adsabs/AstroCLIMB huggingface dataset:
Paper: Probing Ganymede’s Atmosphere with HST Lyα Images in Transit of Jupiter
Authors: Lorenz Roth, Gregorio Marchesini, Tracy M. Becker, H. Jens Hoeijmakers, Philippa M. Molyneux, Kurt D. Retherford, Joachim Saur, Shane R. Carberry Mogan, Jamey R. Szalay
Figure caption: Top: brightness of the 24 limb bins around Ganymede for observation oe9z04010 (red) with statistical error (shaded gray) and for our model image as shown in Figure 3(a) (black line). Bottom: difference between model and observation divided by the error σ.
Paper: Pluto’s Surface Mapping Using Unsupervised Learning from Near-infrared Observations of LEISA/Ralph
Authors: A. Emran, C. M. Dalle Ore, C. J. Ahrens, M. K. H. Khan, V. F. Chevrier, D. P. Cruikshank
Figure caption: The mean (red line) ±1σ standard deviation (gray shade) I/F spectra of each surface unit on Pluto using the PC-GMM. The spectra of the surface units in the subplots are labeled as C followed by the corresponding assigned surface unit number. The standard deviation of the I/F spectra shows varying degrees of closeness to the mean spectra at different wavelengths. All the classes (except C7) exhibit a clear presence of CH4 ice absorption bands with varying degrees of strength.
Paper: Pluto’s Surface Mapping Using Unsupervised Learning from Near-infrared Observations of LEISA/Ralph
Authors: A. Emran, C. M. Dalle Ore, C. J. Ahrens, M. K. H. Khan, V. F. Chevrier, D. P. Cruikshank
Figure caption: The average I/F spectra of the 10 composite surface units. The colors of the spectra correspond to the colors of the surface unit in Figure 7. The dashed vertical lines indicate N2 absorption at 2.15 μm (red), CO absorption at 1.58 μm (green), CH4:N2 absorption at 1.69 μm (black), and H2O absorption at 1.65 and 2.0 μm (blue). Note that the I/F values in the y-axis have offsets for clarity. For a higher-resolution colored version of the figure, the readers are referred to the online version of the paper (the wavelengths and average I/F data in the figure are available in the publicly available repository at https://github.com/alemran042/Pluto-PC-GMM-Data).
Paper: Pluto’s Surface Mapping Using Unsupervised Learning from Near-infrared Observations of LEISA/Ralph
Authors: A. Emran, C. M. Dalle Ore, C. J. Ahrens, M. K. H. Khan, V. F. Chevrier, D. P. Cruikshank
Figure caption: (a) LORRI higher-resolution (panchromatic) basemap projected to LEISA scenes. The LORRI basemap is labeled with latitude (blue) and longitude (black) grids and formal and informal names of major geomorphic features. The red rectangles represent the reference locations in Figure 9. (b) Eight surface units were found on Pluto using the PC-GMM. The surface units follow the latitudinal pattern and are consistent with broad geographic regions and geologic features. The north pole at Lowell Regio, Cthulhu Macula, Tombaugh Regio, SP and Venera Terra, and Pioneer Terra region show distinct surface units. The values in the legend indicate the assigned surface unit number. Note: the lower right region belongs to strongly shadowed areas, and thus the pixels at these locations were excluded from the map (see the text for a detailed explanation).
Paper: Pluto’s Surface Mapping Using Unsupervised Learning from Near-infrared Observations of LEISA/Ralph
Authors: A. Emran, C. M. Dalle Ore, C. J. Ahrens, M. K. H. Khan, V. F. Chevrier, D. P. Cruikshank
Figure caption: The generalized global compositional map of Pluto superposed on a higher-resolution LORRI basemap. The compositions of the surface units are labeled to the corresponding unit. The asterisk superscript appended to a component refers only to that component, where ∗ = larger grain/-rich material and ∗∗ = pure ice. The latitude and longitude are labeled as the blue and black grid, respectively. The values in the legend indicate the assigned surface unit number.
Paper: Pluto’s Surface Mapping Using Unsupervised Learning from Near-infrared Observations of LEISA/Ralph
Authors: A. Emran, C. M. Dalle Ore, C. J. Ahrens, M. K. H. Khan, V. F. Chevrier, D. P. Cruikshank
Figure caption: The probability plot of each surface unit at LEISA image pixel level using the PC-GMM. The subplots are superposed on the LORRI basemap. The probability scale ranges from 0 to 1, where higher values indicate a higher probability of the corresponding unit. We also refer the reader to Figure A5 in the Appendix, which shows that most of the surface units are dominated by pixels with higher probability values (close to 1).
Paper: Pluto’s Surface Mapping Using Unsupervised Learning from Near-infrared Observations of LEISA/Ralph
Authors: A. Emran, C. M. Dalle Ore, C. J. Ahrens, M. K. H. Khan, V. F. Chevrier, D. P. Cruikshank
Figure caption: The subset of the generalized surface map at mountains in (a) Cthulhu Macula, (b) Kiladze crater, and (c) Elliot crater. For the reference location, please see Figure 1(a).
Paper: Pluto’s Surface Mapping Using Unsupervised Learning from Near-infrared Observations of LEISA/Ralph
Authors: A. Emran, C. M. Dalle Ore, C. J. Ahrens, M. K. H. Khan, V. F. Chevrier, D. P. Cruikshank
Figure caption: Post-classification probabilities of subunits for the region enriched with N2 and CO (#6; top row) and the subunits for the H2O-rich areas (#3; bottom row). The central part of SP shows a distinct subunit, while the southern SP and Venera Terra share common characteristics. H2O ice at the Kiladze crater shows a difference from that at Krun Macula. Refer to the histogram plot in Figure A7 in the Appendix, which shows that the post-class surface units of C3 and C6 are dominated by pixels with higher probability values.
Paper: Pluto’s Surface Mapping Using Unsupervised Learning from Near-infrared Observations of LEISA/Ralph
Authors: A. Emran, C. M. Dalle Ore, C. J. Ahrens, M. K. H. Khan, V. F. Chevrier, D. P. Cruikshank
Figure caption: (a) Global-scale surface compositional distribution and inferred volatile transport directions (indicated by arrows) proposed in this study; (b) schematic view of the major ice distribution and direction of nitrogen sublimation transport explained by Protopapa et al. (2017). Panel (b) was adopted from Cruikshank et al. (2021) and Protopapa et al. (2017).
Paper: Pluto’s Surface Mapping Using Unsupervised Learning from Near-infrared Observations of LEISA/Ralph
Authors: A. Emran, C. M. Dalle Ore, C. J. Ahrens, M. K. H. Khan, V. F. Chevrier, D. P. Cruikshank
Figure caption: The cumulative explained variance (left panel) and scree plot (right panel) of the PC axes. At four PC axes, the plot shows 92.93% total variance of the data. The PC 5 onward shows a very gentle increase in the cumulative explained variance.
Paper: Ammonia Abundance Derived from Juno MWR and VLA Observations of Jupiter
Authors: Chris Moeckel, Imke de Pater, David DeBoer
Figure caption: PJ3 flyby during 2016 December. The map shows X-band (8–10 GHz) VLA observations taken concurrently with the flyby to show the longitudinal structure of the planet (Moeckel et al. 2019; these VLA data will be fully published in a subsequent paper). The red footprints correspond to MWR’s Channel 5 observation, where we have plotted the highest resolution (i.e., smallest emission angle), while the blue footprints correspond to the VLA resolution. (Note: in 2016 December the VLA was in the most extended A configuration.) Inset (a) is a zoom-in for the indicated region and shows all 60 individual observations colorized by their beam-convolved emission angle that are centered around the equator. These 60 observations are the basis for fitting the nadir brightness temperature and the limb darkening at 0°. Inset (b) is a zoom-in to illustrate how the emission angle varies across the beam, where the red central circle corresponds to the boresight center and the black circle indicates the position of the spacecraft. The dashed ellipses correspond to the beams’ extent projected onto the planet, with the contours showing how the emission angle varies across the beam. The final beam-convolved emission angle is 59.°2 when integrating over the 2.5θ HPBW contour that corresponds to 99% of the emission.
Paper: Ammonia Abundance Derived from Juno MWR and VLA Observations of Jupiter
Authors: Chris Moeckel, Imke de Pater, David DeBoer
Figure caption: Brightness temperature at 12° north latitude for the six different channels, plotted against the beam-convolved emission angle. The filled circles correspond to the observations considered in the fit, and the open circles correspond to the observations that were removed. Open circles inward of the cutoff are observations with high susceptibility to synchrotron radiation (spatial filter—see Section 2.3), while open circles outward of the cutoff are measurements where part of the beam is off the planet. The cutoff is determined such that all beam elements interior to 2.5 HPBW are on the planet. The impact of the synchrotron radiation leaking in through the sidelobes can be seen in Channels 1 and 2, where the brightness temperature increases beyond 60°, while the drop in brightness temperature at higher frequencies is due to the cold sky leaking in. The solid line with shading corresponds to the fitted model according to Equation (3) and three uncertainty fidelities (n = 1, 3, 10). The intersect at an emission angle of zero is the nadir brightness temperature, and the limb-darkening parameter defines the shape of the curve.
Paper: Ammonia Abundance Derived from Juno MWR and VLA Observations of Jupiter
Authors: Chris Moeckel, Imke de Pater, David DeBoer
Figure caption: Verification of the fit uncertainty derived from the weighted least-squares approach (see Equation (5)) by comparing the results to an MCMC method using the same underlying model to fit the data (see Equation (3)). The solid black lines correspond to the 16%, 50%, and 84% quantiles, while the dotted red lines indicate the errors retrieved from the weighted least-squares approach. The best-fit quantities for both methods agree well within their uncertainties. The largest difference is found in the uncertainty of the fitted limb-darkening parameter, where the weighted least-squares errors are smaller than the MCMC results.
Paper: Ammonia Abundance Derived from Juno MWR and VLA Observations of Jupiter
Authors: Chris Moeckel, Imke de Pater, David DeBoer
Figure caption: Ammonia distribution based on the combined Juno and VLA observations. The top panel shows the distribution based on the thermochemical equilibrium model, the middle panel is based on the stochastic model, and the bottom panel shows the effect of clouds on the radio spectrum. (The data used to create this figure are available.)
Paper: Ammonia Abundance Derived from Juno MWR and VLA Observations of Jupiter
Authors: Chris Moeckel, Imke de Pater, David DeBoer
Figure caption: Retrieved atmospheric parameters as a function of latitude (with the individual zones and belts denoted by the vertical dashed lines) and for various combinations of uncertainty (different colors and line styles). Left: (a) relative humidity of ammonia gas; (b) cloud formation efficiency; (c) the reduced χ 2 value for the retrieved atmosphere. Right: (d) bottom pressure of mixing region; (e) pressure range over which mixing occurs; (f) absolute change in ammonia abundance in the mixing region. (The data used to create this figure are available.)
Paper: Ammonia Abundance Derived from Juno MWR and VLA Observations of Jupiter
Authors: Chris Moeckel, Imke de Pater, David DeBoer
Figure caption: Effect of regularization constraint on the ammonia abundance profile in the EZ when fitting to the combined VLA and Juno data. The yellow lines show the results of various different retrievals (and are the same as in Figure 10), while the horizontal lines are the pressure nodes between which the gradient can vary. The three different panels show the effect of the vertical regularization as described in Section 3.2, where the shading corresponds to the 0.16 and 0.84 quantiles from the MCMC runs. The left panel has the regularization turned off (λ = 0 in Equation (20)), with a mild regularization constraint in the middle and a moderate constraint on the right. While the regularization constraint changes how smooth the profile is with altitude, it changes very little about the overall structure.
Paper: Ammonia Abundance Derived from Juno MWR and VLA Observations of Jupiter
Authors: Chris Moeckel, Imke de Pater, David DeBoer
Figure caption: Uncertainties for and correlations between the retrieved parameters in the EZ.
Paper: Ammonia Abundance Derived from Juno MWR and VLA Observations of Jupiter
Authors: Chris Moeckel, Imke de Pater, David DeBoer
Figure caption: Same as Figure 18, but for the NEB.
Paper: Ammonia Abundance Derived from Juno MWR and VLA Observations of Jupiter
Authors: Chris Moeckel, Imke de Pater, David DeBoer
Figure caption: Same as Figure 11, but for the PJ1 observations. The MWR brightness temperature observations are given by the solid lines, and their uncertainty is given by the shaded region. The red and blue solid lines are the brightness temperatures for the abundances as plotted in Figure 13. The dashed–dotted line corresponds to the forward-modeling results based on the ammonia distribution as published by Li et al. (2017). We note that while the two models of the structure of the atmosphere differ, both models produce similar fits to the observation.
Paper: Ammonia Abundance Derived from Juno MWR and VLA Observations of Jupiter
Authors: Chris Moeckel, Imke de Pater, David DeBoer
Figure caption: Slices through the atmosphere at the same locations as indicated in Figure 9. We plot additionally here 10 draws from the MCMC results to show the spread in solutions that are consistent with the observations. The left panel shows the solutions for our perturbed thermochemical equilibrium model, while the right panel shows the results for our stochastic model. We can see that the overall structure is reproduced well by the MCMC draws, indicating the confidence in the retrieved solutions.
Paper: Ammonia Abundance Derived from Juno MWR and VLA Observations of Jupiter
Authors: Chris Moeckel, Imke de Pater, David DeBoer
Figure caption: Comparison of ammonia abundance map for PJ1, based on the preliminary retrieval (Li et al. 2017) and the best-fit model in this paper. The overall structure looks very similar, with the biggest difference that we do not find evidence for the ammonia inversion centered around 6 bars in the earlier retrieval as obtained by Li et al. (2017). (The data used to create this figure are available.)
Paper: Ammonia Abundance Derived from Juno MWR and VLA Observations of Jupiter
Authors: Chris Moeckel, Imke de Pater, David DeBoer
Figure caption: Same as Figure 14, but for the limb-darkening coefficient.
Paper: Probing Ganymede’s Atmosphere with HST Lyα Images in Transit of Jupiter
Authors: Lorenz Roth, Gregorio Marchesini, Tracy M. Becker, H. Jens Hoeijmakers, Philippa M. Molyneux, Kurt D. Retherford, Joachim Saur, Shane R. Carberry Mogan, Jamey R. Szalay
Figure caption: (a) Complete model image for exposure 2 (oe9z04010) after convolution with the PSF. The 4.3 kR from geocorona and IPM are not included, and the total intensity is therefore lower than in the observations shown in Figure 2. (b, c) Radial brightness profiles for 3-pixel-wide bins (annuli) around the disk center derived from the observation image (red) with error range (gray), from the model images without corona (dashed) and with attenuation by the best-fit corona (solid black). The fitted background (without the disk of Ganymede) is shown by the dashed–dotted line. Data points outside the disk (right of the dotted line for the disk edge) are used for fitting the corona density.
Paper: Probing Ganymede’s Atmosphere with HST Lyα Images in Transit of Jupiter
Authors: Lorenz Roth, Gregorio Marchesini, Tracy M. Becker, H. Jens Hoeijmakers, Philippa M. Molyneux, Kurt D. Retherford, Joachim Saur, Shane R. Carberry Mogan, Jamey R. Szalay
Figure caption: Top: full STIS spectral images over the full 25″ × 25″ detector with the dispersed signal from the 6″ × 6″ aperture box centered near y pixel = 350. The horizontal axis contains both spatial and spectral information. Counts above and below the 6″ (or ∼240-pixel) high trace are instrumental only. Photons in the bright box at left are interpreted to be all Lyα emissions, thus representing a monochromatic image. Bottom: zoom-in of the Lyα image with units converted to kR and centered on Ganymede’s disk. The direction of Ganymede’s north pole (NP) is shown by the black arrow.
Paper: Probing Ganymede’s Atmosphere with HST Lyα Images in Transit of Jupiter
Authors: Lorenz Roth, Gregorio Marchesini, Tracy M. Becker, H. Jens Hoeijmakers, Philippa M. Molyneux, Kurt D. Retherford, Joachim Saur, Shane R. Carberry Mogan, Jamey R. Szalay
Figure caption: Observing geometry of Ganymede and Jupiter during the two exposures. The Jovian longitudes and latitudes, the black moon, and the black 6″ × 6″ STIS aperture (black frame) depict the geometry at the midpoint of each exposure. The positions of Ganymede and the aperture on the disk at the exposure start and end times are shown with the gray frames, with the arrow indicating the moon movement/tracking. The equator and some meridian are labeled for orientation.
Paper: Probing Ganymede’s Atmosphere with HST Lyα Images in Transit of Jupiter
Authors: Lorenz Roth, Gregorio Marchesini, Tracy M. Becker, H. Jens Hoeijmakers, Philippa M. Molyneux, Kurt D. Retherford, Joachim Saur, Shane R. Carberry Mogan, Jamey R. Szalay
Figure caption: (a) Maxwellian profile of the Lyα scattering cross section for an assumed temperature of 1000 K in Ganymede’s H corona. (b) Transmission of the corona assuming a column density of N H = 5 × 1012 cm−2. (c) Assumed Maxwellian Lyα profile for the Jovian dayglow (FWHM = 0.1 Å), before (solid) and after (dashed) attenuation by the Ganymede H corona, using the transmission in panel (b).
Paper: Pluto’s Surface Mapping Using Unsupervised Learning from Near-infrared Observations of LEISA/Ralph
Authors: A. Emran, C. M. Dalle Ore, C. J. Ahrens, M. K. H. Khan, V. F. Chevrier, D. P. Cruikshank
Figure caption: Images of two LEISA cubes at a band at 2 μm and mosaics covering the entire Pluto disk, adopted from Schmitt et al. (2017). The image cubes are projected to a common orthographic viewing geometry appropriate to mid-time between scenes. The red line represents the overlapping line where the image cubes were cut and joined together side by side.
Paper: Pluto’s Surface Mapping Using Unsupervised Learning from Near-infrared Observations of LEISA/Ralph
Authors: A. Emran, C. M. Dalle Ore, C. J. Ahrens, M. K. H. Khan, V. F. Chevrier, D. P. Cruikshank
Figure caption: The first nine PCs from LEISA REFF data cubes. Up to four PC axes, the disks show substantial surface information (covering 92.93% total variance of the data). The PC 5 and onward components show “noisy” data; therefore, PC 5 and higher were not considered in the analysis. Note: the assignment of “noisy” data was accomplished subjectively by visually analyzing whether PC axes can represent the underlying geomorphology of Pluto.
Paper: Pluto’s Surface Mapping Using Unsupervised Learning from Near-infrared Observations of LEISA/Ralph
Authors: A. Emran, C. M. Dalle Ore, C. J. Ahrens, M. K. H. Khan, V. F. Chevrier, D. P. Cruikshank
Figure caption: The histogram of the probabilities extracted from the pixels corresponding to each post-class of the C3 and C6 units. The plot shows that the surface units are dominated by pixels with higher probability values (close to 1)—indicating the dominance of mostly pure pixels at each unit.
Paper: Pluto’s Surface Mapping Using Unsupervised Learning from Near-infrared Observations of LEISA/Ralph
Authors: A. Emran, C. M. Dalle Ore, C. J. Ahrens, M. K. H. Khan, V. F. Chevrier, D. P. Cruikshank
Figure caption: Observed and predicted density plot for different numbers of clusters using the PC-GMM algorithm. The observed data were the first four PC axes used as the input data for the GMM, while the predicted data were simulated from the resulting GMM model parameter. The density for both observed and predicted data was calculated from multivariate kernel density estimates. The coefficient of the determination (R  2 ) for linear fit between the observed and predicted density for the eight-GC solution is 0.96—suggesting a good fit of the model.
Paper: Pluto’s Surface Mapping Using Unsupervised Learning from Near-infrared Observations of LEISA/Ralph
Authors: A. Emran, C. M. Dalle Ore, C. J. Ahrens, M. K. H. Khan, V. F. Chevrier, D. P. Cruikshank
Figure caption: The mean (red line) ±1σ standard deviation (gray shade) I/F spectra of each subunit for #6 (top row) and #3 (bottom row). The standard deviation of the I/F spectra shows varying degrees of closeness to the mean spectra at different wavelengths.
Paper: Pluto’s Surface Mapping Using Unsupervised Learning from Near-infrared Observations of LEISA/Ralph
Authors: A. Emran, C. M. Dalle Ore, C. J. Ahrens, M. K. H. Khan, V. F. Chevrier, D. P. Cruikshank
Figure caption: The boxplot of the probabilities extracted from the pixels of corresponding classes (GCs). The median is shown by the line that divides the box into two parts, while the left and right lines of the box represent the lower quartile (25%) and upper quartile (75%), respectively. The plot shows that most of the surface units are dominated by pixels with higher probability values (close to 1)—indicating the dominance of mostly pure pixels at each unit.
Paper: Pluto’s Surface Mapping Using Unsupervised Learning from Near-infrared Observations of LEISA/Ralph
Authors: A. Emran, C. M. Dalle Ore, C. J. Ahrens, M. K. H. Khan, V. F. Chevrier, D. P. Cruikshank
Figure caption: Scaled AIC and BIC values at the different number of clusters. Both AIC and BIC show a similar trend as a function of the number of clusters. The plot shows the first local minima BIC (and AIC) value to be 8 and therefore was considered the optimal number of clusters in this study.
Paper: Pluto’s Surface Mapping Using Unsupervised Learning from Near-infrared Observations of LEISA/Ralph
Authors: A. Emran, C. M. Dalle Ore, C. J. Ahrens, M. K. H. Khan, V. F. Chevrier, D. P. Cruikshank
Figure caption: Pluto nomenclature—the map showing the names of major provinces, geologic units, and features on Pluto. Credit: NASA/Johns Hopkins University Applied Physics Laboratory (JHU-APL)/ Southwest Research Institute (SwRI).
Paper: Ammonia Abundance Derived from Juno MWR and VLA Observations of Jupiter
Authors: Chris Moeckel, Imke de Pater, David DeBoer
Figure caption: Ammonia abundance map based on Juno’s orbit average brightness temperature and limb darkening with a 3σ fit uncertainty level. The map shows familiar features such as the column of enriched ammonia in the EZ and the depletion of ammonia in the NEB, with smaller-scale features at the midlatitudes. Plotted on the same color scale is the original VLA map based on observations between 2013 and 2014 (de Pater et al. 2016, 2019b). The overall structure is very similar between the two maps, with the Juno data allowing us to resolve more structure at depth and the transitions in the atmosphere owing to its low-frequency observations. The dotted lines indicate the latitude at which the zonal wind speed peaks, and with that the difference between the zones and belts. The vertical gray lines correspond to locations of the atmospheric cross sections in Figure 10. (The data used to create this figure are available.)
Paper: Ammonia Abundance Derived from Juno MWR and VLA Observations of Jupiter
Authors: Chris Moeckel, Imke de Pater, David DeBoer
Figure caption: Weighting functions visualize the pressure levels from which radiation is received. We plot the functions for both end members on the planet, a highly enriched part in the atmosphere (solid line corresponding to the EZ), and a highly depleted part (dashed line corresponding to the NEB) showing the depth and range from which we receive the signal. The abundance profiles for which we compute the weighting functions are taken from our fit to the orbit-averaged MWR data (see Figure 9). The left panel shows the relevant functions for the Juno spacecraft, and the right panel shows the function for the VLA.