et al. (2010).
Paper: Full Abundance Study of Two Newly Discovered Barium Giants
Authors: Sara Vitali, Ana Escorza, Ditte Slumstrup, Paula Jofré
Figure caption: RV data for J04034842+1551272 (top) and J16564223-2108420 (bottom) published by the GALAH (G. M. De Silva et al. 2015) survey (blue square) and the APOGEE (S. R. Majewski et al. 2017) survey (olive circles) or measured for this work (red star).
Paper: Full Abundance Study of Two Newly Discovered Barium Giants
Authors: Sara Vitali, Ana Escorza, Ditte Slumstrup, Paula Jofré
Figure caption: Spectral energy distribution for J04034842+1551272 built with VOSA (A. Bayo et al. 2008). The photometry includes GALEX NUV (L. Bianchi et al. 2017), Johnson B, V, R, and I (E. Høg et al. 2000; A. A. Henden et al. 2015), SLOAN/SDSS g, r, i, and z (S. Alam et al. 2015), Gaia G, G BP, and G RP (Gaia Collaboration et al. 2023a), 2MASS J, H, and K  S  (M. F. Skrutskie et al. 2006), and WISE W1, W2, W3, and W4. The model comes from the New Grids of ATLAS9 Model Atmospheres from F. Castelli & R. L. Kurucz (2003).
Paper: The Sonora Substellar Atmosphere Models. III. Diamondback: Atmospheric Properties, Spectra, and Evolution for Warm Cloudy Substellar Objects
Authors: Caroline V. Morley, Sagnick Mukherjee, Mark S. Marley, Jonathan J. Fortney, Channon Visscher, Roxana Lupu, Ehsan Gharib-Nezhad, Daniel Thorngren, Richard Freedman, Natasha Batalha
Figure caption:  P–T profiles of a selection of cloudy and cloud-free models from T eff = 900 to 2400 K, with log g = 4.5. Cloud-free models are shown as blue lines for comparison. Cloudy models with thinner clouds (f sed = 8) and thicker clouds (f sed = 4) are shown with purple and red lines, respectively. The thicker regions indicate convective parts of the atmosphere, while the thinner regions are radiative. Condensation curves for the four clouds included in this model grid are shown with dashed lines. Note that cloudy models are always warmer than a cloud-free model with the same effective temperature.
Paper: The Sonora Substellar Atmosphere Models. III. Diamondback: Atmospheric Properties, Spectra, and Evolution for Warm Cloudy Substellar Objects
Authors: Caroline V. Morley, Sagnick Mukherjee, Mark S. Marley, Jonathan J. Fortney, Channon Visscher, Roxana Lupu, Ehsan Gharib-Nezhad, Daniel Thorngren, Richard Freedman, Natasha Batalha
Figure caption: Cumulative optical depth as a function of each parameter in the Diamondback grid. Cumulative optical depth is calculated by summing τ at a wavelength of 1.1 μm from the top of the atmosphere. τ = 1 is shown as a dashed vertical line to guide the eye. The values of the parameters being held constant are listed in the lower left corner of each panel (T eff = 1500 K, [M/H] = 0.0, log g = 4.5, f sed = 4). The top left panel shows that increasing effective temperature raises the cloud base height to lower pressures and decreases the overall optical depth of the cloud. The top right panel shows that decreasing gravity typically raises the cloud base to lower pressures. The bottom left panel shows that decreasing f sed changes the vertical extent of the cloud. The bottom right panel shows that increasing metallicity creates a more optically thick cloud, raising the cloud base to lower pressures.
Paper: The Sonora Substellar Atmosphere Models. III. Diamondback: Atmospheric Properties, Spectra, and Evolution for Warm Cloudy Substellar Objects
Authors: Caroline V. Morley, Sagnick Mukherjee, Mark S. Marley, Jonathan J. Fortney, Channon Visscher, Roxana Lupu, Ehsan Gharib-Nezhad, Daniel Thorngren, Richard Freedman, Natasha Batalha
Figure caption: Sonora Diamondback models compared to the prior-generation models from D. Saumon & M. S. Marley (2008). All models have log g = 5.0 and f sed = 2. The Diamondback models have slightly thicker clouds for a given f sed, due mostly to the addition of the MgSiO3 cloud, which was not included in D. Saumon & M. S. Marley (2008). The area between the models is shaded blue where the Diamondback model is brighter and pink where the D. Saumon & M. S. Marley (2008) model is brighter. Changes can be seen at wavelengths affected by hydride opacities (e.g., FeH at 1.6 μm), oxide, and alkali opacities. Models are smoothed to a constant wavelength spacing (λ/Δλ = 1000 at 2 μm).
Paper: The Sonora Substellar Atmosphere Models. III. Diamondback: Atmospheric Properties, Spectra, and Evolution for Warm Cloudy Substellar Objects
Authors: Caroline V. Morley, Sagnick Mukherjee, Mark S. Marley, Jonathan J. Fortney, Channon Visscher, Roxana Lupu, Ehsan Gharib-Nezhad, Daniel Thorngren, Richard Freedman, Natasha Batalha
Figure caption: Radiative–convective boundaries for a selection of models. All models have log g = 4.5. Filled circles show the deep radiative–convective boundary for each model; larger circles indicate cloudier models, as shown in the legend. The shaded rectangles show lower-pressure detached convective zones. These are never present in the hottest models or cloud-free models, but they start to appear at T eff < 1900 K owing to the additional opacity of clouds.
Paper: The Sonora Substellar Atmosphere Models. III. Diamondback: Atmospheric Properties, Spectra, and Evolution for Warm Cloudy Substellar Objects
Authors: Caroline V. Morley, Sagnick Mukherjee, Mark S. Marley, Jonathan J. Fortney, Channon Visscher, Roxana Lupu, Ehsan Gharib-Nezhad, Daniel Thorngren, Richard Freedman, Natasha Batalha
Figure caption: Sonora Diamondback models compared to the prior generation (Sonora Bobcat) models from M. S. Marley et al. (2021). All models have log g = 5.0 and are cloud-free. The small differences seen are attributable to differences in the line lists used, including the alkali lines and oxide/hydride features. Models are smoothed to a constant wavelength spacing (Δλ/λ = 1000 at 2 μm).
Paper: The Sonora Substellar Atmosphere Models. III. Diamondback: Atmospheric Properties, Spectra, and Evolution for Warm Cloudy Substellar Objects
Authors: Caroline V. Morley, Sagnick Mukherjee, Mark S. Marley, Jonathan J. Fortney, Channon Visscher, Roxana Lupu, Ehsan Gharib-Nezhad, Daniel Thorngren, Richard Freedman, Natasha Batalha
Figure caption: Mie scattering absorption efficiencies, Q abs, of the four clouds included in these models: MgSiO3, Mg2SiO4, Fe, and Al2O3. Note the large features for silicates at ∼10 μm and those for Al2O3 at ∼13 μm. For each cloud species, Q abs has larger-amplitude features for smaller particles.
Paper: The Sonora Substellar Atmosphere Models. III. Diamondback: Atmospheric Properties, Spectra, and Evolution for Warm Cloudy Substellar Objects
Authors: Caroline V. Morley, Sagnick Mukherjee, Mark S. Marley, Jonathan J. Fortney, Channon Visscher, Roxana Lupu, Ehsan Gharib-Nezhad, Daniel Thorngren, Richard Freedman, Natasha Batalha
Figure caption: Representative eddy diffusion coefficients, K  zz , which parameterize vertical mixing of the cloud material. Profiles are taken for models with effective temperatures spanning our grid and have log g = 4.5, f sed = 3, and [M/H] = 0.0.