adsabs/AstroCLIMB huggingface dataset:
Paper: Europa’s Surface Water-ice Crystallinity and Correlations between Lineae and Hydrate Composition
Authors: Jodi R. Berdis, James R. Murphy, Nancy J. Chanover
Figure caption: Left panels: NNLS spectral mixture analysis model results (red) for the spectra of three pixels (black) across the NIMS 15e015 cube. The χ 2 of the model fit is provided for each panel at top center, and the approximate species abundances of the model fit are provided at center right. Species that are listed under “Trace amounts” are present in abundances of ∼5%. Right panels: linear spectral mixture analysis model results (blue) from three types of spectra (black) across the NIMS 15e015 cube (Figure 6 in Dalton et al. 2012). Dalton et al. (2012) selected three types of spectra that were averaged together to produce the above spectra, (a) Vis Bright (13 spectra), (b) Vis Dark (24 spectra), and (c) IR Bright (20 spectra).
Paper: Europa’s Surface Water-ice Crystallinity and Correlations between Lineae and Hydrate Composition
Authors: Jodi R. Berdis, James R. Murphy, Nancy J. Chanover
Figure caption: Left panels: NNLS spectral mixture analysis model results (red) from the spectra of three pixels (black) across the NIMS 17e009 cube. The χ 2 of the model fit is provided for each panel at top center, and the approximate species abundances of the model fit are provided at center right. Species that are listed under “Trace amounts” were present in abundances of ∼5%. Right panels: linear spectral mixture analysis model results (blue) from three types of spectra (black) across the NIMS 17e009 cube (Figure 8 in Dalton et al. 2012). Dalton et al. (2012) selected three types of spectra that were averaged together to produce the above spectra, (a) Vis Bright (13 spectra), (b) Vis Dark (24 spectra), and (c) IR Bright (20 spectra).
Paper: Europa’s Surface Water-ice Crystallinity and Correlations between Lineae and Hydrate Composition
Authors: Jodi R. Berdis, James R. Murphy, Nancy J. Chanover
Figure caption: Abundance profiles across the 15e015 NIMS cube for amorphous water-ice at grain sizes ranging from 100 to 250 μm, where grain sizes less than 100 μm are not included, as they were present in trace amounts.
Paper: Europa’s Surface Water-ice Crystallinity and Correlations between Lineae and Hydrate Composition
Authors: Jodi R. Berdis, James R. Murphy, Nancy J. Chanover
Figure caption: Distribution of χ 2 across the NIMS 15e015 NIMS cube. The mean χ 2 of pixels in which the NNLS was performed is 0.03633.
Paper: Europa’s Surface Water-ice Crystallinity and Correlations between Lineae and Hydrate Composition
Authors: Jodi R. Berdis, James R. Murphy, Nancy J. Chanover
Figure caption: Distribution of crystallinity across the 15e015 NIMS cube. The mean crystallinity of all pixels in this cube is ∼35%.
Paper: Europa’s Surface Water-ice Crystallinity and Correlations between Lineae and Hydrate Composition
Authors: Jodi R. Berdis, James R. Murphy, Nancy J. Chanover
Figure caption: The full spectral reference library for non-water-ice materials resampled at a spectral resolution that matches that of the 15e015 NIMS image cube. These spectra were offset vertically for clarity.
Paper: Europa’s Surface Water-ice Crystallinity and Correlations between Lineae and Hydrate Composition
Authors: Jodi R. Berdis, James R. Murphy, Nancy J. Chanover
Figure caption: Abundance profiles across the 17e009 NIMS cube for MgCl2, H2SO4, and 250 μm amorphous water-ice; all other non-water-ice species are not included, as they were present in trace amounts. These three species display a feature located at ∼124°–125° W and are likely influenced by a geological feature at that location. Images of a doublet ridge and triple band are shown to demonstrate possible surface features that could cause these abundance patterns. The doublet ridge pictured is ∼2 km wide, and the triple band pictured is ∼10 km wide; however, widths for both types of features vary substantially across Europa’s surface. Images courtesy of Fagents et al. (2000).
Paper: Europa’s Surface Water-ice Crystallinity and Correlations between Lineae and Hydrate Composition
Authors: Jodi R. Berdis, James R. Murphy, Nancy J. Chanover
Figure caption: Representation of the 17e009 NIMS cube overlaying the relevant region from an image mosaic (image courtesy of USGS Astrogeology Science Center; Controlled Photomosaic Map of Europa, Je 15M CMN). The black rectangle with a blue border represents the approximate location and size of the 17e009 NIMS cube. Adonis Linea is labeled and crosses through the 17e009 NIMS cube; however, the exact location where it crosses the cube is unknown, as the two data sets are not co-registered. Below the image is a spatial latitude–longitude representation of the data that exists within the 15e015 cube. The black pixels represent locations where image spectroscopy data were acquired.
Paper: Europa’s Surface Water-ice Crystallinity and Correlations between Lineae and Hydrate Composition
Authors: Jodi R. Berdis, James R. Murphy, Nancy J. Chanover
Figure caption: Abundance profiles across the 17e009 NIMS cube for crystalline water-ice at grain sizes ranging from 100 to 250 μm, where grain sizes less than 100 μm are not included, as they were present in trace amounts.
Paper: Possible Activity in 468861 (2013 LU28)
Authors: Lily A. Slemp, Karen J. Meech, Erica Bufanda, Jan T. Kleyna, Olivier Hainaut, James Bauer, Robert Weryk, Larry Denneau, Jacqueline V. Keane, Bhuwan C. Bhatt, Devendra K. Sahu, Chase M. Urasaki, Richard Wainscoat, Marco Micheli
Figure caption: Stacked CFHT data of the object on 2018 May 6 for a total exposure time of 1921 s. Image A is stacked on the stars, while image B is stacked on 2013 LU28 at non-sidereal rates.
Paper: Europa’s Surface Water-ice Crystallinity and Correlations between Lineae and Hydrate Composition
Authors: Jodi R. Berdis, James R. Murphy, Nancy J. Chanover
Figure caption: Distribution of crystallinity across the 17e009 NIMS cube. The mean crystallinity of all pixels in this cube is ∼15%.
Paper: Europa’s Surface Water-ice Crystallinity and Correlations between Lineae and Hydrate Composition
Authors: Jodi R. Berdis, James R. Murphy, Nancy J. Chanover
Figure caption: Abundance profiles across the 17e009 NIMS cube for amorphous water-ice at grain sizes ranging from 100 to 250 μm, where grain sizes less than 100 μm are not included, as they were present in trace amounts.
Paper: Possible Activity in 468861 (2013 LU28)
Authors: Lily A. Slemp, Karen J. Meech, Erica Bufanda, Jan T. Kleyna, Olivier Hainaut, James Bauer, Robert Weryk, Larry Denneau, Jacqueline V. Keane, Bhuwan C. Bhatt, Devendra K. Sahu, Chase M. Urasaki, Richard Wainscoat, Marco Micheli
Figure caption: The spectral reflectivity of 2013 LU28 in the g, r, i, and z bands in comparison to other solar system small body surfaces. Positive slope indicates a red color in the surface material. The color is consistent with organic-rich comet surfaces.
Paper: Europa’s Surface Water-ice Crystallinity and Correlations between Lineae and Hydrate Composition
Authors: Jodi R. Berdis, James R. Murphy, Nancy J. Chanover
Figure caption: Distribution of χ 2 across the NIMS 17e009 NIMS cube. The χ 2 appears to improve at higher longitudes; the mean χ 2 of pixels in which the NNLS was performed is 0.03285.
Paper: Possible Activity in 468861 (2013 LU28)
Authors: Lily A. Slemp, Karen J. Meech, Erica Bufanda, Jan T. Kleyna, Olivier Hainaut, James Bauer, Robert Weryk, Larry Denneau, Jacqueline V. Keane, Bhuwan C. Bhatt, Devendra K. Sahu, Chase M. Urasaki, Richard Wainscoat, Marco Micheli
Figure caption: The asteroidal light curve (plotted for a nucleus radius ∼55.7 km, p r  = 0.04) suggests that there may have been activity near −90° < TA (deg) < −70° (r ∼ 17.0–12.9 au) between 2016 October and 2019 October 2019. The inset shows the data plotted as reduced magnitude (removing the effects of geometry) with error bars. It looks like there might be as much as 0.5–0.6 mag scatter from rotation.
Paper: Possible Activity in 468861 (2013 LU28)
Authors: Lily A. Slemp, Karen J. Meech, Erica Bufanda, Jan T. Kleyna, Olivier Hainaut, James Bauer, Robert Weryk, Larry Denneau, Jacqueline V. Keane, Bhuwan C. Bhatt, Devendra K. Sahu, Chase M. Urasaki, Richard Wainscoat, Marco Micheli
Figure caption: (a) A deep stack of 2013 LU28 from 17 nights of images (total exposure of 40 minutes in the ﹩r^{\prime} ﹩-band) from CFHT Megacam that shows no obvious coma. 2013 LU28 may be like other active objects with minimal coma like 95P/Chiron. (b) Image of 2013 LU28 from 2018 May 16 showing the profile cut for surface brightness. (c) Surface brightness profile of 2013 LU28 from 2018 May 16 (TA = −80°.7, r = 14.76 au) compared to field stars shows that there is no scattered light contributed from any dust coma.
Paper: Possible Activity in 468861 (2013 LU28)
Authors: Lily A. Slemp, Karen J. Meech, Erica Bufanda, Jan T. Kleyna, Olivier Hainaut, James Bauer, Robert Weryk, Larry Denneau, Jacqueline V. Keane, Bhuwan C. Bhatt, Devendra K. Sahu, Chase M. Urasaki, Richard Wainscoat, Marco Micheli
Figure caption: (a) The azimuthal surface brightness profile of 2013 LU28 from 2018 May 16 (TA = −80°.7, r = 14.76 au) compared to field stars shows that there is no scattered light contributed from any significant dust coma. (b) Using Equation (13), the difference in surface profiles is converted to a maximum dust production rate that can remain undetected.
Paper: Polar Ice Accumulation from Volcanically Induced Transient Atmospheres on the Moon
Authors: Andrew X. Wilcoski, Paul O. Hayne, Margaret E. Landis
Figure caption: A zoomed-out version of Figure 4 that includes Jeans escape rates. The plot shows atmospheric escape rate as a function of atmospheric mass for H2O (solid blue line) and CO (dashed–dotted orange line) photodissociative escape, sputtering escape (green dashed line), and Jeans escape (red dotted line). For photodissociation, the atmospheric mass represents the mass of that single species (H2O or CO) present in the atmosphere.
Paper: Polar Ice Accumulation from Volcanically Induced Transient Atmospheres on the Moon
Authors: Andrew X. Wilcoski, Paul O. Hayne, Margaret E. Landis
Figure caption: Atmospheric escape rate as a function of atmospheric mass for H2O (solid blue line) and CO (dashed–dotted orange line) photodissociative escape and sputtering escape (green dashed line). For photodissociation, the atmospheric mass represents the mass of that single species (H2O or CO) present in atmosphere. Jeans escape rates (not shown here; see Appendix A) vary from 10−11 to 10−5 kg s−1 over the range of atmospheric masses from 108 to 1014 kg and are therefore negligible compared to photodissociative and sputtering escape rates.
Paper: Polar Ice Accumulation from Volcanically Induced Transient Atmospheres on the Moon
Authors: Andrew X. Wilcoski, Paul O. Hayne, Margaret E. Landis
Figure caption: Ice distribution and thickness after a complete 2 Gyr model run. (a, b) Maps from ±60° latitude to the poles in (a) the south and (b) the north. (c, d) Maps from ±80° latitude to the poles in (c) the south and (d) the north. (e, f) Ice deposits remaining after 4 Gyr of sublimation to space.
Paper: Polar Ice Accumulation from Volcanically Induced Transient Atmospheres on the Moon
Authors: Andrew X. Wilcoski, Paul O. Hayne, Margaret E. Landis
Figure caption: (a) Total (blue) and H2O (orange) mass of the atmosphere over a typical 4–2 Ga model run. (b) Rate of water removal from the atmosphere due to ice accumulation (purple) and photodissociation (yellow) over time. As shown in the insets, when atmospheric water vapor mass is high, ice accumulation dominates over photodissociation and vice versa.
Paper: Polar Ice Accumulation from Volcanically Induced Transient Atmospheres on the Moon
Authors: Andrew X. Wilcoski, Paul O. Hayne, Margaret E. Landis
Figure caption: Frost thickness predicted by the nightside condensation model as it varies with latitude and time for an arbitrary longitude. Time begins at sunset and continues for two lunar days. The dotted white lines denote sunrise (i.e., the dawn terminator), and the dashed white line denotes sunset.
Paper: Polar Ice Accumulation from Volcanically Induced Transient Atmospheres on the Moon
Authors: Andrew X. Wilcoski, Paul O. Hayne, Margaret E. Landis
Figure caption: Maximum surface temperatures measured by the Diviner Lunar Radiometer Experiment and adjusted to account for ∼25% lower solar luminosity at ∼3.5 Ga from ±60° latitude to the poles in the south (left) and the north (right).
Paper: Polar Ice Accumulation from Volcanically Induced Transient Atmospheres on the Moon
Authors: Andrew X. Wilcoski, Paul O. Hayne, Margaret E. Landis
Figure caption: Fractional surface area covered by ice deposits as a function of latitude at both poles. Bin width is 1° in latitude. The solid teal line and the dashed orange line represent the fractional areas of ice in the south and north, respectively, resulting from the final ice distribution of the model shown in Figures 8(c) and (d). The teal circles and orange triangles represent fractional areas of ice after 4 Gyr of sublimation to space in the south and north, respectively (Figures 8(e) and (f)).
Paper: Polar Ice Accumulation from Volcanically Induced Transient Atmospheres on the Moon
Authors: Andrew X. Wilcoski, Paul O. Hayne, Margaret E. Landis
Figure caption: Exobase height above the surface as a function of atmospheric mass for an atmospheric temperature of 253 K. The different curves are for the bulk atmosphere with a mean molecular mass of 31.4 g mol−1 (orange solid line), a pure H2O atmosphere (blue dashed–dotted line), and a pure CO atmosphere (green dashed line).
Paper: Polar Ice Accumulation from Volcanically Induced Transient Atmospheres on the Moon
Authors: Andrew X. Wilcoski, Paul O. Hayne, Margaret E. Landis
Figure caption: A typical histogram of eruption frequency in time over the 4–2 Ga period, based on Figure 1 of Whitten & Head (2015). The bins sizes are 107 yr.
Paper: Polar Ice Accumulation from Volcanically Induced Transient Atmospheres on the Moon
Authors: Andrew X. Wilcoski, Paul O. Hayne, Margaret E. Landis
Figure caption: Erupted vapor mass histograms for two different model runs. The blue circles are derived from an exponential SFD with a total of 50,000 eruptions. The orange triangles are derived from a power-law SFD with a total of 545,000 eruptions. The exponential distribution is the default assumption of our model.
Paper: Polar Ice Accumulation from Volcanically Induced Transient Atmospheres on the Moon
Authors: Andrew X. Wilcoski, Paul O. Hayne, Margaret E. Landis
Figure caption: Primary model components. Volcanic outgassing is balanced against atmospheric escape to space and surface ice formation, assuming a well-mixed atmosphere.
Paper: Connections between the Transient Polar Warming and Solstitial Pause on Mars
Authors: Anthony D. Toigo, Darryn W. Waugh
Figure caption: Zonal-average and 30 sol averaged temperature (K) on a constant 10 Pa pressure surface. Temperature is contoured every 20 K between 140 and 200 K, and values >180 K are shaded gray and <140 K are shaded dark gray.
Paper: Connections between the Transient Polar Warming and Solstitial Pause on Mars
Authors: Anthony D. Toigo, Darryn W. Waugh
Figure caption: Zonal-average and 30 sol averaged zonal winds (m s−1) on a constant 10 Pa pressure surface. Winds is contoured every 50 m s−1 between −150 and 150 m s−1, and values >100 m s−1 are shaded gray and >150 m s−1 are shaded dark gray.
Paper: Connections between the Transient Polar Warming and Solstitial Pause on Mars
Authors: Anthony D. Toigo, Darryn W. Waugh
Figure caption: Zonal-average and 30 sol averaged scaled vertical wind (m s−1) on a constant 30 Pa pressure surface. Winds are contoured at 0, −10, −50, and −100 mm s−1, with values <−50 mm s−1 shaded gray and <−100 mm s−1 shaded dark gray.
Paper: Connections between the Transient Polar Warming and Solstitial Pause on Mars
Authors: Anthony D. Toigo, Darryn W. Waugh
Figure caption: Zonal-average and 30 sol averaged Eady growth rate (10−6 s−1) at a constant height of 0.2 km above the surface. Growth rate is contoured at 0, 10, 20, and 50 × 10−6 s−1, and values >20 × 10−6 s−1 are shaded gray and >50 × 10−6 s−1 are shaded dark gray.
Paper: Connections between the Transient Polar Warming and Solstitial Pause on Mars
Authors: Anthony D. Toigo, Darryn W. Waugh
Figure caption: Zonal-average and 30 sol averaged rms(﹩T^{\prime} ﹩) (K) at a constant height of 2.5 km above the surface as a function of solar longitude and latitude. rms(﹩T^{\prime} ﹩) contours are every 2.5 K, from 0 to 10 K, with values >5 K shaded gray and >10 K shaded dark gray.
Paper: Connections between the Transient Polar Warming and Solstitial Pause on Mars
Authors: Anthony D. Toigo, Darryn W. Waugh
Figure caption: Zonal-average and 30 sol averaged scaled potential vorticity (10−6 K m2 kg−1 s−1) on a constant 10 Pa pressure surface. Potential vorticity is contoured every 100 units between 0 and 300, and values >200 are shaded gray and >300 are shaded dark gray.
Paper: Connections between the Transient Polar Warming and Solstitial Pause on Mars
Authors: Anthony D. Toigo, Darryn W. Waugh
Figure caption: Zonal-average and 30 sol averaged zonal-wind vertical wind shear dU/dz (10−3 s−1) at a constant height of 0.2 km above the surface. Wind shear is contoured every 5 × 10−3 s−1, and values >5 × 10−3 s−1 are shaded gray and >15 × 10−3 s−1 are shaded dark gray.
Paper: Mapping Lunar Swirls with Machine Learning: The Application of Unsupervised and Supervised Image Classification Algorithms in Reiner Gamma and Mare Ingenii
Authors: Frank C. Chuang, Matthew D. Richardson, John R. Weirich, Amanda A. Sickafoose, Deborah L. Domingue
Figure caption: (A). Surface reflectance images of the Reiner Gamma study region at 18° incidence angle (top), 42° incidence angle (middle), and 66° incidence angle (bottom). Reflectance was derived by applying a conversion constant to raw DN values in the LROC NAC left and right stereo pairs. See text for details. White areas are impact craters and large geologic features that have been masked. Images have an applied contrast stretch of 2.5 standard deviations of the mean. These images are in the boxed area shown in Figure 1(a). (B). Surface reflectance images of the Mare Ingenii study region at 33° incidence angle (top), 48° incidence angle (middle), and 63° incidence angle (bottom). Reflectance was derived by applying a conversion constant to raw DN values in the LROC NAC left and right stereo pairs. See text for details. White areas are impact craters and large geologic features that have been masked. Images have an applied contrast stretch of 2.5 standard deviations of the mean. These images are in the boxed area shown in Figure 1(b).
Paper: Mapping Lunar Swirls with Machine Learning: The Application of Unsupervised and Supervised Image Classification Algorithms in Reiner Gamma and Mare Ingenii
Authors: Frank C. Chuang, Matthew D. Richardson, John R. Weirich, Amanda A. Sickafoose, Deborah L. Domingue
Figure caption: Probability maps from the MLC algorithm for the (A) Reiner Gamma and (B) Mare Ingenii study regions. Lower probabilities (<91%) are red to orange, moderate probabilities (91%–94%) are yellow to light green, and higher probabilities are light green to dark green (>94%). Lower probabilities are associated with anomalously high albedos, typically areas with greater relief or geologic features such as the edges of crater rims or ejecta deposits. For reference, the training areas for on-swirl (blue), off-swirl (pink), and diffuse-swirl (tan) are shown (see Figures 3(a) and (b)).
Paper: Mapping Lunar Swirls with Machine Learning: The Application of Unsupervised and Supervised Image Classification Algorithms in Reiner Gamma and Mare Ingenii
Authors: Frank C. Chuang, Matthew D. Richardson, John R. Weirich, Amanda A. Sickafoose, Deborah L. Domingue
Figure caption: Cumulative distribution function plots of reflectance values for on- (blue), off- (pink), and diffuse-swirl (tan) classes in the Reiner Gamma study region at (top) 18°, (middle) 42°, and (bottom) 66° incidence angles. The spacings between the three units are relatively even for the MLC algorithm, whereas the diffuse-swirl values are shifted slightly closer to on-swirl values for the K-means algorithms at both 18° and 42° incidence angles. The distinct reflectance ranges for each unit indicate three distinct swirl units.
Paper: Mapping Lunar Swirls with Machine Learning: The Application of Unsupervised and Supervised Image Classification Algorithms in Reiner Gamma and Mare Ingenii
Authors: Frank C. Chuang, Matthew D. Richardson, John R. Weirich, Amanda A. Sickafoose, Deborah L. Domingue
Figure caption: Classification maps using the K-means algorithm for the (A) Reiner Gamma and (B) Mare Ingenii study regions. Multiband albedo data including all three incidence angles were used in the algorithm for both study regions. On-swirl areas are shown in blue, off swirl in pink, and diffuse-swirl in tan. For details on the surface area and pixel counts of each unit type, see Table 3.
Paper: Mapping Lunar Swirls with Machine Learning: The Application of Unsupervised and Supervised Image Classification Algorithms in Reiner Gamma and Mare Ingenii
Authors: Frank C. Chuang, Matthew D. Richardson, John R. Weirich, Amanda A. Sickafoose, Deborah L. Domingue
Figure caption: Classification maps using the MLC algorithm for the (A) Reiner Gamma and (B) Mare Ingenii study regions. Multiband albedo data including all three incidence angles were used in the algorithm for both study regions. On-swirl areas are shown in blue, off-swirl in pink, and diffuse-swirl in tan. For details on the surface area and pixel counts of each unit type, see Table 3.
Paper: Mapping Lunar Swirls with Machine Learning: The Application of Unsupervised and Supervised Image Classification Algorithms in Reiner Gamma and Mare Ingenii
Authors: Frank C. Chuang, Matthew D. Richardson, John R. Weirich, Amanda A. Sickafoose, Deborah L. Domingue
Figure caption: Locations of training areas in (A) Reiner Gamma and (B) Mare Ingenii for the three units of lunar swirls used in the MLC algorithm. On-swirl areas are outlined in blue, off-swirl areas in pink, and diffuse-swirl areas in tan. For details on the surface area and pixel counts of the training areas, see Table 2. The background image is the albedo at 18° and 48° incidence angles for Reiner Gamma and Mare Ingenii, respectively.
Paper: Mapping Lunar Swirls with Machine Learning: The Application of Unsupervised and Supervised Image Classification Algorithms in Reiner Gamma and Mare Ingenii
Authors: Frank C. Chuang, Matthew D. Richardson, John R. Weirich, Amanda A. Sickafoose, Deborah L. Domingue
Figure caption: Elbow plots for (A) Reiner Gamma and (B) Mare Ingenii of the optimal number of k cluster units using the K-means classification algorithm. The total within-cluster sum of squared distances (WCSS) between data points and the centroid location is plotted against the k number of units being evaluated (see text for details). Here, the elbow occurs at k = 3, or three units, when the WCSS no longer changes significantly at a given k and beyond.
Paper: Mapping Lunar Swirls with Machine Learning: The Application of Unsupervised and Supervised Image Classification Algorithms in Reiner Gamma and Mare Ingenii
Authors: Frank C. Chuang, Matthew D. Richardson, John R. Weirich, Amanda A. Sickafoose, Deborah L. Domingue
Figure caption: Cumulative distribution function plots of reflectance albedo values for on- (blue), off- (pink), and diffuse-swirl (tan) classes in the Mare Ingenii study region at (top) 33°, (middle) 48°, and (bottom) 63° incidence angles. The spacings between the three units are relatively even for the K-means algorithm. The distinct reflectance ranges for each unit indicate three distinct swirl units.