adsabs/AstroCLIMB huggingface dataset:
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: Differences in the By component of the induced magnetic field of Europa in the EPhiO coordinate system for a spherically symmetric ice shell (black) and an asymmetric ice shell (green). The difference in response only differs by a few nT as seen from the sub-Jovian point on the surface. Figure from M. J. Styczinski & E. M. Harnett (2021).
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: Artist’s illustration of Prandtl-M glider at Mars (left), and SUPERball v2 tensegrity robot at NASA Ames (right). Gliders and tensegrity landers can be deployed during the final phases of EDL, distributing sensors across regions tens of kilometers across. Figures from J. Levine (2015; left) and M. Vespignani et al. (2018; right).
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 typical relay communication architecture. Ground-based assets communicate with an orbiter over UHF links. The orbiter, in turn, communicates to Earth over the X band. From W. D. Horne et al. (1997).
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: ICARUS COTS ultra-low-SWaP transponder (Max Planck Institute 2015). Image credit MPI f. Ornithology/ MaxCine.
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: Independent microlanders and ballistic penetrators. Left: SHIELD lander concept (N. Barba et al. 2020). Right: Deep Space 2 ballistic penetrator (S. Smrekar et al. 1999).
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: Sensor placement assets for sensor deployment on planetary surfaces, with increasing range after deployment.
Paper: JWST Near-infrared Spectroscopy of High-albedo Jupiter Trojans: A New Surface Type in the Trojan Belt
Authors: Michael E. Brown, Ian Wong, Matthew Belyakov
Figure caption: Comparison of the average spectrum of the four high-albedo Jupiter Trojans (black) to previous Trojan observations. The magenta points show the average of the red Trojan population from J. P. Emery et al. (2011), while the cyan points show the average of the less-red population. The JWST spectrum of Orus (red)—a red Jupiter Trojan—shows a weak 3 μm absorption but a strong 3.4 μm signature of organics, while Eurybates (blue)—a less-red Trojan–shows a strong 3 μm absorption and a distinct 4.26 μm signature of CO2 (I. Wong et al. 2024). The high-albedo Jupiter Trojans do not fit into either of these categories. Polymele is the highest-albedo object in the I. Wong et al. (2024) sample and has a spectrum remarkably similar to the other high-albedo Jupiter Trojans.
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: Normalized polar distribution of sputtered ejecta generated by 4 keV He+ ions impinging at 45° and 60° on enstatite. The experimental data (black) with 1 standard deviation error bars are from H. Biber et al. (2022), along with modeling results from SDTrimSP (gray) and fit results (orange) based on the model presented in N. Jäggi et al. (2023).
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: Experimental data (black; errors are 1 standard deviation) for H+ on wollastonite, CaSiO3 (P. S. Szabo et al. 2018), and He+ on enstatite (H. Biber et al. 2022) with TRIM results (dashed red line) and the SDTrimSP model data (blue line; N. Jäggi et al. 2023) in comparison.
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: Comparison for the calculated density, line-of-sight velocity Vxy, and radial velocity Vzw with the observations. Black lines are for the observed data and blue lines for the modeled data. The data for the three observations are from R. M. Killen et al. (2005).
Paper: Methods for Experimental Planetary Geology in Reduced-gravity Parabolic Flight: A Case Study of Researching Impact Crater Ejecta
Authors: Kirby D. Runyon, Daniel D. Durda, Viliam Klein, Constantine C. C. Tsang, Chloe B. Beddingfield, Carolyn M. Ernst, Olivier S. Barnouin, H. Todd Smith
Figure caption: Two examples of GoPro video stills of the ejecta curtain in flight with manual grain-tracking marks. Each frame of video from a given experiment was imported as a discrete layer in Adobe Photoshop, which allowed manual marking of the position of representative grains from frame to frame (i.e., layer to layer). The distances were quantified based on fiducial marks on the bottom of the tray (not pictured). Some uncertainty in these measurements may exist for some grains with trajectories that are not precisely parallel to the tray length. The position of measured grains was labeled on each Photoshop layer in centimeters, and lines show the distances traveled by each grain across the image set. With the known distances and known frame rate (60 frames per second), velocity was easily computed from changes in grain locations (computed from the average displacement per grain) in initial frames (top) and later frames (bottom). Similarly, we measured the size of ejecta curtain footprints (i.e., the ground-projected thickness of the curtain) and the displacement of regolith grains from impinging ejecta. The horizontal gray bands in both images are the foam-covered top of the ejecta catchment tray. (Still frames from experiment and camera view Flight 2, Lunar 6, Proximal.)
Paper: Methods for Experimental Planetary Geology in Reduced-gravity Parabolic Flight: A Case Study of Researching Impact Crater Ejecta
Authors: Kirby D. Runyon, Daniel D. Durda, Viliam Klein, Constantine C. C. Tsang, Chloe B. Beddingfield, Carolyn M. Ernst, Olivier S. Barnouin, H. Todd Smith
Figure caption: Before (top) and after (bottom) of a deposit of yellow ejecta atop previous experiments of different colored ejecta (now serving as regolith). We measured the thickness, in millimeters, of the yellow ejecta in the “after” frame (vertical yellow bars) and the erosion in the regolith (multicolored; measured in cyan bars) from the bottom of the yellow ejecta to the top of the ejecta from the “before” frame. We made depth measurements in opportune areas and not at regular intervals due to the stochastic nature of ejecta deposition. Numbered fiducial markings along the bottom of each frame are in centimeters and are the distance from the catapult, off-screen to the left. Ejecta was emplaced from left to right in this view. We plan to publish more detailed results and interpretations in a future paper. (Still frames from experiment and camera view Flight 2, Lunar 7, Medial.)
Paper: Methods for Experimental Planetary Geology in Reduced-gravity Parabolic Flight: A Case Study of Researching Impact Crater Ejecta
Authors: Kirby D. Runyon, Daniel D. Durda, Viliam Klein, Constantine C. C. Tsang, Chloe B. Beddingfield, Carolyn M. Ernst, Olivier S. Barnouin, H. Todd Smith
Figure caption: Accelerometer placement and axis orientation relative to the aircraft fuselage. The y-axis is pointing into the page, along the body of the aircraft. The view is looking toward the front of the airplane from the rear. Note the tubular foam pipe insulation protecting the corners and edges. Also visible are two of the white neoprene gloves on the camera-facing side. Note the two visible floor straps (red on the left and gray on the right) that can be placed in custom areas to help researchers hold steady during parabolas. Image credit: the authors.
Paper: Methods for Experimental Planetary Geology in Reduced-gravity Parabolic Flight: A Case Study of Researching Impact Crater Ejecta
Authors: Kirby D. Runyon, Daniel D. Durda, Viliam Klein, Constantine C. C. Tsang, Chloe B. Beddingfield, Carolyn M. Ernst, Olivier S. Barnouin, H. Todd Smith
Figure caption: Box and whisker plots showing the preliminary results of the erosive efficiency of ejecta emplacement for gravitational conditions on Earth, Mars/Mercury, and the Moon. The X symbols denote the mean of each data set, while the horizontal lines denote the medians. The boxes denote the interquartile range between the 25th and 75th percentiles of each data set. The data do not seem conclusive as to the effect of gravity on erosive efficiency, though the results could suggest a slight negative correlation between erosive efficiency with gravity given the slight increase in mean and interquartile range as gravity decreases from 1 to 0.38 to 0.17 g.
Paper: Methods for Experimental Planetary Geology in Reduced-gravity Parabolic Flight: A Case Study of Researching Impact Crater Ejecta
Authors: Kirby D. Runyon, Daniel D. Durda, Viliam Klein, Constantine C. C. Tsang, Chloe B. Beddingfield, Carolyn M. Ernst, Olivier S. Barnouin, H. Todd Smith
Figure caption: Box and whisker plots of ejecta particle speeds for experiments conducted at lunar gravity. The horizontal lines denote the median; the X symbols denote the mean; and the box extents denote the interquartile range between the 25th and 75th percentiles. Note the steady increase of velocity with distance from the catapult, as expected.
Paper: Methods for Experimental Planetary Geology in Reduced-gravity Parabolic Flight: A Case Study of Researching Impact Crater Ejecta
Authors: Kirby D. Runyon, Daniel D. Durda, Viliam Klein, Constantine C. C. Tsang, Chloe B. Beddingfield, Carolyn M. Ernst, Olivier S. Barnouin, H. Todd Smith
Figure caption: Processed z-axis representative accelerometer data for the final zero g, lunar g, and Martian g parabolas. Target gravity levels are shown with gray dashed lines. Note that gravity levels are imperfectly maintained over the course of the parabola; this can have implications for experiment design and uncertainty estimation.
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 north polar PSR pixels underlying M3 PIDs, compared to the histogram of NID north 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 radiance mosaic pixels in each population. Panel (c) compares the histograms of all pixels in northern polar PSRs that have at least one M3 water ice detection (light blue) and no water ice detections (gray).
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 the northern 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: Deviations of radiance at M3 ice detections from their neighboring pixels for the northern polar PSRs, computed from comparisons between the (a) median and (b) maximum radiance values of ShadowCam pixels underlying 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: Normalized histograms of radiance for ShadowCam pixels underlying the M3 280 m ice detections (blue) compared to NID pixels (black) for 14 northern polar PSRs. All histograms have 50 bins.
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 radiances of PSR pixels from the ShadowCam maximum radiance mosaic near the lunar north pole. The blue histogram includes all ShadowCam pixels contained in a 50% isoprobability contour, indicating a high density of M3 ice detections. The black histogram is all ShadowCam pixels on the exterior of a 5% isoprobability contour that are far from M3 ice 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: Normalized histograms of radiance for ShadowCam pixels underlying the expanded M3 ice detections at 1000 m pixel−1 (blue) compared to NID pixels (black) for 14 northern polar PSRs. All histograms have 50 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 PSRs near the lunar north pole examined in this study. Each PSR is clipped from the ShadowCam max radiance mosaic. All PSRs are individually contrast stretched and outlined in black for increased feature visibility. PSRs not included in Table 2 are excluded.
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 ShadowCam radiance for high ice probability (blue) and low ice probability pixels (black) in north polar PSRs. Histograms each contain 50 bins.
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 for high ice probability contour masked pixels (purple) and low ice probability masked pixels (black) in southern polar PSRs. Histograms each contain 50 bins.
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: Crustal density variations for the Moon from localization using the GRGM1200B model (top); from an RM-S-constrained model using a degree and order 5 density model to construct the constraint based on pre-GRAIL data, using localization (middle); and for a degree and order 5 density model using the matrix formulation (bottom). We used the gravity degree range l = 70–150. The RM-S model used a constraint factor of λ = 1016 and a density expansion of degree and order 5. The maps have the same projection and properties as in Figure 1.
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 in spherical harmonics of degree and order 12 (top) and 50 (bottom). We show maps based on localization (left) and the matrix method (right). The map for the degree and order 50 density model from the matrix formulation used a damping constraint (see text). The maps have the same projection and properties as in Figure 1.
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: Crustal density variations for Mars based on an RM-S-constrained model with λ = 1020 and a density expansion of degree and order 5 used to construct the constraint. We show densities based on localization (top; see text for details), based on the matrix method (middle), and based on the matrix method with an additional smoothing constraint using D = 109 (bottom). The map is in a Mollweide projection centered on zero longitude, shaded by topography.
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: Crustal density variations for the Moon from an RM-S-constrained model using a degree and order 12 density model to construct the constraint, based on pre-GRAIL data, using localization (top), and for a degree and order 12 density model using the matrix formulation (bottom). We used the gravity degree range l = 70–150. The RM-S model used a constraint factor of λ = 1016 and a density expansion of degree and order 12. The maps have the same projection and properties as in Figure 1.
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: Differences in density between the localization models and the densities based on the matrix method from Figure 3. The differences for the degree and order 12 model are on the top, and those for the degree and order 50 model (using the constrained density map) are on the bottom. The maps have the same projection and properties as in Figure 1.
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: Crustal density variations for the Moon extracted with the matrix method from an RM1-constrained model based on the SGM150J data matrix, using λRM1 = 1 (top) or λRM1 = 100 (bottom). We used the gravity degree range l = 70–150, and the density models are of degree and order 5. The maps have the same projection and properties as in Figure 1.
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 density models of the crust of Mars, derived from RM-S-constrained Mars gravity models with different values for λ (left) and for density solutions following Equation (15) using different additional constraint values for D (right).
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: Correlations between RM-S-constrained solutions and gravity-from-topography using a constant density for the crust. The RM-S solutions use the GMM-3 data matrix where the constraint is applied from degree 40 for a degree and order 5 density model. Correlations for the GMM-3 model are also shown.
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: Correlations between RM-S-constrained solutions and GRGM1200B for different constraint factors λ. The RM-S solutions use the SGM150J data matrix where the constraint is applied from degree 50 for a degree and order 5 density model.
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: The mean value of SC and OC radar backscatter coefficient for surficial ice PSRs (blue) and illuminated simple crater floors (gray). Both populations are filtered such that the surficial ice PSRs and illuminated crater floors each have an average ϵr of less than or greater than 3.0, respectively, as a test of the inversion model. This filtered data contains 48 surficial ice PSRs and 17 illuminated craters. Error bars are 1σ, and trend lines are fit incorporating the 2D errors on each point (D. York et al. 2004). As expected, the two populations have distinct slope and intercept values in SC–OC space.
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: (a) Scatter-histogram plot: the bottom is a scatterplot showing illuminated crater floors with no M3 detections (gray), surficial ice PSRs with one M3 detections (blue), and surficial ice PSRs with two or more M3 detections (orange) as a function of real dielectric constant (ϵr). Above is a kernel density estimate of the histogram of each of the three populations. (b) M3 detections per km2 versus ϵr for surficial ice PSRs. The ϵr values are the median and interquartile range within each region.
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: (a) Median real dielectric constant (ϵr) within surficial ice PSRs (blue) and illuminated crater floors (gray) as a function of crater diameter. Error bars show the first and third quartiles for each region and the dashed horizontal line denotes the mean of each population’s median. (b) Histogram of ϵr for both populations described in (a).
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: Median real dielectric constant (ϵr) within PSRs lacking surficial water ice detections as a function of crater diameter. Error bars show the first and third quartiles for each region and the dashed horizontal line denotes the mean of the population’s median.