adsabs/AstroCLIMB huggingface dataset:
Paper: Dielectric and Polarimetric Radar Properties of Suspected Surficial Ice Exposures at the Lunar South Pole
Authors: Tanish Himani, Kevin Lewis, G. Wesley Patterson, Edgard G. Rivera-Valentín, Shashwat Shukla, Nicholas Dutton
Figure caption: Controlled polar mosaic of real dielectric constant (ϵr) for the lunar south pole from 70° to 90° S. Lines of latitude are every 2° and lines of longitude every 30°. PSRs within craters of size 5–25 km are shaded in black. The bottom shows a zoomed version with permanently shadowed areas shaded in white. Figure modified from S. Shukla et al. (2024) to include PSRs.
Paper: In situ, Surface-deployed Distributed Instruments for Planetary Science: Scientific Opportunities and Technology Feasibility
Authors: Federico Rossi, Robert C. Anderson, Saptarshi Bandyopadhyay, Erik Brandon, Ashish Goel, Joshua Vander Hook, Michael Mischna, Michaela Villarreal, Mark Wronkiewicz
Figure caption: Six years of atmospheric opacity data illustrating the seasonality of atmospheric “dustiness” on Mars. Opacity is noticeably higher in the second half of the year, which corresponds to southern spring and summer, when Mars is closest to the Sun. Data obtained by the Mars Exploration Rover “Opportunity,” from the PDS Planetary Atmospheres Node at https://atmos.nmsu.edu/data_and_services/atmospheres_data/MARS/MER/spirit/atm_opacity.html
Paper: In situ, Surface-deployed Distributed Instruments for Planetary Science: Scientific Opportunities and Technology Feasibility
Authors: Federico Rossi, Robert C. Anderson, Saptarshi Bandyopadhyay, Erik Brandon, Ashish Goel, Joshua Vander Hook, Michael Mischna, Michaela Villarreal, Mark Wronkiewicz
Figure caption: A global image of Mars showing the presence of water ice clouds in the lower latitudes and near large topographic features. This collection of clouds is referred to as the ’aphelion cloud belt’ and occurs primarily during the late northern spring/early summer, when Mars is farthest from the Sun. Planetary Data System image PIA02653. Image credit: NASA/JPL-Caltech-MSSS
Paper: In situ, Surface-deployed Distributed Instruments for Planetary Science: Scientific Opportunities and Technology Feasibility
Authors: Federico Rossi, Robert C. Anderson, Saptarshi Bandyopadhyay, Erik Brandon, Ashish Goel, Joshua Vander Hook, Michael Mischna, Michaela Villarreal, Mark Wronkiewicz
Figure caption: The annual pressure cycle as measured by Viking Landers 1 and 2 shows a 30% variation between peak and trough. Offset of the two curves is due to different elevation of each lander.
Paper: In situ, Surface-deployed Distributed Instruments for Planetary Science: Scientific Opportunities and Technology Feasibility
Authors: Federico Rossi, Robert C. Anderson, Saptarshi Bandyopadhyay, Erik Brandon, Ashish Goel, Joshua Vander Hook, Michael Mischna, Michaela Villarreal, Mark Wronkiewicz
Figure caption: Illustration of Mars’ thermal tide, responsible for large, daily variations in pressure at the Martian surface. Heating by the Sun drives global circulation of air toward the night side of the planet, resulting in a diurnal cycle of surface pressure as the planet rotates beneath the Sun. The timing and structure of the thermal tide are dependent upon such elements as atmospheric dust and topography. Planetary Data System image PIA16478. Image credit: NASA/JPL-Caltech/Ashima Research/SwRI.
Paper: In situ, Surface-deployed Distributed Instruments for Planetary Science: Scientific Opportunities and Technology Feasibility
Authors: Federico Rossi, Robert C. Anderson, Saptarshi Bandyopadhyay, Erik Brandon, Ashish Goel, Joshua Vander Hook, Michael Mischna, Michaela Villarreal, Mark Wronkiewicz
Figure caption: NASA JPL’s SensorWeb distributed instrument concept. Left: Functional SensorWeb pod. Right: Proposed planetary surface deployment schemes for sensor web. From K. A. Delin & S. P. Jackson (2000).
Paper: In situ, Surface-deployed Distributed Instruments for Planetary Science: Scientific Opportunities and Technology Feasibility
Authors: Federico Rossi, Robert C. Anderson, Saptarshi Bandyopadhyay, Erik Brandon, Ashish Goel, Joshua Vander Hook, Michael Mischna, Michaela Villarreal, Mark Wronkiewicz
Figure caption: Volcano Sensor Web architecture illustrating how retasking happens upon triggering based on event detection (S. Chien et al. 2005).
Paper: In situ, Surface-deployed Distributed Instruments for Planetary Science: Scientific Opportunities and Technology Feasibility
Authors: Federico Rossi, Robert C. Anderson, Saptarshi Bandyopadhyay, Erik Brandon, Ashish Goel, Joshua Vander Hook, Michael Mischna, Michaela Villarreal, Mark Wronkiewicz
Figure caption: Scheme of the proposed Venera-D mission in 2011 (V. Vorontsov et al. 2011, their Figure 4).
Paper: In situ, Surface-deployed Distributed Instruments for Planetary Science: Scientific Opportunities and Technology Feasibility
Authors: Federico Rossi, Robert C. Anderson, Saptarshi Bandyopadhyay, Erik Brandon, Ashish Goel, Joshua Vander Hook, Michael Mischna, Michaela Villarreal, Mark Wronkiewicz
Figure caption: Proposed design reference missions for exploration of Venus (left) and Mars (right; P. Beauchamp et al. 2018; E. Lyness et al. 2019).
Paper: In situ, Surface-deployed Distributed Instruments for Planetary Science: Scientific Opportunities and Technology Feasibility
Authors: Federico Rossi, Robert C. Anderson, Saptarshi Bandyopadhyay, Erik Brandon, Ashish Goel, Joshua Vander Hook, Michael Mischna, Michaela Villarreal, Mark Wronkiewicz
Figure caption: Lunar Geophysical Network (LGN) mission overview (left) and science instruments (right). From C. Neal et al. (2020), Figures 7 and 3.
Paper: In situ, Surface-deployed Distributed Instruments for Planetary Science: Scientific Opportunities and Technology Feasibility
Authors: Federico Rossi, Robert C. Anderson, Saptarshi Bandyopadhyay, Erik Brandon, Ashish Goel, Joshua Vander Hook, Michael Mischna, Michaela Villarreal, Mark Wronkiewicz
Figure caption: Representative primary batteries at 0∘ C. From F. C. Krause et al. (2018).
Paper: In situ, Surface-deployed Distributed Instruments for Planetary Science: Scientific Opportunities and Technology Feasibility
Authors: Federico Rossi, Robert C. Anderson, Saptarshi Bandyopadhyay, Erik Brandon, Ashish Goel, Joshua Vander Hook, Michael Mischna, Michaela Villarreal, Mark Wronkiewicz
Figure caption: Range of radioisotope thermoelectric generators, producing power from about 40 mW to >100 W. From G. R. Schmidt et al. (2005).
Paper: In situ, Surface-deployed Distributed Instruments for Planetary Science: Scientific Opportunities and Technology Feasibility
Authors: Federico Rossi, Robert C. Anderson, Saptarshi Bandyopadhyay, Erik Brandon, Ashish Goel, Joshua Vander Hook, Michael Mischna, Michaela Villarreal, Mark Wronkiewicz
Figure caption: Maturity of enabling technologies for each of the science concepts presented in Section 3.
Paper: In situ, Surface-deployed Distributed Instruments for Planetary Science: Scientific Opportunities and Technology Feasibility
Authors: Federico Rossi, Robert C. Anderson, Saptarshi Bandyopadhyay, Erik Brandon, Ashish Goel, Joshua Vander Hook, Michael Mischna, Michaela Villarreal, Mark Wronkiewicz
Figure caption: Simplified distributed sensor network power system block diagram.
Paper: In situ, Surface-deployed Distributed Instruments for Planetary Science: Scientific Opportunities and Technology Feasibility
Authors: Federico Rossi, Robert C. Anderson, Saptarshi Bandyopadhyay, Erik Brandon, Ashish Goel, Joshua Vander Hook, Michael Mischna, Michaela Villarreal, Mark Wronkiewicz
Figure caption: Missions concepts enabled by small RTG power sources. From G. R. Schmidt et al. (2005).
Paper: The Interplay of Surface Composition, Mineralogy, and Physical Conditions That Affect the Surface Release Processes and Particle Environment of Mercury
Authors: Peter Wurz, Noah Jäggi, André Galli, Audrey Vorburger, Deborah Domingue, Paul S. Szabo, Johannes Benkhoff, Océane Barraud, Daniel Wolf Savin
Figure caption: Simplified TAS diagram of volcanic rocks with the compositions (dots) of the geochemical terranes after T. J. McCoy et al. (2018). The uncertainties (lines) are based on the possibility that Na and K within Mercury’s regolith are not bound within grains and do not represent the rock chemistry. Boninites describes any rock that has SiO2 > 52 wt%, MgO > 8 wt%, and TiO2 < 0.5 wt%.
Paper: The Interplay of Surface Composition, Mineralogy, and Physical Conditions That Affect the Surface Release Processes and Particle Environment of Mercury
Authors: Peter Wurz, Noah Jäggi, André Galli, Audrey Vorburger, Deborah Domingue, Paul S. Szabo, Johannes Benkhoff, Océane Barraud, Daniel Wolf Savin
Figure caption: Typical reflectance spectra of some major and minor spectral units measured by MASCS/VIRS. Localization of the spectra used for the hollows, LRM, and NSP are detailed in O. Barraud et al. (2020). Spectra used for the Nathair Facula and pyroclastic deposits are from O. Barraud et al. (2021). Hollows exhibit a strong concave curvature between 300 and 600 nm (shaded region) compared to other spectral units.
Paper: The Interplay of Surface Composition, Mineralogy, and Physical Conditions That Affect the Surface Release Processes and Particle Environment of Mercury
Authors: Peter Wurz, Noah Jäggi, André Galli, Audrey Vorburger, Deborah Domingue, Paul S. Szabo, Johannes Benkhoff, Océane Barraud, Daniel Wolf Savin
Figure caption: Calculation of density profiles in Mercury’s exosphere for sputtered CaO (solid line) and its fragments Ca (dashed line) and O (dashed–dotted line) resulting from photodissociation of sputtered CaO along its trajectory in the exosphere.
Paper: Radiance Contrasts at Possible Lunar Water Ice Exposures Seen by ShadowCam
Authors: Jordan Ando, Shuai Li, Mark Robinson, Robert Wagner
Figure caption: Normalized histogram of ShadowCam radiance at southern polar PSR pixels underlying M3 PIDs, compared to the histogram of NID southern polar PSR ShadowCam pixels. This is performed for M3 pixels of (a) the original resolution (280 m pixel−1) and (b) the expanded size of ice detections (1000 m pixel−1). The numbers in the legend indicate the total number of 60 m ShadowCam maximum mosaic pixels in each population. Panel (c) compares the histograms of all pixels in southern polar PSRs that have at least one M3 water ice detection (cyan) and those that have no water ice detections (gray). All histograms are binned using 100 evenly spaced bins from 0 to 0.6.
Paper: Radiance Contrasts at Possible Lunar Water Ice Exposures Seen by ShadowCam
Authors: Jordan Ando, Shuai Li, Mark Robinson, Robert Wagner
Figure caption: Deviations of radiance at M3 ice detections from their neighboring pixels for southern polar PSRs, computed from comparisons between the (a) median and (b) maximum radiance values of ShadowCam pixels underlying the M3 ice detections (280 m pixel−1) and their neighboring M3 pixel-sized regions. The orange shaded region represents the bootstrapped 3σ range of mean percent shifts determined from each PSR. Error bars on each PSR’s mean ice detection shifts represent 1σ. The expanded yellow shaded region includes the mean standard deviation from a PSR’s set of random shifts, equivalent to the mean shift in orange with the error bars on each point.
Paper: Radiance Contrasts at Possible Lunar Water Ice Exposures Seen by ShadowCam
Authors: Jordan Ando, Shuai Li, Mark Robinson, Robert Wagner
Figure caption: Examples of anomalous deviations in median radiance between an M3 PID (green) and its neighboring M3-sized regions (blue). Panel (a) is in Shoemaker, and panel (b) is in Faustini. Blue and green points indicate the median pixel within each region. Panel (c) shows an anomalously high radiance patch on a crater wall in Shoemaker. Panel (d) shows the same spot as in the 20 m pixel−1 slope map (M. K. Barker et al. 2023).
Paper: Radiance Contrasts at Possible Lunar Water Ice Exposures Seen by ShadowCam
Authors: Jordan Ando, Shuai Li, Mark Robinson, Robert Wagner
Figure caption: Normalized histograms of radiance of ShadowCam PID pixels (purple) that underlie the M3 ice detections (280 m pixel−1) compared to NID pixels (black) for 14 southern polar PSRs.
Paper: Radiance Contrasts at Possible Lunar Water Ice Exposures Seen by ShadowCam
Authors: Jordan Ando, Shuai Li, Mark Robinson, Robert Wagner
Figure caption: Deviations of radiance at M3 ice detections from their neighboring pixels for southern polar PSRs, computed from comparisons between the (a) median and (b) maximum radiance values of ShadowCam pixels underlying the expanded M3 ice detections (1000 m pixel−1) and their neighboring M3 pixel-sized regions. The orange shaded region represents the bootstrapped 3σ range of mean percent shifts determined from each PSR. Error bars on each PSR’s mean ice detection shifts represent 1σ. The expanded yellow shaded region includes the mean standard deviation from a PSR’s set of random shifts, equivalent to the mean shift in orange with the error bars on each point.
Paper: Radiance Contrasts at Possible Lunar Water Ice Exposures Seen by ShadowCam
Authors: Jordan Ando, Shuai Li, Mark Robinson, Robert Wagner
Figure caption: These maps are used in the process of determining water ice iso-proportion contours using M3 detections. Panel (a) depicts all M3 water ice exposures in yellow, over the ShadowCam maximum radiance mosaic of the lunar south pole. M3 detections are not true to scale, for visibility. These exposures are used to generate the kernel density estimate heatmap in panel (b). From this, we calculate the isoprobability contours in panel (c) excluding 50% (blue) and 5% (red) of the probability mass. Areas within the blue contour are considered to have a high probability of water ice presence, while areas excluded from the red contour have a low or zero probability of water ice.
Paper: Radiance Contrasts at Possible Lunar Water Ice Exposures Seen by ShadowCam
Authors: Jordan Ando, Shuai Li, Mark Robinson, Robert Wagner
Figure caption: Normalized histograms of radiance of ShadowCam max radiance mosaics at high ice probability (purple) and low ice probability (black) regions in PSRs near the lunar south pole. Specifically, the purple histogram contains all ShadowCam pixels contained in a 50% isoprobability contour, indicating a high density of M3 ice detections. The black histogram contains all ShadowCam pixels on the exterior of a 5% isoprobability contour, meaning that all pixels are far from M3 detections. Both histograms have 100 bins.
Paper: Radiance Contrasts at Possible Lunar Water Ice Exposures Seen by ShadowCam
Authors: Jordan Ando, Shuai Li, Mark Robinson, Robert Wagner
Figure caption: Maps of the large southern polar PSRs examined in this study. Each PSR is clipped from the ShadowCam max radiance mosaics and individually contrast stretched for increased feature visibility. Panel (a) depicts all PSRs studied, while panel (b) provides a zoomed-in view of the larger PSRs nearer to the south pole.
Paper: Radiance Contrasts at Possible Lunar Water Ice Exposures Seen by ShadowCam
Authors: Jordan Ando, Shuai Li, Mark Robinson, Robert Wagner
Figure caption: Normalized histograms of radiance of ShadowCam pixels (purple) that underlie the M3 PIDs (1000 m pixel−1) compared to NID pixels (black) for 14 southern polar PSRs.
Paper: Supercooling, Glass Formation, and Mineral Assemblages upon Freezing of Salty Ice Grains from Enceladus’s Ocean
Authors: Fabian Klenner, Lucas M. Fifer, Baptiste Journaux, Ardith D. Bravenec, Allan M. M. Leal, Steven D. Vance, David C. Catling
Figure caption: Precipitating mineral assemblages (top), pH evolution (center), and residual liquid water (bottom) upon freezing of solution II at 3.1 bar. Left panels show PHREEQC simulations; right panels show Reaktoro simulations.
Paper: Supercooling, Glass Formation, and Mineral Assemblages upon Freezing of Salty Ice Grains from Enceladus’s Ocean
Authors: Fabian Klenner, Lucas M. Fifer, Baptiste Journaux, Ardith D. Bravenec, Allan M. M. Leal, Steven D. Vance, David C. Catling
Figure caption: Thermogram of solution II, flash frozen (1227 K minute−1). Melting of a salty, yet unidentified compound is observed in the heating ramp at ∼139 K.
Paper: Supercooling, Glass Formation, and Mineral Assemblages upon Freezing of Salty Ice Grains from Enceladus’s Ocean
Authors: Fabian Klenner, Lucas M. Fifer, Baptiste Journaux, Ardith D. Bravenec, Allan M. M. Leal, Steven D. Vance, David C. Catling
Figure caption: Precipitation of salts from solution I and solution II at 6.1 mbar and 20 bar, respectively, simulated with PHREEQC. For both solutions, the precipitation temperatures of the individual salts vary by less than 0.3 K in the investigated pressure range.
Paper: Supercooling, Glass Formation, and Mineral Assemblages upon Freezing of Salty Ice Grains from Enceladus’s Ocean
Authors: Fabian Klenner, Lucas M. Fifer, Baptiste Journaux, Ardith D. Bravenec, Allan M. M. Leal, Steven D. Vance, David C. Catling
Figure caption: Saturation indices of nitrogen-bearing species in solution I and solution II during freezing at 3.1 bar, simulated with PHREEQC. A saturation index of 0 would mean that the respective species is saturated in solution.
Paper: Supercooling, Glass Formation, and Mineral Assemblages upon Freezing of Salty Ice Grains from Enceladus’s Ocean
Authors: Fabian Klenner, Lucas M. Fifer, Baptiste Journaux, Ardith D. Bravenec, Allan M. M. Leal, Steven D. Vance, David C. Catling
Figure caption: Mineral assemblages precipitating at 3.1 bar from NaCl-dominated water reservoirs at low pH, simulated with PHREEQC. The pH was adjusted by adding 80 mM chlorine ions to solution I and 780 mM chlorine ions to solution II (Table A4). In solution II, disodium phosphate dodecahydrate (red) redissolves when Na(H2PO4):2H2O (olive) precipitates.
Paper: Supercooling, Glass Formation, and Mineral Assemblages upon Freezing of Salty Ice Grains from Enceladus’s Ocean
Authors: Fabian Klenner, Lucas M. Fifer, Baptiste Journaux, Ardith D. Bravenec, Allan M. M. Leal, Steven D. Vance, David C. Catling
Figure caption: NH4+ and NH3 concentrations in solution I and solution II during freezing at 3.1 bar, simulated with PHREEQC.
Paper: Probing Lateral Density Variations in the Crust from Gravity and Topography: Applications to the Moon and Mars
Authors: Sander Goossens, Terence J. Sabaka
Figure caption: Density of the Moon’s crust determined from effective density and localization (top) and from the matrix formulation (bottom). Both maps are from a degree and order 5 spherical harmonic expansion of density (see text). The map is in a Mollweide projection centered on the nearside. The map is shaded by topography, and we indicate the locations of the mare areas.
Paper: Probing Lateral Density Variations in the Crust from Gravity and Topography: Applications to the Moon and Mars
Authors: Sander Goossens, Terence J. Sabaka
Figure caption: Power per degree for several spherical harmonic expansions of density of different maximum degree and order, using either localization or the matrix mapping (left), and correlations between these models and the model obtained through localization (right).
Paper: NEOWISE Data Processing and Color Corrections for Near-Earth Asteroid Observations
Authors: Samuel A. Myers, Ellen S. Howell, Yanga R. Fernández, Sean E. Marshall, Christopher Magri, Ronald J. Vervack, Mary L. Hinkle
Figure caption: Figure of SpeX effective blackbody temperature fits and corresponding calculated theoretical blackbody temperatures. Each vertical line of symbols is one SpeX observation, sorted by heliocentric distance. The cross is the effective blackbody temperature found by fitting blackbody curves to the SpeX data and the box is the theoretical blackbody temperature calculated using Equation (2). The difference between the two never exceeds 30 K. For rH < 1.25 au, the direction of offset is random; for rH > 1.25 au, the calculated theoretical value is always less than the fitted effective value. All observations shown here were chosen solely based on their observing geometries, which closely match trends in NEOWISE observation geometries.
Paper: NEOWISE Data Processing and Color Corrections for Near-Earth Asteroid Observations
Authors: Samuel A. Myers, Ellen S. Howell, Yanga R. Fernández, Sean E. Marshall, Christopher Magri, Ronald J. Vervack, Mary L. Hinkle
Figure caption: Raw NEOWISE-R images. The reticle shows the location from which the automatic pipeline extracts a magnitude value. (A) and (B): UO1 W2 and JM8 W2. Example of a nonspurious detection with the object clearly visible. (C) and (D): JD6 W2. Examples of contamination by a background source. (E) and (F): JD6 W1. Examples of a spurious detection with no object visible. (G): JD6 W2. Example of a streaked image, in this case likely due to a bad column. (H): JM8 W2. Example of a diffraction spike overlapping the primary object. All frames shown here were not flagged by the automatic pipeline.
Paper: NEOWISE Data Processing and Color Corrections for Near-Earth Asteroid Observations
Authors: Samuel A. Myers, Ellen S. Howell, Yanga R. Fernández, Sean E. Marshall, Christopher Magri, Ronald J. Vervack, Mary L. Hinkle
Figure caption: Model results for JM8. The x-axis is thermal inertia in TIU, and the y-axis is visible albedo. Each colored square represents a different model with the color indicating the number of data sets fit by the given model. Note that in some cases the largest number of data sets fit by a model is less than the total number of data sets. Each panel shows model results after a different phase of the data processing.
Paper: NEOWISE Data Processing and Color Corrections for Near-Earth Asteroid Observations
Authors: Samuel A. Myers, Ellen S. Howell, Yanga R. Fernández, Sean E. Marshall, Christopher Magri, Ronald J. Vervack, Mary L. Hinkle
Figure caption: Model results for JD6. The x-axis is thermal inertia in TIU, and the y-axis is visible albedo. Each colored square represents a different model with the color indicating the number of data sets fit by the given model. Note that in some cases the largest number of data sets fit by a model is less than the total number of data sets. This may be due to our simple thermal model being unable to capture aspects of JD6’s elongated shape. Each panel shows model results after a different phase of the data processing.
Paper: NEOWISE Data Processing and Color Corrections for Near-Earth Asteroid Observations
Authors: Samuel A. Myers, Ellen S. Howell, Yanga R. Fernández, Sean E. Marshall, Christopher Magri, Ronald J. Vervack, Mary L. Hinkle
Figure caption: Example of an effective blackbody curve fit to a SpeX data set. The data are an observation of Ivar taken on 2013 September 15. The solid blue line is the effective blackbody curve fit to the SpeX data with a temperature of 257 K. The dashed red line is the theoretical blackbody curve calculated using Equation (2) with a temperature of 236 K. The inputs used are rH = 1.3214 au, p = 0.15, and G = 0.60. The vertical dashed lines show the outer limits of the W1 bandpass filter. The x-axis is wavelength in microns. The y-axis is normalized flux, normalized at 3.4 μm.
Paper: NEOWISE Data Processing and Color Corrections for Near-Earth Asteroid Observations
Authors: Samuel A. Myers, Ellen S. Howell, Yanga R. Fernández, Sean E. Marshall, Christopher Magri, Ronald J. Vervack, Mary L. Hinkle
Figure caption: Model results for UO1. The x-axis is thermal inertia in TIU, and the y-axis is visible albedo. Each colored square represents a different model with the color indicating the number of data sets fit by the given model. Note that in some cases the largest number of data sets fit by a model is less than the total number of data sets. Each panel shows model results after a different phase of the data processing.
Paper: NEOWISE Data Processing and Color Corrections for Near-Earth Asteroid Observations
Authors: Samuel A. Myers, Ellen S. Howell, Yanga R. Fernández, Sean E. Marshall, Christopher Magri, Ronald J. Vervack, Mary L. Hinkle
Figure caption: Example simple thermal model fit to NEOWISE-R data. Models that fall within the 1σ uncertainties are considered to fit the data. Here, we show an example of a fully color-corrected NEOWISE-R data set for JD6. The x-axis is wavelength in microns, and the y-axis is absolute flux density.