adsabs/AstroCLIMB huggingface dataset:
Paper: Did Earth Eat Its Leftovers? Impact Ejecta as a Component of the Late Veneer
Authors: Philip J. Carter, Sarah T. Stewart
Figure caption: Evolution of the provenance correlation coefficient, body mass, and semimajor axis for the growing embryo and the 12 bodies with the most similar provenances at the end of an example Grand Tack simulation (022GTJf6hgas). The left and right panels correspond to the most massive and second most massive embryos as shown in Figure 3. The thick purple line indicates the embryo, and the thin solid, dashed, and dotted–dashed lines indicate the most similar bodies, colored in order of correlation coefficient from purple (highest) to yellow (lowest). Note that the properties of the bodies shown in these plots, including orbits, correspond to the fixed output times of the provenance data; collision ejecta may have undergone tens of orbits before appearing on the plots, hence the wide range of semimajor axes for new fragments.
Paper: Did Earth Eat Its Leftovers? Impact Ejecta as a Component of the Late Veneer
Authors: Philip J. Carter, Sarah T. Stewart
Figure caption: Evolution of the provenance correlation coefficient, body mass, and semimajor axis for the growing embryo and the 12 bodies with the most similar provenances at the end of an example calm accretion simulation (022f6sgas_4). The left and right panels correspond to the most massive and second most massive embryos as shown in Figure 4. The thick purple line indicates the embryo, and the thin solid, dashed, and dotted–dashed lines indicate the most similar bodies, colored in order of correlation coefficient from purple (highest) to yellow (lowest).
Paper: Did Earth Eat Its Leftovers? Impact Ejecta as a Component of the Late Veneer
Authors: Philip J. Carter, Sarah T. Stewart
Figure caption: Fraction of surviving bodies that are collision fragments at the end of example Grand Tack (022GTJf6hgas; purple dashed line) and calm accretion (022f6sgas_4; orange solid line) simulations. The large fragment fractions at 1.8–2.0 and 2.6–2.8 au (0.14 and 1.0) for the calm accretion case are due to the small numbers of bodies in these bins (seven and one, respectively).
Paper: Did Earth Eat Its Leftovers? Impact Ejecta as a Component of the Late Veneer
Authors: Philip J. Carter, Sarah T. Stewart
Figure caption: Number and fraction of planetesimals (M < 0.01 M ⊕) with correlation coefficients between the planetesimal core and embryo mantle of 0.5 or above for each embryo in several simulations. The numbers of planetesimals with similar cores are shown as a function of semimajor axis (left) and mass (right). Blue points are for Grand Tack simulations, and magenta points are for calm accretion simulations. The sizes of the points are scaled to the mass of the embryos. Embryos from low-resolution simulations are excluded. The fraction of planetesimals that are similar is calculated only for planetesimals with semimajor axes within 0.2 au of the embryo, as indicated by the black horizontal bar in the lower left panel.
Paper: Did Earth Eat Its Leftovers? Impact Ejecta as a Component of the Late Veneer
Authors: Philip J. Carter, Sarah T. Stewart
Figure caption: Provenance correlation distributions for nearby bodies with increasing distance from the embryo for the three most massive embryos in example Grand Tack (022GTJf6hgas) and calm accretion (022f6sgas_4) simulations. The colors and line styles (see legend) indicate the width of the bin around the embryo in which correlation is calculated. The gray dashed vertical line highlights the correlation coefficient of 0.5 used for comparison elsewhere in this work.
Paper: Did Earth Eat Its Leftovers? Impact Ejecta as a Component of the Late Veneer
Authors: Philip J. Carter, Sarah T. Stewart
Figure caption: Provenance histograms for the most massive embryo (gray background) and the nine bodies with the most similar bulk provenances (with similarity decreasing left to right and top to bottom) at the end of a Grand Tack simulation (022GTJf6hgas). The black histogram shows the bulk provenance for each body, and the colored bars show the corresponding core provenance. The numbers above each panel give the mass and core mass fraction (CF) of each body.
Paper: Asteroid Polarimetric Phase Behavior in the Near-infrared: S- and C-complex Objects
Authors: Joseph R. Masiero, S. Tinyanont, Maxwell A. Millar-Blanchaer
Figure caption: Polarimetric phase curve for four S-complex asteroids: Juno, Iris, Eunomia, and Massalia. J- and H-band data are presented in this manuscript, while u-, b-, v-, and r-band data for these three objects are from the Asteroid Polarimetric Database (Lupishko 2022). Functional form fits to the data using Equation (1) are shown with dashed lines, with the color of the line corresponding to the bandpass being fit.
Paper: Accurate Machine-learning Atmospheric Retrieval via a Neural-network Surrogate Model for Radiative Transfer
Authors: Michael D. Himes, Joseph Harrington, Adam D. Cobb, Atılım Güneş Baydin, Frank Soboczenski, Molly D. O'Beirne, Simone Zorzan, David C. Wright, Zacchaeus Scheffer, Shawn D. Domagal-Goldman, Giada N. Arney
Figure caption: Four comparisons of planetary emission spectra predicted by MARGE and calculated by BART. The smoothed curves use a Savitzky–Golay filter with a third-order polynomial across a window of 101 elements (100 cm−1). The purple color arises due to a detailed match between the red and blue spectra at high resolution. For the residuals, a black line is plotted at 0 to show regions where the neural network consistently over- or underpredicts the spectrum. A histogram of the high-resolution residuals appears to the right of the residual scatter plot, where the x-axis shows the probability density function (PDF) for the range of residual percentages. Top left: case with T(p) profile that increases in temperature with altitude, with H2O and CO2 emission lines. Top right: case with T(p) profile that decreases in temperature with altitude, with absorption primarily due to CH4 and H2O. Bottom left: cases with T(p) profile that has an inversion around 0.1 bar, with CH4, CO, and CO2 absorption and emission features. Bottom right: case with T(p) profile that is nearly isothermal at the pressures with sensitivity.
Paper: Accurate Machine-learning Atmospheric Retrieval via a Neural-network Surrogate Model for Radiative Transfer
Authors: Michael D. Himes, Joseph Harrington, Adam D. Cobb, Atılım Güneş Baydin, Frank Soboczenski, Molly D. O'Beirne, Simone Zorzan, David C. Wright, Zacchaeus Scheffer, Shawn D. Domagal-Goldman, Giada N. Arney
Figure caption: Schematic diagram of our inverse modeling method, color-coded based on the scope of our software packages. MARGE (Section 2.3.1) generates a data set based on a deterministic, forward process (e.g., RT) and trains a surrogate model to approximate that process. Using the trained surrogate, HOMER (Section 2.3.2) infers the inverse process (e.g., atmospheric retrieval) by simulating many forward models and comparing them to the target data (e.g., an observed spectrum) in a Bayesian framework.
Paper: The Promise and Limitations of Precision Gravity: Application to the Interior Structure of Uranus and Neptune
Authors: Naor Movshovitz, Jonathan J. Fortney
Figure caption: Same views as Figure 5, showing samples constrained with successively higher-order J  n  up to J 12 (﹩{n}_{\max }﹩ in Equation (3)), in addition to the constraints of mass, radius, and rotation period. The likelihood function used ﹩{\sigma }_{{J}_{2}}/{J}_{2}={10}^{-6}﹩, ﹩{\sigma }_{{J}_{4}}/{J}_{4}={10}^{-5}﹩, ﹩{\sigma }_{{J}_{6}}/{J}_{6}={10}^{-4}﹩, ﹩{\sigma }_{{J}_{8}}/{J}_{8}={10}^{-4}﹩, ﹩{\sigma }_{{J}_{10}}/{J}_{10}={10}^{-2}﹩, and ﹩{\sigma }_{{J}_{12}}/{J}_{12}={10}^{0}﹩.
Paper: The Promise and Limitations of Precision Gravity: Application to the Interior Structure of Uranus and Neptune
Authors: Naor Movshovitz, Jonathan J. Fortney
Figure caption: Same views as Figure 3, showing samples constrained by J 2 poorly known, J 2 precisely known, and J 2 and J 4 precisely known, in addition to the previous constraints of mass, radius, and rotation period.
Paper: The Promise and Limitations of Precision Gravity: Application to the Interior Structure of Uranus and Neptune
Authors: Naor Movshovitz, Jonathan J. Fortney
Figure caption: Three views of the baseline sample. Left: shaded region showing the range of density values, for each level surface mean radius s, between the 2nd and 98th percentiles of the sample, in effect, the extent of the sample minus the occasional outlier. Middle: subset of 20 density profiles from the sample, taken at equal intervals of percentiles of the central (s = 0) density values. Right: histogram of the MOI factor, integrated while accounting for the equilibrium shape of level surfaces.
Paper: The Promise and Limitations of Precision Gravity: Application to the Interior Structure of Uranus and Neptune
Authors: Naor Movshovitz, Jonathan J. Fortney
Figure caption: Illustration of the density parameterization used in this work. The dashed line is a degree-8 polynomial, the first part of Equation (4), constructed to match the surface density ρ 0 (a small but nonzero value) and have a vanishing derivative near the center. The two dotted lines are the inner (z 1 = 0.15) and outer (z 2 = 0.76) sigmoid functions of the second part of Equation (4). In this example, both are approximating a sharp discontinuity (ν 1 = ν 2 = 1000), meaning the increase in density (σ 1, σ 2) happens over a short span. The dotted–dashed line is the sum of the other three lines resulting in a density profile that matches the mass, radius, and low-order gravity of Uranus.
Paper: The Promise and Limitations of Precision Gravity: Application to the Interior Structure of Uranus and Neptune
Authors: Naor Movshovitz, Jonathan J. Fortney
Figure caption: Same views as Figure 7, showing samples obtained with a constant, rather than sampled, rotation period. (Note the changed horizontal axis limits in the MOI histogram.)
Paper: The Promise and Limitations of Precision Gravity: Application to the Interior Structure of Uranus and Neptune
Authors: Naor Movshovitz, Jonathan J. Fortney
Figure caption: Pressure–density envelopes integrated from sampled ρ(s) profiles of Uranus. Left: samples obtained with a Gaussian prior on the rotation period. Right: samples obtained with a known rotation period. Overlaid isentropes for hypothetical homogeneous compositions extend from a common T 10 bars = 150 K.
Paper: The Promise and Limitations of Precision Gravity: Application to the Interior Structure of Uranus and Neptune
Authors: Naor Movshovitz, Jonathan J. Fortney
Figure caption: Envelope view of sampled ρ(s) profiles of Uranus (left) and Neptune (right) matching currently available observables (Jacobson 2009, 2014). The dark shaded regions show the range of density values at every radius that lie between the 16th and 84th percentiles (roughly the sample’s 1σ spread), and the light shaded regions show the value between the 2nd and 98th percentiles, roughly the sample’s 2σ spread.
Paper: The Promise and Limitations of Precision Gravity: Application to the Interior Structure of Uranus and Neptune
Authors: Naor Movshovitz, Jonathan J. Fortney
Figure caption: Same views as Figure 3, showing samples obtained with large uncertainty (30 minute, light shade, solid lines), realistic uncertainty (10 minute, medium shade, dashed lines), and no uncertainty (dark shade, dotted lines) on the rotation period. The samples are essentially the same.
Paper: The Promise and Limitations of Precision Gravity: Application to the Interior Structure of Uranus and Neptune
Authors: Naor Movshovitz, Jonathan J. Fortney
Figure caption: The J values from an example MCMC run, illustrating correlations between J  n .
Paper: Asteroid Polarimetric Phase Behavior in the Near-infrared: S- and C-complex Objects
Authors: Joseph R. Masiero, S. Tinyanont, Maxwell A. Millar-Blanchaer
Figure caption: Comparison of predicted and measured polarization degrees and angles for polarized standard stars. The top left panel shows the measured polarization value against the literature values corrected for wavelength changes. The top right panel shows the difference in polarization (measured–literature) against the scaled literature values. The bottom left panel shows the measured angle of polarization compared to the literature angle, and the bottom right panel shows the difference in angle compared to the literature angle. Both difference plots include dashed lines showing the mean offset values, with the value for each band given in the legend.
Paper: Asteroid Polarimetric Phase Behavior in the Near-infrared: S- and C-complex Objects
Authors: Joseph R. Masiero, S. Tinyanont, Maxwell A. Millar-Blanchaer
Figure caption: Comparison of predicted and measured polarization degrees and angles for C-complex asteroids, which are not expected to show any chromatic variations. Plot layout is the same as shown in Figure 2. Polarization degree P here is given in the typical fashion for asteroid measurements, where negative values indicate polarization in the scattering plane and positive values polarization perpendicular to it, and no wavelength-dependent scaling has been applied. Polarization angles were only compared for cases where the predicted polarization was ∣P∣ > 0.25%.
Paper: Asteroid Polarimetric Phase Behavior in the Near-infrared: S- and C-complex Objects
Authors: Joseph R. Masiero, S. Tinyanont, Maxwell A. Millar-Blanchaer
Figure caption: Polarimetric phase curve for three C-complex asteroids: Ceres, Pallas, and Adeona. J- and H-band data are presented in this manuscript, while u-, b-, v-, and r-band data for these three objects are from the Asteroid Polarimetric Database (Lupishko 2022). Functional form fits to the data using Equation (1) are shown with dashed lines, with the color of the line corresponding to the bandpass being fit.
Paper: Asteroid Polarimetric Phase Behavior in the Near-infrared: S- and C-complex Objects
Authors: Joseph R. Masiero, S. Tinyanont, Maxwell A. Millar-Blanchaer
Figure caption: Histogram showing the changes in measured polarization between observations for nominally unpolarized standard stars. X-axis units are in percent polarization, and the mean for each band, given in the legend, is consistent with 0.1% overall system precision.
Paper: Accurate Machine-learning Atmospheric Retrieval via a Neural-network Surrogate Model for Radiative Transfer
Authors: Michael D. Himes, Joseph Harrington, Adam D. Cobb, Atılım Güneş Baydin, Frank Soboczenski, Molly D. O'Beirne, Simone Zorzan, David C. Wright, Zacchaeus Scheffer, Shawn D. Domagal-Goldman, Giada N. Arney
Figure caption: Comparisons between HOMER and BART posteriors. Top left: best-fit spectrum of HOMER, with 1σ, 2σ, and 3σ regions. Top right: normalized probability density functions of the 2D marginalized pairwise posteriors retrieved for HD 189733 b, with the 1D marginalized posteriors along the diagonal. The purple color arises from the close match between HOMER and BART. Bottom left: posterior median, 1σ from the median, and 2σ from the median T(p) profile. In the regions with sensitivity, HOMER closely matches BART, with a slightly greater uncertainty. Bottom right: normalized contribution functions, which show the pressure range each filter probes, for the best-fit BART model.
Paper: Accurate Machine-learning Atmospheric Retrieval via a Neural-network Surrogate Model for Radiative Transfer
Authors: Michael D. Himes, Joseph Harrington, Adam D. Cobb, Atılım Güneş Baydin, Frank Soboczenski, Molly D. O'Beirne, Simone Zorzan, David C. Wright, Zacchaeus Scheffer, Shawn D. Domagal-Goldman, Giada N. Arney
Figure caption: Example of a range test. The learning rate begins at a value too small to make noticeable changes to the weights of the model. At a learning rate of ∼4 ×  10−4, the loss begins to decrease, indicating that the model has begun learning. However, at a learning rate of ∼5 ×  10−3, the loss begins to behave erratically, and it becomes very large at a learning rate of 10−2. From this, a learning rate policy varying between 6 ×  10−4 and 4 ×  10−3 would likely perform well for this architecture.
Paper: A Predicted Dearth of Majority Hypervolatile Ices in Oort Cloud Comets
Authors: C. M. Lisse, G. R. Gladstone, L. A. Young, D. P. Cruikshank, S. A. Sandford, B. Schmitt, S. A. Stern, H. A. Weaver, O. Umurhan, Y. J. Pendleton, J. T. Keane, J. M. Parker, R. P. Binzel, A. M. Earle, M. Horanyi, M. El-Maarry, A. F. Cheng, J. M. Moore, W. B. McKinnon, W. M. Grundy, J. J. Kavelaars, I. R. Linscott, W. Lyra, B. L. Lewis, D. T. Britt, J. R. Spencer, C. B. Olkin, R. L. McNutt, H. A. Elliott, N. Dello-Russo, J. K. Steckloff, M. Neveu, O. Mousis
Figure caption: Species-specific Q gas vs. temperature curves for species expected in comets and KBOs (upper colored curves). Horizontal dashed lines: values of the thermally driven outgassing rates at which an icy species is depleted in 1, 10, 100, 1000, 4600, and 12,000 Myr for an Arrokoth-sized body. Upper colored curves: loss rates of a piece of surface ice evaporating into free space. Lower colored curves: the much slower loss rates for the same species, after allowing for an overlying lag layer with thermal diffusivity = 3 × 10−7 s2 m−1 impeding the flow of heat and gas into free space from the interior (Davidsson+21, Steckloff+21, Prialnik+21). Top axis: heliocentric distance from the Sun for a blackbody at local thermal equilibrium temperature T. From these curves and constraints, one can see that hypervolatile ices CO, N2, and CH4 are stable in cold, dense molecular clouds and in modern KBOs residing beyond ∼100 au from the Sun, but were lost by ∼20 Myr after Arrokoth's formation. One can also see that mesostable ices like CO2, while easily removed from the surface, can remain stably at depth inside a KBO for more than the age of the solar system.
Paper: Upper Limits on the Escape of Volatiles from (486958) Arrokoth Using New Horizons Alice Ultraviolet Spectrograph Observations
Authors: G. Randall Gladstone, Carey M. Lisse, Leslie A. Young, Joel Wm. Parker, Kelsi N. Singer, John R. Spencer, Harold A. Weaver, S. Alan Stern
Figure caption: Far-ultraviolet solar count rate spectra, using a log brightness scale in units of counts s−1 nm−1, obtained just after the New Horizons flyby of Arrokoth, are shown vs. time. The Sun was observed through the solar occultation channel of the Alice ultraviolet spectrograph in two periods, during 06:08:23–06:18:53 and 06:23:08–06:32:48 on 2019 January 1. Plotted on the left is the column density (black line) in units of 1014 molecules cm−2 that would be expected along the line of sight toward the Sun if N2 molecules were sublimating from Arrokoth into space at a rate of 2 × 1028 molecules s−1. The New Horizons range to Arrokoth and the closest distance between the line of sight toward the Sun and Arrokoth (i.e., the impact parameter) are also plotted for comparison (orange and blue lines, respectively). Apart from variations due to the movement of the Sun within the Alice field of view, the solar spectrum is unchanging during the entire observation, indicating no detectable opacity associated with volatile escape from Arrokoth.
Paper: Upper Limits on the Escape of Volatiles from (486958) Arrokoth Using New Horizons Alice Ultraviolet Spectrograph Observations
Authors: G. Randall Gladstone, Carey M. Lisse, Leslie A. Young, Joel Wm. Parker, Kelsi N. Singer, John R. Spencer, Harold A. Weaver, S. Alan Stern
Figure caption: Opacities detectable at the 3σ level during the Alice solar appulse observation following the Arrokoth flyby are shown as a function of wavelength (black line). The photoabsorption cross sections of several species are plotted for comparison, and each are scaled to the opacity limit at the minimum column density needed for detection at the 3σ level at some point in the Alice bandpass, using Equation (6). The corresponding loss rates needed to produce detectable column densities for each of the species are provided on the plot and in Table 1. Note that several species would first be detectable at similar wavelengths, e.g., CO2, O2, and CH4 at ∼98 nm, and C2H6 and CH3OH at ∼103 nm.
Paper: A Predicted Dearth of Majority Hypervolatile Ices in Oort Cloud Comets
Authors: C. M. Lisse, G. R. Gladstone, L. A. Young, D. P. Cruikshank, S. A. Sandford, B. Schmitt, S. A. Stern, H. A. Weaver, O. Umurhan, Y. J. Pendleton, J. T. Keane, J. M. Parker, R. P. Binzel, A. M. Earle, M. Horanyi, M. El-Maarry, A. F. Cheng, J. M. Moore, W. B. McKinnon, W. M. Grundy, J. J. Kavelaars, I. R. Linscott, W. Lyra, B. L. Lewis, D. T. Britt, J. R. Spencer, C. B. Olkin, R. L. McNutt, H. A. Elliott, N. Dello-Russo, J. K. Steckloff, M. Neveu, O. Mousis
Figure caption: Species-specific 3σ Q gas production upper limits for Arrokoth, as determined by Gladstone et al. (2022; horizontal colored dashed lines at 107–108 × 1021 molecules s−1). Also plotted on the same scale are the gas production rates for different expected icy species found in comets and KBOs (colored curves), as well as the local LTE temperature at 45 au from the Sun for Arrokoth and its subsolar (noon-time) temperature (vertical dashed lines). Hypervolatile species like N2, CO, and CH4 (red) violate the new NH/Alice upper detection limits by 6–8 orders of magnitude. After Lisse+21.
Paper: Europa’s Surface Water-ice Crystallinity and Correlations between Lineae and Hydrate Composition
Authors: Jodi R. Berdis, James R. Murphy, Nancy J. Chanover
Figure caption: Synthetic water-ice spectra with grain sizes of 50 μm produced for a viewing geometry that matches the location of the 15e015 NIMS cube and resampled at a spectral resolution that matches the 15e015 NIMS cube. We used a more recent set of optical constants, leading to slight differences in the shape of our water-ice absorption features compared to those used by Dalton et al. (2012). We included amorphous and crystalline water-ice spectra in our study, whereas Dalton et al. (2012) only included crystalline water-ice.
Paper: Europa’s Surface Water-ice Crystallinity and Correlations between Lineae and Hydrate Composition
Authors: Jodi R. Berdis, James R. Murphy, Nancy J. Chanover
Figure caption: A subset of grain sizes of the Hapke-modeled amorphous and crystalline water-ice spectra produced for a viewing geometry that matches the location of the 15e015 NIMS cube and resampled at a spectral resolution that matches the 15e015 NIMS cube. Solid lines represent crystalline water-ice, whereas dashed lines represent amorphous water-ice. The spectra for each set of grain sizes are offset by 0.2 in reflectance for viewing clarity.
Paper: Europa’s Surface Water-ice Crystallinity and Correlations between Lineae and Hydrate Composition
Authors: Jodi R. Berdis, James R. Murphy, Nancy J. Chanover
Figure caption: Representation of the 15e015 NIMS cube overlaying the relevant region from an image mosaic (image courtesy of USGS Astrogeology Science Center; Controlled Photomosaic Map of Europa, Je 15M CMN). The black rectangle with a blue border represents the approximate location and size of the 15e015 NIMS cube. Drizzlecomb Linea is labeled and crosses through the NIMS cube; however, the exact location where it crosses the cube is unknown, as the two data sets are not co-registered. Below the image is a spatial latitude–longitude representation of the data that exists within the 15e015 cube. The black pixels represent locations where image spectroscopy data were acquired.
Paper: Europa’s Surface Water-ice Crystallinity and Correlations between Lineae and Hydrate Composition
Authors: Jodi R. Berdis, James R. Murphy, Nancy J. Chanover
Figure caption: Abundance profiles across the 15e015 NIMS cube for MgSO4 · 6H2O, Na2SO4 · 10H2O, H2SO4, and KCl; all other non-water-ice species are not included, as they were present in trace amounts.
Paper: Europa’s Surface Water-ice Crystallinity and Correlations between Lineae and Hydrate Composition
Authors: Jodi R. Berdis, James R. Murphy, Nancy J. Chanover
Figure caption: Abundance profiles across the 15e015 NIMS cube for crystalline water-ice at grain sizes ranging from 25 to 250 μm.