adsabs/AstroCLIMB huggingface dataset:
Paper: Spatially Resolved Observations of Europa’s Surface with Subaru/IRCS at 1.0–1.8 μm: Upper Limits to the Abundances of Hydrated Cl-bearing Salts
Authors: Shuya Tan, Yasuhito Sekine, Masayuki Kuzuhara
Figure caption: Extracted signal counts per second from Europa’s surface. Each curve shows the spectra of one surface area (see the top insets for the locations). (a) Data for the southern regions in the leading hemisphere observed on 2019 May 17. (b) Data for the southern regions in the trailing hemisphere observed on 2019 May 18. In the gray wavelength ranges, the spectra contain high levels of noise.
Paper: Spatially Resolved Observations of Europa’s Surface with Subaru/IRCS at 1.0–1.8 μm: Upper Limits to the Abundances of Hydrated Cl-bearing Salts
Authors: Shuya Tan, Yasuhito Sekine, Masayuki Kuzuhara
Figure caption: Extracted signal counts per second from the northern and equatorial regions of Europa’s surface. Each curve shows spectra for a given surface area (see the top insets for the corresponding locations). Data for the (a) equatorial and (b) northern regions in the leading hemisphere were taken on 2019 May 17. Data for the (c) equatorial and (d) northern regions in the trailing hemisphere were taken on 2019 May 18. In the gray wavelength ranges, the spectra are affected by high levels of noise.
Paper: Spatially Resolved Observations of Europa’s Surface with Subaru/IRCS at 1.0–1.8 μm: Upper Limits to the Abundances of Hydrated Cl-bearing Salts
Authors: Shuya Tan, Yasuhito Sekine, Masayuki Kuzuhara
Figure caption: Connected reflectance spectra of the northern and equatorial regions on Europa at wavelengths of 1.0–1.8 μm. The observed area on Europa is shown in the top left panel. Data for the (a) equatorial and (b) northern regions in the leading hemisphere were taken on 2019 May 17. Data for the (c) equatorial and (d) northern regions in the trailing hemisphere were taken on 2019 May 18. Gray bars represent the wavelengths corresponding to telluric atmospheric absorption features (1.35–1.42 μm). Vertical dashed lines indicate a wavelength of 1.2 μm. In the reflectance spectra, we observe no significant absorption features around 1.2 μm, where hydrated Cl-bearing salts exhibit their characteristic absorption features. The slopes at 1.0–1.3 μm become steeper in regions close to the north pole.
Paper: Occultation Mapping of Io’s Surface in the Near-infrared. I. Inferring Static Maps
Authors: Fran Bartolić, Rodrigo Luger, Daniel Foreman-Mackey, Robert R. Howell, Julie A. Rathbun
Figure caption: Occultations of Io by Jupiter observed 3 months apart in 1998 using NASA’s IRTF. The light curve on the left is the ingress of the occultation (Io disappearing behind the limb of Jupiter), and the one on the right is the egress (Io appearing behind Jupiter). Each visible steplike feature in the light curve (a rapid increase/decrease in total flux at particular times) is a consequence of a volcanic hot spot coming in or out of view during the course of the occultation. The two light curves contain a wealth of information about thermal emission from Io’s surface. ⏬
Paper: Occultation Mapping of Io’s Surface in the Near-infrared. I. Inferring Static Maps
Authors: Fran Bartolić, Rodrigo Luger, Daniel Foreman-Mackey, Robert R. Howell, Julie A. Rathbun
Figure caption: Illustration of the transformation from a pixel map (left) defined on a fixed grid on the sphere into the spherical harmonic basis at l = 20 (right) via a linear operator  P  †. The intensity of each pixel was drawn independently from an exponential prior. The histograms below each of the maps show the distributions of intensities on the map. Since the pixel map is higher resolution than the spherical harmonic map at l = 20, the spherical harmonic map appears smoother, and the intensity distribution is more similar to a skewed Gaussian than an exponential distribution. Despite this fact, we find that fitting for maps in the pixel basis and transforming them to spherical harmonics at each MCMC step in order to compute the light curve analytically with starry is better than fitting for spherical harmonics because the pixel grid provides a strong constraint on the spherical harmonic map structure. ⏬
Paper: Occultation Mapping of Io’s Surface in the Near-infrared. I. Inferring Static Maps
Authors: Fran Bartolić, Rodrigo Luger, Daniel Foreman-Mackey, Robert R. Howell, Julie A. Rathbun
Figure caption: Posterior shrinkage for different kinds of observations of Io as a function of spherical harmonic degree (angular scale), averaged across all m modes. Posterior shrinkage of 1 represents maximum information gain in updating from the prior to the posterior, while zero represents no information gain. The posterior variance has been computed for different kinds of simulated observations of Io over the course of a single year: phase curves (blue), occultations by Jupiter (orange), combined phase curves and occultations by Jupiter (green), and occultations of Io by other Galilean moons (red lines). ⏬
Paper: Occultation Mapping of Io’s Surface in the Near-infrared. I. Inferring Static Maps
Authors: Fran Bartolić, Rodrigo Luger, Daniel Foreman-Mackey, Robert R. Howell, Julie A. Rathbun
Figure caption: Comparison between two models fitted to a simulated light curve of an occultation by Jupiter that was generated from a map consisting of a single bright spot (first row). Each of the columns beneath the simulated map shows the (median) posterior estimate of the map (second row), the data and the posterior flux samples (third row), and the residuals (fourth row). The left column corresponds to a model in which we place a Gaussian prior on spherical harmonic coefficients  y  and fit for  y , while the right column corresponds to a hybrid model in which we fit for pixels  p  (with an exponential prior on pixel intensity), but we use  y  to compute the flux analytically with starry. The benefit of using pixels is that it is much easier to encode assumptions on what the map should look like in pixel space rather than spherical harmonic space; this results in a visibly more accurate inferred map. The residuals for the pixel model show undesirable patterns due to ringing artifacts, which are a consequence of the exponential prior favoring more localized features; we discuss how to alleviate this issue in the text. ⏬
Paper: Occultation Mapping of Io’s Surface in the Near-infrared. I. Inferring Static Maps
Authors: Fran Bartolić, Rodrigo Luger, Daniel Foreman-Mackey, Robert R. Howell, Julie A. Rathbun
Figure caption: Normalized intensity of a spherical harmonic expansion of a Gaussian spot placed at 0° latitude and longitude. The expansion is in the quantity ﹩\cos {\rm{\Delta }}\theta ﹩, where Δθ = 5°. The three plots show the spot profile for increasing values of the smoothing parameter σ  s  = 0. The colored lines correspond to spot expansions up to a certain order, and the black line is the exact expansion. The purpose of smoothing is to taper higher-order spherical harmonic coefficients in order to suppress ringing artifacts that result in negative intensities. High levels of smoothing suppress the ringing completely, but as a result, they increase the spot size and eliminate differences between expansions above a certain order. Intermediate levels of smoothing provide a compromise between the two extremes. ⏬
Paper: Occultation Mapping of Io’s Surface in the Near-infrared. I. Inferring Static Maps
Authors: Fran Bartolić, Rodrigo Luger, Daniel Foreman-Mackey, Robert R. Howell, Julie A. Rathbun
Figure caption: Inferred l = 20 map obtained by fitting a pair of simulated ingress/egress occultation light curves of Io using a hybrid pixel model with a regularized horseshoe prior on the pixels. With this prior, the model is able to accurately recover the simulated map. The plot shows the simulated l = 20 map (first row), the posterior (median) estimate of the inferred map (second row), the inferred map as seen by the observer during the occultation (small circles), the data and posterior samples of the flux (orange lines), and the normalized residuals (fifth row). The locations of the simulated spots on the inferred map are marked with gray crosses. ⏬
Paper: Occultation Mapping of Io’s Surface in the Near-infrared. I. Inferring Static Maps
Authors: Fran Bartolić, Rodrigo Luger, Daniel Foreman-Mackey, Robert R. Howell, Julie A. Rathbun
Figure caption: Same as Figure 6 but for light curves with S/N = 10. ⏬
Paper: Occultation Mapping of Io’s Surface in the Near-infrared. I. Inferring Static Maps
Authors: Fran Bartolić, Rodrigo Luger, Daniel Foreman-Mackey, Robert R. Howell, Julie A. Rathbun
Figure caption: Inferred l = 20 maps obtained by fitting a pair of observations of occultations of Io by Jupiter in 1998. The observations were made several months apart with the NASA IRTF. We fit a single map to both observations simultaneously, although we allow for a difference in the overall amplitude of the map between ingress and egress. The model includes a Gaussian process to account for correlated noise caused by atmospheric variability and the fact that our limited-resolution map cannot fully capture the sharp steps in data. We treat all error bars as random variables and plot the median posterior estimates of those error bars. The plot shows the inferred maps (first row), the same maps from the perspective of the observer during the occultation (small circles), the light curves and posterior samples of the flux including the Gaussian process (orange lines), and the residuals with respect to a median flux estimate. The maps show two hot spots; the bright one is emission from Loki Patera, and the faint one is most likely emission from Kanehekili. A detailed view of the two hot spots is shown in Figure 9. ⏬
Paper: Occultation Mapping of Io’s Surface in the Near-infrared. I. Inferring Static Maps
Authors: Fran Bartolić, Rodrigo Luger, Daniel Foreman-Mackey, Robert R. Howell, Julie A. Rathbun
Figure caption: Contour plot of the hot spots shown in Figure 8 overlaid on the U.S. Geological Survey’s map of the surface of Io, which was constructed from observations by the Galileo spacecraft. The left hot spot is centered around Loki Patera, and the right hot spot is centered at the Kanehekili Fluctus lava flow. The contour lines show the 5th, 50th, and 95th percentiles of intensity above an arbitrarily defined intensity of the “background” region around the spot. The asymmetric white cross in the center of the contours in each panel shows the uncertainty in the inferred position of the peak intensity of the hot spot. This uncertainty is so small for the spot on the left that the cross is barely visible. It is much larger for the spot on the right, where the uncertainty in longitude is ∼2° and the uncertainty in the latitude of the spot is ∼15°. ⏬
Paper: Occultation Mapping of Io’s Surface in the Near-infrared. I. Inferring Static Maps
Authors: Fran Bartolić, Rodrigo Luger, Daniel Foreman-Mackey, Robert R. Howell, Julie A. Rathbun
Figure caption: Same as Figure 8, except this figure shows the output of a model that does not include a Gaussian process to account for correlated structure in the data. As a result, the model tries to capture the correlations in the data by placing two extra spots on the map and inflating the error bars. At least one of the two extra spots is artificial. ⏬
Paper: Occultation Mapping of Io’s Surface in the Near-infrared. I. Inferring Static Maps
Authors: Fran Bartolić, Rodrigo Luger, Daniel Foreman-Mackey, Robert R. Howell, Julie A. Rathbun
Figure caption: Inferred l = 20 maps obtained by fitting a pair of observations of occultations of Io by Jupiter in 2017. The observations were made several months apart with the NASA IRTF. We fit a single map to both observations simultaneously, although we allow for a difference in the overall amplitude of the map between ingress and egress. The model includes a Gaussian process to account for correlated noise caused by atmospheric variability and the fact that our limited-resolution map cannot fully capture the sharp steps in the data. We treat all error bars as random variables and plot the median posterior estimates of those error bars. The plot shows the inferred maps (first row), the same maps from the perspective of the observer during the occultation (small circles), the light curves and posterior samples of the flux including the Gaussian process (orange lines), and the residuals with respect to a median flux estimate. The maps show two hot spots; the bright one is emission from Loki Patera, and the faint one is emission from Janus. A detailed view of the two hot spots is shown in Figure 12. ⏬
Paper: Occultation Mapping of Io’s Surface in the Near-infrared. I. Inferring Static Maps
Authors: Fran Bartolić, Rodrigo Luger, Daniel Foreman-Mackey, Robert R. Howell, Julie A. Rathbun
Figure caption: Contour plot of the hot spots shown in Figure 11 overlaid on the U.S. Geological Survey’s map of the surface of Io, which was constructed from observations by the Galileo spacecraft. The left hot spot is centered around Loki Patera, and the right hot spot is centered around Janus Patera. The contour lines show the 5th, 50th, and 95th percentiles of intensity above an arbitrarily defined intensity of the “background” region around the spot. The asymmetric white cross in the center of the contours in each panel shows the uncertainty in the inferred position of the peak intensity of the hot spot (barely visible in the left panel). ⏬
Paper: Ariel's Elastic Thicknesses and Heat Fluxes
Authors: Chloe B. Beddingfield, Richard J. Cartwright, Erin Leonard, Tom Nordheim, Francesca Scipioni
Figure caption: Estimated heat fluxes across Ariel’s imaged surface for pure H2O ice and for H2O ice mixed with NH3 hydrates (Table 2). The stars are labeled to show the associated chasma: (A) Unnamed Canyon 1 (Figure 4); (B) Kewpie (Figure 4); (C) Brownie (Figure 4); (D) Pixie, northern flank (Figure 4); (E) Pixie, southern flank (Figure 4); (F) Kra (Figure 4); (G) Sylph Chasma, northern flank (Figure 5); (H) Sylph Chasma, southern flank (Figure 5); (I) Kachina Chasmata (Figure 6); (J) Korrigan Chasma, estimated values assuming a pure H2O ice lithosphere are from Peterson et al. (2015) (Figure 4). Here we also provide estimated values in this region for a lithosphere that contains NH3 hydrates. These values incorporate the estimated range of values for T  b  and T  e  discussed in Peterson et al. (2015). See Figure 2 for the geologic unit and tectonic symbol legends. The nonannotated version of the underlying geologic map is provided in Croft & Soderblom (1991). The results displayed here are also presented in Table 2.
Paper: Ariel's Elastic Thicknesses and Heat Fluxes
Authors: Chloe B. Beddingfield, Richard J. Cartwright, Erin Leonard, Tom Nordheim, Francesca Scipioni
Figure caption: (a) Annotated version of Voyager 2 ISS image c2684338 (pixel resolution: 1300 m pixel−1) showing the location of Sylph Chasma and nearby large impact craters. (b) The locations of the profile lines used for this analysis. (c) The DEM generated for this region. (d) The locations of impact craters used to estimate the uncertainty associated with the DEM by analyzing crater depth-diameter ratios and comparing them with previously estimated values for Ariel (results shown in Figure A1).
Paper: Ariel's Elastic Thicknesses and Heat Fluxes
Authors: Chloe B. Beddingfield, Richard J. Cartwright, Erin Leonard, Tom Nordheim, Francesca Scipioni
Figure caption: (a) Annotated version of Voyager 2 ISS image c2684533 (pixel resolution: 999 m pixel−1) showing the location of Kachina Chasmata. (b) The locations of the profile lines used for this analysis. (c) The DEM generated for this region. (d) The locations of impact craters used to estimate the uncertainty associated with the DEM by analyzing crater depth-diameter ratios and comparing them with previously estimated values for Ariel (results shown in Figure A1).
Paper: Ariel's Elastic Thicknesses and Heat Fluxes
Authors: Chloe B. Beddingfield, Richard J. Cartwright, Erin Leonard, Tom Nordheim, Francesca Scipioni
Figure caption: Estimated elastic thicknesses across Ariel’s imaged surface (Table 2). The stars are labeled to show the associated chasma: (A) Unnamed Canyon 1 (Figure 4); (B) Kewpie (Figure 4); (C) Brownie (Figure 4); (D) Pixie, northern flank (Figure 4); (E) Pixie, southern flank (Figure 4); (F) Kra (Figure 4); (G) Sylph Chasma, northern flank (Figure 5); (H) Sylph Chasma, southern flank (Figure 5); (I) Kachina Chasmata (Figure 6); (J) Korrigan Chasma, estimated values are from Peterson et al. (2015) (Figure 4). See Figure 2 for the geologic unit and tectonic symbol legends. The nonannotated version of the underlying geologic map is provided in Croft & Soderblom (1991). The results displayed here are also presented in Table 2.
Paper: Ariel's Elastic Thicknesses and Heat Fluxes
Authors: Chloe B. Beddingfield, Richard J. Cartwright, Erin Leonard, Tom Nordheim, Francesca Scipioni
Figure caption: Combined profile data (gray) with superimposed model flexural profiles (blue) for all chasmata analyzed. The profile line locations for the Unnamed Canyon (Lines 1–5), Kewpie (Lines 6–8), Brownie (Lines 10–13), Pixie (Lines 14–22), and Kra (Lines 23 and 24) are shown in Figure 4. The data for Sylph (Lines 25–45) are shown in Figure 5, and the data for Kachina (Lines 46–65) are shown in Figure 6. The blue lines show the modeled flexural profiles that best fit the profile data for each chasma. See Section A.2 for plots that show the uncertainty associated with these data.(The data used to create this figure are available.)
Paper: Ariel's Elastic Thicknesses and Heat Fluxes
Authors: Chloe B. Beddingfield, Richard J. Cartwright, Erin Leonard, Tom Nordheim, Francesca Scipioni
Figure caption: Combined profile data (gray) with superimposed model flexural profiles that represent the uncertainty minimum (blue) for all chasmata analyzed. The profile line locations for the Unnamed Canyon (Lines 1–5), Kewpie (Lines 6–8), Brownie (Lines 10–13), Pixie (Lines 14–22), and Kra (Lines 23 and 24) are shown in Figure 4. The data for Sylph (Lines 25–45) are shown in Figure 5, and the data for Kachina (Lines 46–65) are shown in Figure 6. See Figure 7 for plots that show the modeled best-fit flexural profiles associated with these data.
Paper: Ariel's Elastic Thicknesses and Heat Fluxes
Authors: Chloe B. Beddingfield, Richard J. Cartwright, Erin Leonard, Tom Nordheim, Francesca Scipioni
Figure caption: Combined profile data (gray) with superimposed model flexural profiles that represent the uncertainty maximum (blue) for all chasmata analyzed. The profile line locations for the Unnamed Canyon (Lines 1–5), Kewpie (Lines 6–8), Brownie (Lines 10–13), Pixie (Lines 14–22), and Kra (Lines 23 and 24) are shown in Figure 4. The data for Sylph (Lines 25–45) are shown in Figure 5, and the data for Kachina (Lines 46–65) are shown in Figure 6. See Figure 7 for plots that show the modeled best-fit flexural profiles associated with these data.
Paper: Ariel's Elastic Thicknesses and Heat Fluxes
Authors: Chloe B. Beddingfield, Richard J. Cartwright, Erin Leonard, Tom Nordheim, Francesca Scipioni
Figure caption: Depth-diameter (d-D) ratios of impact craters analyzed in the DEMs used for this work (Figure 2) and those analyzed in Schenk (1989). Comparison of these d-D ratios corroborates the accuracy of the DEMs generated for this work.
Paper: A Collision Mechanism for the Removal of Earth's Trojan Asteroids
Authors: Kevin J. Napier, Larissa Markwardt, Fred C. Adams, David W. Gerdes, Hsing Wen Lin, 省文 林
Figure caption: Histograms of changes in Earth’s semimajor axis (left) and eccentricity (right) during each simulation. The initial values were a = 1 au and e = 0.0167.
Paper: Moderately High Obliquity Promotes Biospheric Oxygenation
Authors: Megan N. Barnett, Stephanie L. Olson
Figure caption: Export particulate organic carbon (POC; panel (a)), oxygen flux (﹩{F}_{{{\rm{O}}}_{2}}﹩; panel (b)), and oxygen flux per unit organic carbon (﹩{F}_{{{\rm{O}}}_{2}}﹩:POC; panel (c)) from varying phosphate levels at different obliquity levels. The POL label stands for present-day ocean levels and indicates the present-day Earth phosphate inventory scenario. Other scenarios are labeled with their modification factor times present-day ocean levels. RLS is tuned to match chemical profiles from Earth’s present-day ocean and present-day obliquity (Ridgwell et al. 2007).
Paper: Moderately High Obliquity Promotes Biospheric Oxygenation
Authors: Megan N. Barnett, Stephanie L. Olson
Figure caption: Seasonal Northern Hemisphere (N.H.) values for average mixed layer depth (panel (a)), total surface ocean PO4 (panel (b)), and total export POC (panel (c)).
Paper: Moderately High Obliquity Promotes Biospheric Oxygenation
Authors: Megan N. Barnett, Stephanie L. Olson
Figure caption: Export particulate organic carbon (POC; panel (a)), oxygen flux (﹩{F}_{{{\rm{O}}}_{2}}﹩; panel (b)), and oxygen flux per unit organic carbon (﹩{F}_{{{\rm{O}}}_{2}}﹩:POC; panel (c)) for scenarios with various RLSs (each scenario is labeled by the modifying factor times RLS) at different obliquity levels. Phosphate inventory in each of these experiments is set to POL.
Paper: Moderately High Obliquity Promotes Biospheric Oxygenation
Authors: Megan N. Barnett, Stephanie L. Olson
Figure caption: Annual global ﹩{F}_{{{\rm{O}}}_{2}}﹩ for simulations with covaried phosphate inventory, RLS, and obliquity. We consider obliquities of 0° (panel (a)), 15° (panel (b)), 30° (panel (c)), and 45° (panel (d)). As seen in the single variable models, annual global ﹩{F}_{{{\rm{O}}}_{2}}﹩ increases with increasing phosphate inventory, decreasing RLS, and increasing obliquity.
Paper: New Polarimetric Data for the Galilean Satellites: Europa Observations and Modeling
Authors: Nikolai Kiselev, Vera Rosenbush, Karri Muinonen, Ludmilla Kolokolova, Aleksandr Savushkin, Nikolai Karpov
Figure caption: (a) Original data for Europa and the fit to them by a second-degree polynomial (blue curve) obtained by Dollfus (1975). (b) Comparison of the Dollfus data (blue circles) with our new polarimetric measurements (black circles) and the data obtained by Rosenbush & Kiselev (2005) (red circles) and Zaitsev (2016) (green circles). The fit to our data is displayed by the solid gray curve.
Paper: Moderately High Obliquity Promotes Biospheric Oxygenation
Authors: Megan N. Barnett, Stephanie L. Olson
Figure caption: Global annual export particulate organic carbon for simulations with covaried phosphate inventory, RLS, and obliquity. We consider obliquities of 0° (panel (a)), 15° (panel (b)), 30° (panel (c)), and 45° (panel (d)). Annual global export POC increases with increasing phosphate inventory (Figure 2), decreasing RLS (Figure 3), and increasing obliquity.
Paper: Moderately High Obliquity Promotes Biospheric Oxygenation
Authors: Megan N. Barnett, Stephanie L. Olson
Figure caption: Annual global ﹩{F}_{{{\rm{O}}}_{2}}﹩ per unit POC for simulations with covaried phosphate inventory, RLS, and obliquity. We consider obliquities of 0° (panel (a)), 15° (panel (b)), 30° (panel (c)), and 45° (panel (d)). Global average ﹩{F}_{{{\rm{O}}}_{2}}﹩:POC increases with increasing phosphate inventory, increasing RLS, and increasing obliquity.
Paper: Moderately High Obliquity Promotes Biospheric Oxygenation
Authors: Megan N. Barnett, Stephanie L. Olson
Figure caption: Depiction and definitions of parameters of interest varied in models in this paper. The white dashed–dotted horizontal line in the right panel indicates the depth of the RLS in the schematic.
Paper: Moderately High Obliquity Promotes Biospheric Oxygenation
Authors: Megan N. Barnett, Stephanie L. Olson
Figure caption: Global maps of annual average sea-ice cover percentage and oxygen flux for simulations with POL phosphate inventory and RLS. We consider obliquities of 0° (panels (a)/(b)), 15° (panels (c)/(d)), 30° (panels (e)/(f)), and 45° (panels (g)/(h)). The left color bar denotes the percentage of sea-ice cover in each area box, while the right color bar denotes annual average oxygen flux from each quadrant. The gray regions correspond to areas of land coverage, where ice coverage and oxygen flux are not measured.