adsabs/AstroCLIMB huggingface dataset:
Paper: Thermal Properties of the Leading Hemisphere of Callisto Inferred from ALMA Observations
Authors: Maria Camarca, Katherine de Kleer, Bryan Butler, Alex B. Akins, Alexander Thelen, Imke de Pater, Mark A. Gurwell, Arielle Moullet
Figure caption: Examples of a linear mixture of two models of different thermal inertias (Γ, J m−2 K−1 s−1/2), one using Γ = 2000 and another using Γ = 15. Positive (red) and negative (blue) residuals correspond to surface regions in the data that are warmer and cooler than predicted by the model, respectively. The figure demonstrates that very low thermal inertia models predict too little limb emission relative to disk center, while very high thermal inertia models provide a poor fit for the opposite reason; a mixture of the two models provides a better fit without systematic center-to-limb trends in the residuals.
Paper: Thermal Properties of the Leading Hemisphere of Callisto Inferred from ALMA Observations
Authors: Maria Camarca, Katherine de Kleer, Bryan Butler, Alex B. Akins, Alexander Thelen, Imke de Pater, Mark A. Gurwell, Arielle Moullet
Figure caption: High-resolution version of the wavelength-integrated albedo map of Callisto based on Voyager and Galileo data. The creation of this map is described in Section 3.2. The data behind Figure 3 are available in Tar: FITS files format.(The data used to create this figure are available.)
Paper: Using Neural Networks to Model Main Belt Asteroid Albedos as a Function of Their Proper Orbital Elements
Authors: Zachary Murray
Figure caption: Here we show the true visible albedos as reported by NEOWISE (top row), the mean predicted visible albedos (second row), the averaged model uncertainty (third row), and the stochasticity of the belt (final row) as a function of the proper orbital elements (note the difference in color scales between each row). We can see that large groups of asteroids, corresponding to different families, tend to have low albedos, where these families make up a significant portion of the inner asteroid belt. In addition, there is a clear, but highly structured gradient in albedo from the inner to outer main belt. The smoothing of the predicted albedos makes some structure in the main belt more obvious, such as a large region of inclined asteroids with low albedos in the central main belt. The third row shows that the model uncertainties due to observational errors tend to be largest in sparsely populated parts of the belt, whereas the final row shows that areas near asteroid families tend to be relatively homogeneous in albedo.
Paper: Using Neural Networks to Model Main Belt Asteroid Albedos as a Function of Their Proper Orbital Elements
Authors: Zachary Murray
Figure caption: In the left panel, we show the residuals for the neural net for all the asteroids in our sample (all bodies), the residuals for those in recognized asteroid families (families), those not known to belong to an asteroid family (background), and the residuals for a prediction based on the average albedo. We contrast them with a naive prediction that simply assigns the average albedo to every asteroid regardless of its dynamical state. We can see that the residuals from the neural net ensemble are significantly smaller than that of the naive prediction. In the right panel, we show the distribution of σ belt for asteroids in and outside families. We recover the relative homogeneity of albedos in the asteroid families compared to those of the background belt. Note that the small displacement of the residuals from zero comes from the errors being given in terms of the albedo itself, rather than its log. The transformation between these causes a small asymmetry in the error which shows up here as a small shift away from zero.
Paper: Using Neural Networks to Model Main Belt Asteroid Albedos as a Function of Their Proper Orbital Elements
Authors: Zachary Murray
Figure caption: We illustrate the increase of the mean absolute error among asteroids with small diameters. The top panel shows the absolute error vs. ﹩{\mathrm{log}}_{10}(D)﹩ for each of the asteroids in our data set (gray points) and a binned average of that error (red step function). The behavior over D < 20 km is modeled by a linear function, shown here as a dashed green line. The horizontal dashed line is the mean absolute error over the entire data set (0.182), whereas the horizontal solid line is the mean absolute error if the predictions were taken to be a simple mean (0.291) of the asteroid albedos (without accounting for their orbital elements). The neural network decreases the mean absolute error by just over 37 %. The bottom panel shows a histogram of the gray points: there are very few asteroids in the sample with diameters of more than 20 km, and we ignore such bodies in our linear fit.
Paper: Using Neural Networks to Model Main Belt Asteroid Albedos as a Function of Their Proper Orbital Elements
Authors: Zachary Murray
Figure caption: We show ﹩{\mathrm{log}}_{10}({p}_{V})﹩ for asteroid families in the left panel, and the background of asteroids not in families in the right panel, shown in the plane of ﹩\sin (i)﹩ and a proper orbital elements. We can see the classified families only comprise a portion of the regions with similar albedos.
Paper: Using Neural Networks to Model Main Belt Asteroid Albedos as a Function of Their Proper Orbital Elements
Authors: Zachary Murray
Figure caption: Here we show both the training (black) and validation (red) rms error vs. epoch for a single representative sample of our ensemble of fits to the visible albedos. The validation loss rapidly decreases at early epochs before starting to decrease more slowly after epoch 500. It reaches a shallow minimum near epoch 1100 before gradually increasing and overfitting at later epochs. The dashed vertical line denotes the epoch of minimum validation loss. The weights from this epoch are used in the ensemble.
Paper: Using Neural Networks to Model Main Belt Asteroid Albedos as a Function of Their Proper Orbital Elements
Authors: Zachary Murray
Figure caption: Here we show the distribution of the logarithm of the visible and infrared albedos from the NEOWISE catalog. The distribution of both distributions are bimodal, with the visible albedos peaking near p  V  = 0.05 and p  V  = 0.3 and the infrared albedos peaking near p IR = 0.1 and p IR = 0.4.
Paper: Using Neural Networks to Model Main Belt Asteroid Albedos as a Function of Their Proper Orbital Elements
Authors: Zachary Murray
Figure caption: We show the observed ﹩{\mathrm{log}}_{10}({p}_{V})﹩ albedos of asteroids near the z2 resonance in the left panel, and the predicted ﹩{\mathrm{log}}_{10}({p}_{V})﹩ albedos of asteroids in the same region in the right panel, shown in the plane of proper a and ﹩2\dot{\omega }+\dot{{\rm{\Omega }}}﹩ proper precessional frequencies. We outline the secular resonance with a horizontal dashed line ﹩2{\dot{\omega }}_{6}+{\dot{{\rm{\Omega }}}}_{6}﹩ and consider all those asteroids in the shaded region as part of the resonance.
Paper: A Radiative-convective Model for Terrestrial Planets with Self-consistent Patchy Clouds 
Authors: James D. Windsor, Tyler D. Robinson, Ravi kumar Kopparapu, Amber V. Young, David E. Trilling, Joe LLama
Figure caption: Top: cloud vertical extent as a function of fractional cloudiness. Cloud optical depth is indicated by the depth of the purple color, with darker purple corresponding to layer optical depths that are large, whereas lighter colors correspond to layer optical depths that are small. Layer optical depths above 20 are not resolved to better enable the depiction of optically thinner cloud layers. Bottom: planetary surface temperature (red; left Y-axis) and planetary Bond albedo (blue; right Y-axis) as a function of the fractional cloudiness. Planetary surface temperature is highest for a low fractional cloudiness of 10% and reaches a minimum for a fractional cloudiness of 100%. Planetary Bond albedo reaches a minimum at the low fractional cloudiness of 10%, and reaches a maximum at a fractional cloudiness of 100%. An Earth-like Bond albedo of 0.33 corresponds to a surface temperature of 273 K and a fractional cloudiness of 20%.
Paper: A Radiative-convective Model for Terrestrial Planets with Self-consistent Patchy Clouds 
Authors: James D. Windsor, Tyler D. Robinson, Ravi kumar Kopparapu, Amber V. Young, David E. Trilling, Joe LLama
Figure caption: Effective insolation required for planetary energy balance (S eff;  contours) as a function of the cloud fraction (horizontal axis) and sedimentation efficiency (vertical axis). A S eff value of unity corresponds to a case where the climate state could be maintained with Earth’s current level of insolation.
Paper: A Radiative-convective Model for Terrestrial Planets with Self-consistent Patchy Clouds 
Authors: James D. Windsor, Tyler D. Robinson, Ravi kumar Kopparapu, Amber V. Young, David E. Trilling, Joe LLama
Figure caption: Cloud and atmospheric thermal profiles of the baseline Earth-like model (black solid line) as compared to a ICRCCM midlatitude Earth sounding (green dashed–dotted line) and the cloud-free tuned CLIMA model (red dashed line). In the troposphere, model moist convection is flagged with a translucent horizontal blue line at each differential layer boundary. The tropopause temperature in this model case is 191 K, and the surface global-average temperature is 292 K. The model tropospheric thermal gradient is shallower than that of the ICRCCM midlatitude sounding.
Paper: A Radiative-convective Model for Terrestrial Planets with Self-consistent Patchy Clouds 
Authors: James D. Windsor, Tyler D. Robinson, Ravi kumar Kopparapu, Amber V. Young, David E. Trilling, Joe LLama
Figure caption: Top: cloud layer number density as a function of model layers. The gray shaded regions correspond to the cloud condensate particle number densities. In each cloud structure, the black dashed lines correspond to where the cloud cumulative optical depths reach unity, integrated downward. Cloud particle number densities follow a similar structure regardless of the number of model layers. Bottom: surface temperature (red; left vertical axis) and planetary Bond albedo (blue; right vertical axis) as a function of the number of model layers. The horizontal axis corresponds to the number of layers the climate model uses. Planetary surface temperature and Bond albedo stabilize after about 500 model layers.
Paper: A Radiative-convective Model for Terrestrial Planets with Self-consistent Patchy Clouds 
Authors: James D. Windsor, Tyler D. Robinson, Ravi kumar Kopparapu, Amber V. Young, David E. Trilling, Joe LLama
Figure caption: Top: cloud vertical structure as a function of global surface relative humidity. Shading indicates layer optical depth, with darker purple shades corresponding to more opaque layers. Bottom: global surface layer temperature (red; left Y-axis) and planetary Bond albedo (blue; right Y-axis) as a function of global surface relative humidity. Planetary surface temperature reaches a maximum at 301 K and a minimum at 282 K. The planetary Bond albedo spans a range of 0.47–0.525.
Paper: A Radiative-convective Model for Terrestrial Planets with Self-consistent Patchy Clouds 
Authors: James D. Windsor, Tyler D. Robinson, Ravi kumar Kopparapu, Amber V. Young, David E. Trilling, Joe LLama
Figure caption: Upwelling (left) and downwelling (right) radiative fluxes for CLIMA (solid) and SMART (dashed). Green lines correspond to thermal fluxes and black lines correspond to solar fluxes. Deep-atmospheric flux profiles are in good agreement.
Paper: A Radiative-convective Model for Terrestrial Planets with Self-consistent Patchy Clouds 
Authors: James D. Windsor, Tyler D. Robinson, Ravi kumar Kopparapu, Amber V. Young, David E. Trilling, Joe LLama
Figure caption: Model schematic. Here, ﹩{F}_{{\rm{s}}}^{\mathrm{net}}﹩,﹩{F}_{{\rm{t}}}^{\mathrm{net}}﹩, and F c are the net short wave (hatched pattern), the net thermal (blue), and the convective heat flux (orange), respectively.
Paper: A Radiative-convective Model for Terrestrial Planets with Self-consistent Patchy Clouds 
Authors: James D. Windsor, Tyler D. Robinson, Ravi kumar Kopparapu, Amber V. Young, David E. Trilling, Joe LLama
Figure caption: Planetary reflectance as a function of wavelength for Earth-like example cases around a Sun-like host star. As shown in Table 1, cloud fraction decreases as case number increases. We also show a high-fidelity 3D spectral simulation of Earth for comparison (Robinson et al. 2011). All model reflection spectra showcase high reflectivity at visible and shorter near-infrared wavelengths. At longer near-infrared wavelengths, overall larger droplets in lower-case-number simulations result in more absorptive clouds, thereby flipping the relationship between continuum reflectivity and case number seen at shorter wavelengths.
Paper: A Radiative-convective Model for Terrestrial Planets with Self-consistent Patchy Clouds 
Authors: James D. Windsor, Tyler D. Robinson, Ravi kumar Kopparapu, Amber V. Young, David E. Trilling, Joe LLama
Figure caption: Top: reflectance spectra for the Super-Earth cases. Navy blue is is a Super-Earth climate with a midrange sedimentation efficiency, light blue is a climate with a high sedimentaion efficiency. Here, red represents a low-range sedimentation efficiency of 1 with 100% fractional cloudiness, and orange represents a midrange sedimentation efficiency of 2. The gray solid line represents a Super-Earth spectral model without clouds. The Super-Earth models with 100% clouds result in cloud decks that are comparably lower, resulting in more water-vapor absorption above the cloud layers and subsequently deeper water-vapor absorption spectral features.
Paper: A Radiative-convective Model for Terrestrial Planets with Self-consistent Patchy Clouds 
Authors: James D. Windsor, Tyler D. Robinson, Ravi kumar Kopparapu, Amber V. Young, David E. Trilling, Joe LLama
Figure caption: Atmospheric thermal profiles from the forward climate model with various values of insolation (gray shades) vs. a profile from the inverse climate model (red). For the forward models, the insolation is guided by results in Figure 7 so that the equilibrium thermal structure should be similar to the inverse model. Ozone warming in the stratopause is driven by the incident amount of flux on the top of the planetary atmosphere. In this set of forward models there is a divergence in the stratopause temperature due to changes in the amount of incident flux (i.e., S = 0.879 results in less ozone warming above the cloud deck, whereas S = 2.190 results in large ozone warming above the cloud deck). Overall, the surface temperatures in each forward case are near to that provided by the inverse climate model.
Paper: A Radiative-convective Model for Terrestrial Planets with Self-consistent Patchy Clouds 
Authors: James D. Windsor, Tyler D. Robinson, Ravi kumar Kopparapu, Amber V. Young, David E. Trilling, Joe LLama
Figure caption: Super-Earth atmospheric thermal profiles for low (left column), midrange (middle column), and efficient (right column) values of the cloud sedimentation efficiency. Here, the top row represents 50% cloudy climates and the bottom row showcases climates with 100% clouds. Cloud vertical extent is indicated with a shaded box in the background.
Paper: A Radiative-convective Model for Terrestrial Planets with Self-consistent Patchy Clouds 
Authors: James D. Windsor, Tyler D. Robinson, Ravi kumar Kopparapu, Amber V. Young, David E. Trilling, Joe LLama
Figure caption: Atmospheric thermal structures for highlighted Earth-like test cases (solid colors) and the thermal structure from clear-sky CLIMA where the surface albedo is tuned as in previous works (red dashed). Cloud vertical extent is represented by background shaded boxes. Overall, the cloudy climate models have colder tropopause temperatures than the tuned clear-sky model but have similar stratopause and surface temperatures.
Paper: A Radiative-convective Model for Terrestrial Planets with Self-consistent Patchy Clouds 
Authors: James D. Windsor, Tyler D. Robinson, Ravi kumar Kopparapu, Amber V. Young, David E. Trilling, Joe LLama
Figure caption: Cloud radiative forcing as a function of the sedimentation efficiency (vertical axis) and the cloud fraction (horizontal axis). The dashed gray contour lines correspond to cloud radiative forcing spaced by 15 W m−2; negative numbers correspond to the net cooling effect due to the presence of clouds. Warm (redder) colors correspond to climate states in which the overall atmosphere is undergoing a net warming effect. Conversely, cool colors (bluer) correspond to states in which the climate undergoes a net cooling effect. The white region centered between the −60 and −75 W m−2 contour lines corresponds to where the net cooling effect of the clouds is balanced with the net warming effect due to water-vapor absorption.
Paper: A Radiative-convective Model for Terrestrial Planets with Self-consistent Patchy Clouds 
Authors: James D. Windsor, Tyler D. Robinson, Ravi kumar Kopparapu, Amber V. Young, David E. Trilling, Joe LLama
Figure caption: Log-normal cloud droplet number density at the cloud midpoint for Case 1 (green), Case 2 (cyan), Case 3 (black), and Case 4 (purple). The dashed horizontal lines represent the mean effective cloud droplet radii. The solid horizontal lines correspond to the mean geometric cloud droplet radii. Overall, climates with higher sedimentation efficiency result in cloud droplet number densities that are diminished but have larger cloud droplet radii.
Paper: A Radiative-convective Model for Terrestrial Planets with Self-consistent Patchy Clouds 
Authors: James D. Windsor, Tyler D. Robinson, Ravi kumar Kopparapu, Amber V. Young, David E. Trilling, Joe LLama
Figure caption: Water-vapor radiative forcing (blue) and S eff (green) as a function of tropospheric relative humidity. The radiative forcing of water vapor CF w quickly approaches 50 W m−2 at a relative humidity value of 5% throughout the troposphere. This corresponds to a S eff value of 1.0 and represents a neutral radiative environment. Increasing the amount of water vapor results in higher radiative forcing values and an overall warming effect on the planetary surface.
Paper: The Timeline of Early Lunar Bombardment Constrained by the Evolving Distributions of Differently Aged Melt
Authors: Tiantian Liu, Greg Michael, Kai Wünnemann
Figure caption: Three different scenarios of the early bombardment history, where the impact rate functions are shown above and the plots below are in cumulative form. The typical Neukum impact rate (panel (a)) is taken to represent the tail-end scenario. The impact rate of the terminal cataclysm scenario (panel (c)) is the same as the tail-end but contains an addition of a “cataclysmic peak” at 3.9 Ga. The impact rate of the sawtooth scenario (panel (b)) is directly adopted from Morbidelli et al. (2012): for t < 4.1 Ga, dN 20/dt = 2.7 × 10−16 e 6.93t + 5.9 × 10−7; for t > 4.1 Ga, dN 20/dt = 2.5 × 10−2 e −[ − 4.5 − t]/0.003] ∧ 0.34, where the time t is measured in Ga. In the cumulative forms, the plausible distribution of time points for basin-forming events as predicted from N(20) crater densities (Orgel et al. 2018) is indicated by gray data. The four largest basin events, South Pole–Aitken, Serenitatis, Nectaris, and Imbrium, are labeled and indicated with black lines. The green lines indicate the calculated crater-derived ages of newly detected pre-Nectarian basins (Conrad et al. 2018).
Paper: The Timeline of Early Lunar Bombardment Constrained by the Evolving Distributions of Differently Aged Melt
Authors: Tiantian Liu, Greg Michael, Kai Wünnemann
Figure caption: Globally average distribution of differently aged melt in the near surface. (a) K–Ar datings of lunar meteorites (Michael et al. 2018), where the black curve is the first-order spline interpolation of the gray histogram. The italic number indicates the number of datings. Panels (b) and (c) are the model-derived melt age distributions (top 5 m, comparable to the typical origin depth of lunar meteorites; Warren 1994) while considering the basin list of Orgel et al. (2018) and the addition of newly detected pre-Nectarian basins (Conrad et al. 2018), respectively. The considered basins in panel (c) are 12 more than in panel (b). The shaded areas indicate the standard deviation of the 10 times Monte Carlo simulations.
Paper: Thermal Properties of the Leading Hemisphere of Callisto Inferred from ALMA Observations
Authors: Maria Camarca, Katherine de Kleer, Bryan Butler, Alex B. Akins, Alexander Thelen, Imke de Pater, Mark A. Gurwell, Arielle Moullet
Figure caption: Side-by-side comparison of the localized thermal residuals on Callisto with an albedo map projected to the same observer sublongitude and sublatitude. The latitude/longitude grid spacing is 30°. The ellipse in the lower left corner of each panel represents the ALMA synthesized beam at the time of observing. Left panel: projected albedo map of Callisto with major geologic features identified. Contrast was added for ease of identification. Middle panel: representative residuals from the range of best-fit two thermal inertia models. Right panel: residual contour lines drawn on the projected albedo map to better illustrate the correlation between certain warm/cold regions and local terrain. Contours are drawn for ±3, ±2, and ±1 K.
Paper: Thermal Properties of the Leading Hemisphere of Callisto Inferred from ALMA Observations
Authors: Maria Camarca, Katherine de Kleer, Bryan Butler, Alex B. Akins, Alexander Thelen, Imke de Pater, Mark A. Gurwell, Arielle Moullet
Figure caption: Summary of best-fit two-Γ models. Each cell represents the linear mixture results for a combination of Γlower (vertical axis) and Γhigher (horizontal axis). The text imposed on each cell gives the range of Γlower (shortened to Γ l  on the plot) that satisfied the χ 2 cutoff. The actual color of each cell corresponds to the minimum χ 2 achieved within a given mixture range. For example, the cell corresponding to the mixture of the Γlower = 20 and Γhigher = 1600 models is labeled with “Γ l  = 30%–50%,” meaning that mixtures of 30%–50% Γlower = 20 and 70%–50% Γhigher = 1600 met the χ 2 constraints. The range of acceptable emissivities is included in the upper left corner of the plot. Empty cells marked with a hatch pattern signal model pairs for which no mixing ratio of thermal inertia components met the χ 2 cutoff. The full range of Γ values tested spanned Γlower = 15–400 and Γhigher = 500–2000.
Paper: Thermal Properties of the Leading Hemisphere of Callisto Inferred from ALMA Observations
Authors: Maria Camarca, Katherine de Kleer, Bryan Butler, Alex B. Akins, Alexander Thelen, Imke de Pater, Mark A. Gurwell, Arielle Moullet
Figure caption: Summary of disk-integrated brightness temperature measurements for Callisto plotted as a function of wavelength. The gray vertical bar highlights the wavelength of our measurement, which fits well with most neighboring data. Data in this plot are taken from Berge & Muhleman (1975), Butler (2012), de Pater et al. (1984, 1989, 2021), Gurwell & Moullet (ALMA memo #594; Butler 2012), Moreno (2007), Muhleman & Berge (1991), Muhleman et al. (1986), Pauliny-Toth et al. (1974, 1977); Ulich & Conklin (1976), Ulich (1981), and Ulich et al. (1984). The data behind Figure 4 are available in machine-readable format.(The data used to create this figure are available.)
Paper: Thermal Properties of the Leading Hemisphere of Callisto Inferred from ALMA Observations
Authors: Maria Camarca, Katherine de Kleer, Bryan Butler, Alex B. Akins, Alexander Thelen, Imke de Pater, Mark A. Gurwell, Arielle Moullet
Figure caption: Normalized continuum images of the Galilean satellites obtained with ALMA. The longitude indicated on the top right of each plot is the subobserver longitude at the time of the observation. The latitude/longitude lines are spaced in 30° increments. Io data are from de Pater et al. (2020), Europa from Thelen et al. (2023), Ganymede from de Kleer et al. (2021a), and Callisto from this work.