adsabs/AstroCLIMB huggingface dataset:
Paper: Imaging Spectroscopy: Earth and Planetary Remote Sensing with the PSI Tetracorder and Expert Systems from Rovers to EMIT and Beyond
Authors: Roger N. Clark, Gregg A. Swayze, K. Eric Livo, Philip G. Brodrick, Eldar Noe Dobrea, Srinivasan Vijayarangan, Robert O. Green, David Wettergreen, Alberto Candela, Amanda Hendrix, Carlos Pérez García-Pando, Neil C. Pearson, Melissa D. Lane, Adolfo González-Romero, Xavier Querol
Figure caption: (a) Curved vs. straight-line continua. For weak spectral features on a curved continuum background, straight-line continua can lead to false positives (A), errors in band depth (B), and false negatives (D). Curved continua (C, E) solve these problems but require additional continuum intervals (four endpoints instead of two). The disadvantage of more continuum endpoints is that if one falls in another absorption band, it can distort the cubic spline, creating false positives and false negatives. Tetracorder allows for both linear and curved continua to be used. (b) Illustration of a curved continuum (red line) defined by four continuum intervals defined by the red boxes. The continuum is a cubic spline. The dashed blue line shows a linear continuum and would decrease the derived depth of the feature, and as the feature becomes weaker, it could result in a false-negative detection with a linear continuum.
Paper: Imaging Spectroscopy: Earth and Planetary Remote Sensing with the PSI Tetracorder and Expert Systems from Rovers to EMIT and Beyond
Authors: Roger N. Clark, Gregg A. Swayze, K. Eric Livo, Philip G. Brodrick, Eldar Noe Dobrea, Srinivasan Vijayarangan, Robert O. Green, David Wettergreen, Alberto Candela, Amanda Hendrix, Carlos Pérez García-Pando, Neil C. Pearson, Melissa D. Lane, Adolfo González-Romero, Xavier Querol
Figure caption: Characteristics of an absorption feature. A continuum interval is chosen on each side of the feature to reduce noise. The continuum intervals in this example are about 30 nm wide. A straight-line continuum is fit between the endpoints. The reflectance at the band center (Rb ) and the corresponding continuum reflectance at the band center (Rc ) are found to compute the band depth, D. The continuum is removed by division from both the library reference spectrum and the unknown’s spectrum. From R. N. Clark et al. (2003c), a public domain image.
Paper: Imaging Spectroscopy: Earth and Planetary Remote Sensing with the PSI Tetracorder and Expert Systems from Rovers to EMIT and Beyond
Authors: Roger N. Clark, Gregg A. Swayze, K. Eric Livo, Philip G. Brodrick, Eldar Noe Dobrea, Srinivasan Vijayarangan, Robert O. Green, David Wettergreen, Alberto Candela, Amanda Hendrix, Carlos Pérez García-Pando, Neil C. Pearson, Melissa D. Lane, Adolfo González-Romero, Xavier Querol
Figure caption: Effects of mixtures and grain size on band shape. Pyroxene reflectance spectra are shown as a function of grain size. As the grain size becomes larger, more light is absorbed, the reflectance decreases, and the absorption feature bottoms flatten. Note the trace tremolite contamination causing the narrow absorption features near 1.4 and 2.3 μm. The broader pyroxene absorptions are the continuum background to the narrow tremolite features. This example shows how the components in a mixture can be readily identified even though no unmixing analysis is done. The component features are “spectrally separated” in wavelength position and width, as well as shape. Continuum-removed feature fits (top) show the similarity in shape of features at different grain sizes. The small change in shape can be used to determine grain size from the spectra, independent of abundance. Data from R. N. Clark et al. (2007), USGS Library 06. The digital spectral data are included with the GitHub Tetracorder repository. From Clark et al. (2003c), a public domain image.
Paper: Imaging Spectroscopy: Earth and Planetary Remote Sensing with the PSI Tetracorder and Expert Systems from Rovers to EMIT and Beyond
Authors: Roger N. Clark, Gregg A. Swayze, K. Eric Livo, Philip G. Brodrick, Eldar Noe Dobrea, Srinivasan Vijayarangan, Robert O. Green, David Wettergreen, Alberto Candela, Amanda Hendrix, Carlos Pérez García-Pando, Neil C. Pearson, Melissa D. Lane, Adolfo González-Romero, Xavier Querol
Figure caption: Fuzzy logic. The correlation coefficient (the fit) value is multiplied by the fuzzy logic value. When a parameter value is less than n1 or greater than m2, the fuzzy logic value is set to zero and the fit will be zero. If the parameter value is between n2 and m1, the fuzzy logic value is set to 1.0 and the fit is unchanged. If it is between n1 and n2 or between m1 and m2, the fuzzy logic value is interpolated between 0 and 1, decreasing the fit value.
Paper: Imaging Spectroscopy: Earth and Planetary Remote Sensing with the PSI Tetracorder and Expert Systems from Rovers to EMIT and Beyond
Authors: Roger N. Clark, Gregg A. Swayze, K. Eric Livo, Philip G. Brodrick, Eldar Noe Dobrea, Srinivasan Vijayarangan, Robert O. Green, David Wettergreen, Alberto Candela, Amanda Hendrix, Carlos Pérez García-Pando, Neil C. Pearson, Melissa D. Lane, Adolfo González-Romero, Xavier Querol
Figure caption: Tetracorder mineral map expert system standard products. (a) EMIT image cube of Utah, USA. Arches National Park is in the lower right corner. Results here show minerals detected using absorption features in the near-infrared region. (b) Mars Reconnaissance Orbiter, Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) scene FRT000148C1_07_IF164J_MTR3 for a crater at 7.°7 south, 84.°5 east. The Tetracorder expert system was the same for this wavelength region as the Tetracorder EMIT results in panel (a). This is a Tetracorder 5.27 standard product with expert system 5.27e1. All code and spectral libraries are in the Zenodo repository (R. N. Clark 2024) and on GitHub to reproduce these images.
Paper: Imaging Spectroscopy: Earth and Planetary Remote Sensing with the PSI Tetracorder and Expert Systems from Rovers to EMIT and Beyond
Authors: Roger N. Clark, Gregg A. Swayze, K. Eric Livo, Philip G. Brodrick, Eldar Noe Dobrea, Srinivasan Vijayarangan, Robert O. Green, David Wettergreen, Alberto Candela, Amanda Hendrix, Carlos Pérez García-Pando, Neil C. Pearson, Melissa D. Lane, Adolfo González-Romero, Xavier Querol
Figure caption: (a) Vegetation spectra. From R. N. Clark (1999), a public domain image. (b). Vegetation spectra showing shape changes with vegetation type, and the red-edge position shifts (the edge from ∼0.68 to ∼0.75 μm). The ∼0.55−0.65 μm shape changes could also be used. From R. N. Clark (1999), a public domain image.
Paper: Imaging Spectroscopy: Earth and Planetary Remote Sensing with the PSI Tetracorder and Expert Systems from Rovers to EMIT and Beyond
Authors: Roger N. Clark, Gregg A. Swayze, K. Eric Livo, Philip G. Brodrick, Eldar Noe Dobrea, Srinivasan Vijayarangan, Robert O. Green, David Wettergreen, Alberto Candela, Amanda Hendrix, Carlos Pérez García-Pando, Neil C. Pearson, Melissa D. Lane, Adolfo González-Romero, Xavier Querol
Figure caption: Tetracorder browse product for K-alunite. This browse image includes multiple K-bearing alunites into one image. On the left, the pixels containing K-alunite are shown in red on a grayscale background; on the right, white on a black background. This is an auto-generated Tetracorder product. These images are found in the results.dual.group.* directories.
Paper: Imaging Spectroscopy: Earth and Planetary Remote Sensing with the PSI Tetracorder and Expert Systems from Rovers to EMIT and Beyond
Authors: Roger N. Clark, Gregg A. Swayze, K. Eric Livo, Philip G. Brodrick, Eldar Noe Dobrea, Srinivasan Vijayarangan, Robert O. Green, David Wettergreen, Alberto Candela, Amanda Hendrix, Carlos Pérez García-Pando, Neil C. Pearson, Melissa D. Lane, Adolfo González-Romero, Xavier Querol
Figure caption: Shoulderness definitions. The red line represents the straight-line continuum between lc and rc.
Paper: Stable Stratification of the Helium Rain Layer Yields Vastly Different Interiors and Magnetic Fields for Jupiter and Saturn
Authors: S. Markham, T. Guillot
Figure caption: Sample PT structures of the stable layer as conceptualized by this work compared to B. Militzer et al. (2022; blue dashed curve). We demonstrate how changes to the phase diagram affect the placement of the helium rain layer by translating phase boundary curves from M. Schöttler & R. Redmer (2018) by some offsets in temperature δ T and pressure δ p (see Appendix A for details). The red curves show the temperature structure envisioned by this work, with the solid red curve assuming a thermal conductivity of 300 W m−1 K−1 and the red dashed and dotted curves showing thermal conductivity enhanced by a factor of 10× and 20×, respectively. We argue that the top and bottom of the stable helium rain layer should intersect phase boundaries of corresponding composition.
Paper: Stable Stratification of the Helium Rain Layer Yields Vastly Different Interiors and Magnetic Fields for Jupiter and Saturn
Authors: S. Markham, T. Guillot
Figure caption: Coexistence curves for a hydrogen–helium mixture adapted from M. Schöttler & R. Redmer (2018) with a temperature translation δ T = 650 K following C. R. Mankovich & J. J. Fortney (2020; for further discussion, see Appendix A). At each pressure level for a given temperature below the peak of the curve, there exist two coexisting phases of different composition, one helium-rich and one helium-poor.
Paper: Stable Stratification of the Helium Rain Layer Yields Vastly Different Interiors and Magnetic Fields for Jupiter and Saturn
Authors: S. Markham, T. Guillot
Figure caption: Effective coefficient of thermal expansion α eff from Equation (3) in PT space. Values calculated using the EoS from S. Howard & T. Guillot (2023) and by interpolating the phase boundary surface in PTX space from Figure 1. The red region labeled Jupiter is bounded between Jupiter interior models from B. Militzer et al. (2022; cooler) and S. Howard et al. (2023; warmer). Similarly, the orange region labeled Saturn is bounded between Saturn interior models from C. R. Mankovich & J. J. Fortney (2020; cooler) and W. B. Hubbard et al. (2009; warmer). We found α eff to be negative almost everywhere where hydrogen and helium are phase-separating. The phase-separating region includes phase boundary curves for various sample compositions, showing y = [5, 7.88, 8.87, 9.1, 15]% in ascending order.
Paper: Stable Stratification of the Helium Rain Layer Yields Vastly Different Interiors and Magnetic Fields for Jupiter and Saturn
Authors: S. Markham, T. Guillot
Figure caption: A summary of the outstanding problem with respect to the hydrogen–helium phase diagram. We show predicted phase boundaries based on four different studies (W. Lorenzen et al. 2011; M. A. Morales et al. 2013; M. Schöttler & R. Redmer 2018; S. Brygoo et al. 2021). We compare this to a sample interior structure of Jupiter (S. Howard et al. 2023), and the Lowes radius estimate error bars with shadows (J. E. P. Connerney et al. 2022; S. Sharan et al. 2022). The green dashed curve, measured on the right-hand axis, is a tabulated electrical conductivity profile for Jupiter’s interior (M. French et al. 2012).
Paper: Stable Stratification of the Helium Rain Layer Yields Vastly Different Interiors and Magnetic Fields for Jupiter and Saturn
Authors: S. Markham, T. Guillot
Figure caption: Figure illustrating the nonuniqueness of intersection points for an arbitrary adiabat (solid black). The adiabat intersects the protosolar phase boundary (solid blue), the present Jovian phase boundary (dashed blue), and a variety of phase boundaries further depleted in helium than Jupiter’s present envelope (dotted blue). The top of the helium rain level corresponding to maximum rainout is shown as a vertical line, where the adiabat has a common tangent point with its corresponding phase boundary curve.
Paper: Stable Stratification of the Helium Rain Layer Yields Vastly Different Interiors and Magnetic Fields for Jupiter and Saturn
Authors: S. Markham, T. Guillot
Figure caption: Comparison of our proposed helium rain layer structures in the interior of Jupiter and Saturn. We argue that Jupiter’s helium rain layer may be thin in order for its internal heat flux to be accommodated by diffusion, while Saturn’s may be thick because its internal heat flux may be accommodated primarily by the latent heat of helium raindrops.
Paper: Stable Stratification of the Helium Rain Layer Yields Vastly Different Interiors and Magnetic Fields for Jupiter and Saturn
Authors: S. Markham, T. Guillot
Figure caption: Latent heat associated with the formation of helium raindrops at different pressure levels and envelope helium concentration using data from M. Schöttler & R. Redmer (2018); see Figure 1 and Equation (7).
Paper: Brightness Behavior of Distant Oort Cloud Comets
Authors: Carrie E. Holt, Matthew M. Knight, Michael S. P. Kelley, Tim Lister, Quanzhi Ye, 泉志 叶, Colin Snodgrass, Cyrielle Opitom, Rosita Kokotanekova, Megan E. Schwamb, Matthew M. Dobson, Michele T. Bannister, Marco Micheli, Stefanie N. Milam, Derek C. Richardson, Edward Gomez, Joseph P. Chatelain, Sarah Greenstreet
Figure caption: Left: a kernel density plot showing the distribution of the activity indices fit across all preperihelion observations for “new” (blue) vs. returning (orange) comets using a threshold of 1/a 0 = 4.0 × 10−5 au−1. The value for each comet is shown as a small vertical line at the bottom of the figure. The two populations are consistent within the uncertainties. Returning comets exhibit a narrow distribution of activity indices with an average of n = 3.7 ± 0.7, whereas “new” comets have a broad distribution of n = 4.2 ± 1.9. Right: the overall preperihelion activity index for each object in our sample. The error values show the range of heliocentric distance and the 1σ error on the activity index. The blue plus signs represent “new” comets, while orange circles represent returning comets.
Paper: Brightness Behavior of Distant Oort Cloud Comets
Authors: Carrie E. Holt, Matthew M. Knight, Michael S. P. Kelley, Tim Lister, Quanzhi Ye, 泉志 叶, Colin Snodgrass, Cyrielle Opitom, Rosita Kokotanekova, Megan E. Schwamb, Matthew M. Dobson, Michele T. Bannister, Marco Micheli, Stefanie N. Milam, Derek C. Richardson, Edward Gomez, Joseph P. Chatelain, Sarah Greenstreet
Figure caption: Unenhanced (top row) and enhanced (bottom row) images of C/2021 A1 (Leonard) in the g and r filters (left and middle column) and a “gas” image as described in Section 3.3 (right column) on 2021 November 29, when r h = 0.53 au and Δ = 0.58 au. All images are centered on the optocenter of the comet with north up and east to the left. Light is bright and dark is faint in all panels. The images in the top and bottom row are 200,000 km and 100,000 km across, respectively. The images in the bottom row have been enhanced by subtracting the azimuthal median profile. The morphology of this gas image is very different from the “dust” (r-band) image. Two jets can be seen oriented near positional angles of 40° and 220° (measured from north through east) in the enhanced gas images, seemingly perpendicular to the tail in the northwest direction in the dust images. This image uses the same data and creation process as Figure 11 in T. Lister et al. (2022) but was created independently.
Paper: Brightness Behavior of Distant Oort Cloud Comets
Authors: Carrie E. Holt, Matthew M. Knight, Michael S. P. Kelley, Tim Lister, Quanzhi Ye, 泉志 叶, Colin Snodgrass, Cyrielle Opitom, Rosita Kokotanekova, Megan E. Schwamb, Matthew M. Dobson, Michele T. Bannister, Marco Micheli, Stefanie N. Milam, Derek C. Richardson, Edward Gomez, Joseph P. Chatelain, Sarah Greenstreet
Figure caption: The evolution of g − r color with heliocentric distance within 3 au of the Sun. Left: blue represents the data for new comets and orange for returning. Right: each color represents a different comet. The comets that change to a bluer color do so within 3.0 au from the Sun. Returning comets become bluer within 2 au from the Sun.
Paper: Brightness Behavior of Distant Oort Cloud Comets
Authors: Carrie E. Holt, Matthew M. Knight, Michael S. P. Kelley, Tim Lister, Quanzhi Ye, 泉志 叶, Colin Snodgrass, Cyrielle Opitom, Rosita Kokotanekova, Megan E. Schwamb, Matthew M. Dobson, Michele T. Bannister, Marco Micheli, Stefanie N. Milam, Derek C. Richardson, Edward Gomez, Joseph P. Chatelain, Sarah Greenstreet
Figure caption: A stacked histogram of the color distribution of “new” (blue) vs. returning (orange) comets using a threshold of 1/a 0 = 4.0 × 10−5 au−1 when r h > 3 au. There is no difference in color where “new” comets exhibit an average color g − r = 0.51 ± 0.03 mag and returning comets have g − r = 0.51 ± 0.02 mag. We note that the conclusion is broad because we only have three returning comets and that the uncertainty for each data point is larger than the histogram bin width. Comet C/2021 A1 is not included because the observations only occurred when r h < 3 au.
Paper: Brightness Behavior of Distant Oort Cloud Comets
Authors: Carrie E. Holt, Matthew M. Knight, Michael S. P. Kelley, Tim Lister, Quanzhi Ye, 泉志 叶, Colin Snodgrass, Cyrielle Opitom, Rosita Kokotanekova, Megan E. Schwamb, Matthew M. Dobson, Michele T. Bannister, Marco Micheli, Stefanie N. Milam, Derek C. Richardson, Edward Gomez, Joseph P. Chatelain, Sarah Greenstreet
Figure caption:  A(0°)f ρ (an aperture-independent quantity used as an estimator for dust production) vs. heliocentric distance for all comets in our sample, where orange points represent returning comets and blue points are DNCs using a threshold of 1/a 0 = 4.0 × 10−5 au−1. We find no clear trend with 1/a 0 or r h.
Paper: Brightness Behavior of Distant Oort Cloud Comets
Authors: Carrie E. Holt, Matthew M. Knight, Michael S. P. Kelley, Tim Lister, Quanzhi Ye, 泉志 叶, Colin Snodgrass, Cyrielle Opitom, Rosita Kokotanekova, Megan E. Schwamb, Matthew M. Dobson, Michele T. Bannister, Marco Micheli, Stefanie N. Milam, Derek C. Richardson, Edward Gomez, Joseph P. Chatelain, Sarah Greenstreet
Figure caption: A kernel density plot showing the distribution of the preperihelion activity indices (green) vs. postperihelion (magenta) for our entire sample. We include all activity indices described in Table 2, so a single object will potentially have multiple activity indices. Preperihelion, comets display a wide range of activity indices, while postperihelion is limited to n ∼ 3–6.
Paper: Brightness Behavior of Distant Oort Cloud Comets
Authors: Carrie E. Holt, Matthew M. Knight, Michael S. P. Kelley, Tim Lister, Quanzhi Ye, 泉志 叶, Colin Snodgrass, Cyrielle Opitom, Rosita Kokotanekova, Megan E. Schwamb, Matthew M. Dobson, Michele T. Bannister, Marco Micheli, Stefanie N. Milam, Derek C. Richardson, Edward Gomez, Joseph P. Chatelain, Sarah Greenstreet
Figure caption: The evolution of g − r with heliocentric distance using a 20,000 km aperture. Each point is one visit, and the error bars represent the 1σ error. The x-axis is in log scale.
Paper: Brightness Behavior of Distant Oort Cloud Comets
Authors: Carrie E. Holt, Matthew M. Knight, Michael S. P. Kelley, Tim Lister, Quanzhi Ye, 泉志 叶, Colin Snodgrass, Cyrielle Opitom, Rosita Kokotanekova, Megan E. Schwamb, Matthew M. Dobson, Michele T. Bannister, Marco Micheli, Stefanie N. Milam, Derek C. Richardson, Edward Gomez, Joseph P. Chatelain, Sarah Greenstreet
Figure caption: Morphology analysis for three objects as described in Figure 13. We have highlighted the faint features in C/2021 E3 and C/2021 T4 in light blue.
Paper: Brightness Behavior of Distant Oort Cloud Comets
Authors: Carrie E. Holt, Matthew M. Knight, Michael S. P. Kelley, Tim Lister, Quanzhi Ye, 泉志 叶, Colin Snodgrass, Cyrielle Opitom, Rosita Kokotanekova, Megan E. Schwamb, Matthew M. Dobson, Michele T. Bannister, Marco Micheli, Stefanie N. Milam, Derek C. Richardson, Edward Gomez, Joseph P. Chatelain, Sarah Greenstreet
Figure caption: Morphology analysis for two objects as described in Figure 13.
Paper: The Trojan-like Colors of Low-perihelion Kuiper Belt Objects
Authors: Matthew Belyakov, Michael E. Brown, Alya Al-Kibbi
Figure caption: Scatterplot of the median probability that a low-q KBO (orange) or Centaur (black) from our photometric sample is red given its dynamical origin in the Kuiper Belt. This probability is determined using the method in Figure 4, wherein each real Centaur and low-q KBO is linked to possible Kuiper Belt progenitors, whose colors are simulated using a combination of available photometric data. Dashed lines show the true proportions in observed Centaurs and low-q KBOs with q < 25 au, while the shaded areas show the simulated proportion of red objects with 1σ error. The simulated proportions are consistent with the data proportions for the number of red objects in the Centaurs and low-q KBOs.
Paper: The Trojan-like Colors of Low-perihelion Kuiper Belt Objects
Authors: Matthew Belyakov, Michael E. Brown, Alya Al-Kibbi
Figure caption: Distribution of the eccentricity and inclination of source objects that become dynamically similar to known low-q KBOs or Centaurs at any point in our 4 Gyr simulations. The low-q Kuiper Belt has a unique source from objects with e > 0.6 or i > 40°. Objects with those dynamical characteristics in the Kuiper Belt tend to not have very red colors; thus, the origin of low-q KBOs in these dynamically hot populations causes the population to have significantly more red (fewer very red) objects. A similar analysis of the rarity of very red objects in the scattered disk was presented in Ali-Dib et al. (2021).
Paper: Constraining Ocean and Ice Shell Thickness on Miranda from Surface Geological Structures and Stress Modeling
Authors: Caleb Strom, Tom A. Nordheim, D. Alex Patthoff, Sherry K. Fieber-Beyer
Figure caption: A south polar SatStressGUI plot showing the pattern resulting from 1 km ice shell thickening and an ice shell thickness of 128 km. An extremely thick ice shell value is chosen to demonstrate the potential strength of ice shell thickening stress compared to the other types of stress modeled. Ice shell thickening is the only stress included. The stress is uniformly ∼5 MPa across the hemisphere. Stress is uniformly tensile in contrast to the hemispherically asymmetric stress from obliquity tidal stress or polar wander stress and the hemispherically symmetric stress from eccentricity tidal stress. Stress tensors are plotted to emphasize the isotropy of the stress field. Upper brittle ice shell thickness is 1 km. The viscosity chosen is 1E12 Pa-s.
Paper: Constraining Ocean and Ice Shell Thickness on Miranda from Surface Geological Structures and Stress Modeling
Authors: Caleb Strom, Tom A. Nordheim, D. Alex Patthoff, Sherry K. Fieber-Beyer
Figure caption: For lower ice shell viscosity values ≥1E14 Pa-s, the polar wander stress magnitude increases significantly for ice shells ≤30 km thick compared to ice shells >30 km thick. For lower ice shell viscosity values ≥1E12 Pa-s and <1E14 Pa-s, stress from polar wander does change significantly with decreasing ice shell thickness, but more gradually. Notice the spike in magnitude that is observed for extremely thin ice shells (≤3 km thick) as shown in Figure 7. The above plot represents tidal stress vs. ice shell thickness for Miranda at 10° of polar wander. The vertical axis is on a logarithmic scale. Circles and triangles are added for clarity.
Paper: Constraining Ocean and Ice Shell Thickness on Miranda from Surface Geological Structures and Stress Modeling
Authors: Caleb Strom, Tom A. Nordheim, D. Alex Patthoff, Sherry K. Fieber-Beyer
Figure caption: For lower ice shell viscosity values ≥1E14 Pa-s, the tidal stress magnitude increases significantly for ice shells ≤30 km thick compared to ice shells >30 km thick. For lower ice shell viscosity values ≥1E12 Pa-s and <1E14 Pa-s, there is a significant increase in obliquity tidal stress magnitude with decreasing ice shell thickness, but the change is more gradual. Note that the same spike in tidal stress is observed for extremely thin ice shells (≤3 km thick) as is observed for eccentricity tidal stress and polar wander stress in Figures 7 and 10, respectively. Brittle upper crust thickness is 1 km.