adsabs/AstroCLIMB huggingface dataset:
Paper: Surface Properties of Sediments at the 2014–2015 Holuhraun Lava Flow-field: Insights from Multiwavelength Radar
Authors: Reid P. Perkins, Shannon M. Hibbard, Catherine D. Neish, Christopher W. Hamilton, Bruce A. Campbell
Figure caption: Estimated C-band radar loss due to the change in Fresnel reflectivity contrast (Equation (1)), as well as loss due to mantling (Equation (2)), assuming a base layer real permittivity of 9 (typical for basalt) and varying mantling layer real permittivity. The two loss tangents represent “dry” (0.001) and “wet” (0.1) conditions. These values are compared with our radar losses measured from Sentinel-1 data at Site 2.
Paper: Surface Properties of Sediments at the 2014–2015 Holuhraun Lava Flow-field: Insights from Multiwavelength Radar
Authors: Reid P. Perkins, Shannon M. Hibbard, Catherine D. Neish, Christopher W. Hamilton, Bruce A. Campbell
Figure caption: Loftmyndir ehf. orthomosaics showing the sediment-mantled ROIs (outlined in yellow). (a) Overview of all mapped sediment-mantled ROIs on the northern margin of the 2014–2015 Holuhraun lava flow-field. The white box indicates the location of GPR Site 1, and the black box indicates the extent of Figure 3, the location of GPR Site 2. (b) The 2015 aerial image of a lobe of the northern flow-field centered at 64.92°N, 16.67°W (GPR Site 2). (c) The 2021 aerial image for the same area as panel (b), indicating the color and texture change inferred to be due to sediment mantling.
Paper: Surface Properties of Sediments at the 2014–2015 Holuhraun Lava Flow-field: Insights from Multiwavelength Radar
Authors: Reid P. Perkins, Shannon M. Hibbard, Catherine D. Neish, Christopher W. Hamilton, Bruce A. Campbell
Figure caption: (a) Radar backscatter coefficient (σ°) values over time for all sediment-mantled regions of the northern margin of Holuhraun from 2015 to 2022, as well as values for the rest of the lava flow-field outside the study areas, visible as blue points. (b) The same observations as in panel (a), but for GPR Site 1. (c) The same observations as in panel (a), but for GPR Site 2. In all cases, error bars represent the averaged standard deviation of the data (VH and VV polarization).
Paper: Surface Properties of Sediments at the 2014–2015 Holuhraun Lava Flow-field: Insights from Multiwavelength Radar
Authors: Reid P. Perkins, Shannon M. Hibbard, Catherine D. Neish, Christopher W. Hamilton, Bruce A. Campbell
Figure caption: Sentinel-1 C-band VH radar data and Loftmyndir ehf. aerial imagery of the 2014–2015 Holuhraun lava flow-field, centered on GPR Site 2. The legend for panels (a) and (b) represents radar backscatter values in dB. Mantled area shapefiles are outlined in yellow. (a) Image acquired on 2015 June 27 (ID: S1A_IW_GRDH_1SDV_20150627T074124_20150627T074149_006556_008B94_684C), as well as (c) the Loftmyndir ehf. aerial image acquired on 2015 August 30, as shown in Figure 2(b). (b) Image acquired on 2021 August 20 (ID: S1A_IW_GRDH_1SDV_20210820T184300_20210820T184325_039317_04A49F_E5F0), as well as (d) the Loftmyndir ehf. aerial image acquired on 2021 August 9, as shown in Figure 2(c).
Paper: Surface Properties of Sediments at the 2014–2015 Holuhraun Lava Flow-field: Insights from Multiwavelength Radar
Authors: Reid P. Perkins, Shannon M. Hibbard, Catherine D. Neish, Christopher W. Hamilton, Bruce A. Campbell
Figure caption: LiDAR DEMs of the regions where GPR data were collected: panel (a) represents Site 1, and panel (b) represents Site 2. LiDAR DEMs are overlaid on the Loftmyndir ehf. 2021 orthomosaic. GPR transects are shown in green. For every transect, the transect number marks the starting point of the GPR scan, corresponding to “Meter 0” in Table 2.
Paper: Surface Properties of Sediments at the 2014–2015 Holuhraun Lava Flow-field: Insights from Multiwavelength Radar
Authors: Reid P. Perkins, Shannon M. Hibbard, Catherine D. Neish, Christopher W. Hamilton, Bruce A. Campbell
Figure caption: VH/VV ratio of C-band Sentinel-1 data from 2015 to 2022. The solid line is the trend of the ratio values for each year.
Paper: Surface Properties of Sediments at the 2014–2015 Holuhraun Lava Flow-field: Insights from Multiwavelength Radar
Authors: Reid P. Perkins, Shannon M. Hibbard, Catherine D. Neish, Christopher W. Hamilton, Bruce A. Campbell
Figure caption: Sediment depths collected along Transect 1 with a 50 cm length manual probe. Missing points are >50 cm in depth.
Paper: Surface Properties of Sediments at the 2014–2015 Holuhraun Lava Flow-field: Insights from Multiwavelength Radar
Authors: Reid P. Perkins, Shannon M. Hibbard, Catherine D. Neish, Christopher W. Hamilton, Bruce A. Campbell
Figure caption: Estimated L-band radar loss due to the change in Fresnel reflectivity contrast (Equation (1)), as well as mantling loss (Equation (2)), with a range of loss tangents from 0.001 to 0.1 for a layer thickness (h) of (a) 20 cm and (b) 10 cm. These are compared with the calculated L-band UAVSAR-derived radar loss at the older lava flow just north of Holuhraun (Figure 11).
Paper: Surface Properties of Sediments at the 2014–2015 Holuhraun Lava Flow-field: Insights from Multiwavelength Radar
Authors: Reid P. Perkins, Shannon M. Hibbard, Catherine D. Neish, Christopher W. Hamilton, Bruce A. Campbell
Figure caption: Radar backscatter as a function of the rms slope using Hagfors’s model. The dashed lines indicate likely dielectric constants for Holuhraun sediments (the “mantled” surface), while the solid lines indicate dielectric constant values for basalt (the “pre-mantling” surface). Stars indicate estimated values for “mantled” (black) and “pre-mantled” (gray) rms slopes.
Paper: Surface Properties of Sediments at the 2014–2015 Holuhraun Lava Flow-field: Insights from Multiwavelength Radar
Authors: Reid P. Perkins, Shannon M. Hibbard, Catherine D. Neish, Christopher W. Hamilton, Bruce A. Campbell
Figure caption: L-band UAVSAR HV image of the 2014–2015 Holuhraun lava flow-field. A yellow box indicates an area representing the “mantled” surface of the older lava flow, with the yellow box in the interior of the flow representing a proxy for the “pre-mantled” surface of the older lava flow. The lower value of –30 dB is taken from the average “mantled” surface, while –12.15 dB is the highest value in the data.
Paper: The Open-source Photochem Code: A General Chemical and Climate Model for Interpreting (Exo)Planet Observations
Authors: Nicholas F. Wogan, Natasha E. Batalha, Kevin Zahnle, Joshua Krissansen-Totton, David C. Catling, Eric T. Wolf, Tyler D. Robinson, Victoria Meadows, Giada Arney, Shawn Domagal-Goldman
Figure caption: Photochemical simulations of Venus’s atmosphere as a closed system (lines) compared to observations (black circles). Table B4 lists the sources for all observations. The solid lines are the nominal model, while the dashed lines are a sensitivity test that adds additional chlorine chemistry as described in Section 3.1.1. The dotted lines are the thermochemical equilibrium of each atmospheric layer in the nominal model. Panel (k) is the H2SO4 cloud mixing ratio computed with fH2OSO4(l) = nH2OSO4(l)/n, where nH2OSO4(l) is the number of molecules cm−3 in the condensed phase and n is the total gas-phase number density. Overall, our nominal model reproduces the observed concentrations of CO, HCl, H2SO4, H2S, and SO at all altitudes but fails to explain the measured abundances of SO2, H2O, OCS, S3, and S4. Most of these model−data discrepancies are long-standing unsolved problems in Venus photochemistry (C. J. Bierson & X. Zhang 2020; P. B. Rimmer et al. 2021).
Paper: The Open-source Photochem Code: A General Chemical and Climate Model for Interpreting (Exo)Planet Observations
Authors: Nicholas F. Wogan, Natasha E. Batalha, Kevin Zahnle, Joshua Krissansen-Totton, David C. Catling, Eric T. Wolf, Tyler D. Robinson, Victoria Meadows, Giada Arney, Shawn Domagal-Goldman
Figure caption: Climate simulations of Venus’s atmosphere. (a) A climate model that uses the H2SO4 clouds and gas concentrations predicted by the nominal photochemical simulation in Figure 2. (b) The same as panel (a), except the model uses observed cloud optical properties from D. Crisp (1986). (c) The same as panel (b), except the simulation uses Venus’s observed SO2 profile. In panels (a), (b), and (c), the top panels shows predicted temperature profiles compared to VIRA (A. Seiff et al. 1985). Thick portions of the predicted P–T profile indicate convecting layers. The bottom panels are the net thermal and solar fluxes compared to the solar fluxes measured by Pioneer Venus (M. G. Tomasko et al. 1980). Our climate model can reproduce Venus’s energy balance (panel (c)) when we correct for inaccurate clouds and SO2 predicted by the photochemical model.
Paper: The Open-source Photochem Code: A General Chemical and Climate Model for Interpreting (Exo)Planet Observations
Authors: Nicholas F. Wogan, Natasha E. Batalha, Kevin Zahnle, Joshua Krissansen-Totton, David C. Catling, Eric T. Wolf, Tyler D. Robinson, Victoria Meadows, Giada Arney, Shawn Domagal-Goldman
Figure caption: A schematic P–T profile to aid explanation of our climate model. On the right side, j indexes each atmospheric layer, and the column labeled “Convecting with below?” indicates whether a given j layer is convecting with the layer below it. Note that layer j = 5 is convecting with the layer above it (j = 6), but not with the layer below it (j = 4), so it is labeled “No” in the “Convecting with below?” column. A new atmospheric grid k can be defined that groups the convecting layers, simplifying the iteration to RCE (see text).
Paper: The Open-source Photochem Code: A General Chemical and Climate Model for Interpreting (Exo)Planet Observations
Authors: Nicholas F. Wogan, Natasha E. Batalha, Kevin Zahnle, Joshua Krissansen-Totton, David C. Catling, Eric T. Wolf, Tyler D. Robinson, Victoria Meadows, Giada Arney, Shawn Domagal-Goldman
Figure caption: (a) A photochemical model of Jupiter (lines) compared to observations (circles). Table B4 lists the sources for all observations. (b) A climate simulation of Jupiter’s atmosphere (black line). Thick portions of the predicted P–T profiles indicate convection. The red dotted line is from the A. Seiff et al. (1998) profile based on Galileo probe measurements, and the cyan dotted line is a dry He/H2 adiabat. Photochem captures the main chemistry in Jupiter’s atmosphere and reproduces the planet’s P–T profile.
Paper: The Open-source Photochem Code: A General Chemical and Climate Model for Interpreting (Exo)Planet Observations
Authors: Nicholas F. Wogan, Natasha E. Batalha, Kevin Zahnle, Joshua Krissansen-Totton, David C. Catling, Eric T. Wolf, Tyler D. Robinson, Victoria Meadows, Giada Arney, Shawn Domagal-Goldman
Figure caption: Climate simulations of Titan’s atmosphere. (a) A climate model that uses the haze and gas concentrations predicted by the Figure 7 photochemical model. (b) The same as panel (a), except that the model replaces the computed haze with the observed M. G. Tomasko et al. (2008b) haze optical properties at solar wavelengths based on the Cassini/Huygens mission. (c) The same as panel (a), except that the model removes all haze opacity. In panels (a), (b), and (c), the top panels show predicted temperature profiles compared to the minimum and maximum of the J. H. Waite et al. (2013) empirical model (red shading). The bottom panels are the net thermal and solar fluxes compared to solar fluxes from Cassini/Huygens (Figure 3 in M. G. Tomasko et al. 2008a). Our climate model can reproduce Titan’s energy balance (panel (b)) when we correct for the inaccurate haze opacities predicted by the photochemical model.
Paper: The Open-source Photochem Code: A General Chemical and Climate Model for Interpreting (Exo)Planet Observations
Authors: Nicholas F. Wogan, Natasha E. Batalha, Kevin Zahnle, Joshua Krissansen-Totton, David C. Catling, Eric T. Wolf, Tyler D. Robinson, Victoria Meadows, Giada Arney, Shawn Domagal-Goldman
Figure caption: Photochemical simulations of WASP-39b’s atmosphere at the evening terminator. (a) A model using Photochem with our nominal chemical network (Section 2.2.2). (b) Calculations with Photochem (thin solid lines) and VULCAN (dotted lines) where both codes use a VULCAN chemical network, photolysis cross sections, and thermodynamics. With our nominal chemistry Photochem reproduces the observed SO2 in WASP-39b’s atmosphere (S.-M. Tsai et al. 2023), and Photochem and VULCAN predict a very similar atmospheric composition for the same inputs.
Paper: The Open-source Photochem Code: A General Chemical and Climate Model for Interpreting (Exo)Planet Observations
Authors: Nicholas F. Wogan, Natasha E. Batalha, Kevin Zahnle, Joshua Krissansen-Totton, David C. Catling, Eric T. Wolf, Tyler D. Robinson, Victoria Meadows, Giada Arney, Shawn Domagal-Goldman
Figure caption: (a) A photochemical model of Mars (lines) compared to observations (circles). Table B4 lists the sources for all observations. The dashed blue line is the saturation mixing ratio of H2O. (b) Three climate simulations of Mars representing the clear season (blue line), dusty season (brown line), and a global dust storm (black line). The red dotted line is a ±45° latitude average from the Ames Legacy Mars GCM (M. A. Kahre et al. 2023). Thick portions of the predicted P–T profiles indicate convection. The Photochem code generally reproduces Mars composition (panel (a)) and climate (panel (b)).
Paper: The Open-source Photochem Code: A General Chemical and Climate Model for Interpreting (Exo)Planet Observations
Authors: Nicholas F. Wogan, Natasha E. Batalha, Kevin Zahnle, Joshua Krissansen-Totton, David C. Catling, Eric T. Wolf, Tyler D. Robinson, Victoria Meadows, Giada Arney, Shawn Domagal-Goldman
Figure caption: A photochemical simulation of Titan’s atmosphere (lines) compared to observations (circles and shading). Some observations do not have error bars because no error was reported. The shading in panel (a) outlines the minimum and maximum H2 and CH4 concentrations from the J. H. Waite et al. (2013) empirical model of Titan. Table B4 lists the sources for all observations. For all species except NH3, the model is within a factor of several of observed concentrations.
Paper: The Open-source Photochem Code: A General Chemical and Climate Model for Interpreting (Exo)Planet Observations
Authors: Nicholas F. Wogan, Natasha E. Batalha, Kevin Zahnle, Joshua Krissansen-Totton, David C. Catling, Eric T. Wolf, Tyler D. Robinson, Victoria Meadows, Giada Arney, Shawn Domagal-Goldman
Figure caption: Photochemical simulation of modern Earth’s atmosphere (lines) compared to observations (circles). Table B4 lists the sources for all observations. For observations that do not give an error bar, we assume an error of one order of magnitude to account for diurnal and spatial variations following S.-M. Tsai et al. (2021). Photochem broadly reproduces the observed composition of Earth’s atmosphere.
Paper: The Open-source Photochem Code: A General Chemical and Climate Model for Interpreting (Exo)Planet Observations
Authors: Nicholas F. Wogan, Natasha E. Batalha, Kevin Zahnle, Joshua Krissansen-Totton, David C. Catling, Eric T. Wolf, Tyler D. Robinson, Victoria Meadows, Giada Arney, Shawn Domagal-Goldman
Figure caption: A benchmark between the climate model in Photochem and the HELIOS GPU code for a CO2–H2O atmosphere (50% each) on an Earth-sized planet orbiting the Sun at 0.5 au. Three cases are shown: HELIOS with HELIOS k-distributions (red dotted line), Photochem with HELIOS k-distributions (black line), and Photochem with Photochem k-distributions (blue line). See text for more benchmark details. The acronyms “RCE” and “RT” in the legend stand for “radiative-convective equilibrium” and “radiative transfer,” respectively. Both codes produce very similar P–T profiles, and Photochem’s root-finding approach (Section 2.3) reaches RCE in ∼6× fewer radiative transfer calls.
Paper: The Open-source Photochem Code: A General Chemical and Climate Model for Interpreting (Exo)Planet Observations
Authors: Nicholas F. Wogan, Natasha E. Batalha, Kevin Zahnle, Joshua Krissansen-Totton, David C. Catling, Eric T. Wolf, Tyler D. Robinson, Victoria Meadows, Giada Arney, Shawn Domagal-Goldman
Figure caption: Climate simulations of modern Earth’s atmosphere with 193 and 500 DU of ozone (black and gray lines, respectively) compared to temperature profiles at the equator and 45°N (red and blue lines, respectively) in January from CIRA-86. Thick portions of the predicted P–T profiles indicate convection. Our model broadly reproduces Earth’s P–T profile.
Paper: The Open-source Photochem Code: A General Chemical and Climate Model for Interpreting (Exo)Planet Observations
Authors: Nicholas F. Wogan, Natasha E. Batalha, Kevin Zahnle, Joshua Krissansen-Totton, David C. Catling, Eric T. Wolf, Tyler D. Robinson, Victoria Meadows, Giada Arney, Shawn Domagal-Goldman
Figure caption: A self-consistent photochemical-climate simulation of the modern Earth (solid lines) compared to photochemical and climate simulations described in Section 3.2 that are not self-consistent (dashed lines). Temperatures (black lines) are referenced to the top axis, and mixing ratios (colored lines) are referenced to the bottom axis.
Paper: The Open-source Photochem Code: A General Chemical and Climate Model for Interpreting (Exo)Planet Observations
Authors: Nicholas F. Wogan, Natasha E. Batalha, Kevin Zahnle, Joshua Krissansen-Totton, David C. Catling, Eric T. Wolf, Tyler D. Robinson, Victoria Meadows, Giada Arney, Shawn Domagal-Goldman
Figure caption: The temperature and eddy diffusion profiles used in our photochemical simulations of Venus, Earth, Mars, Titan, Jupiter, and WASP-39b.
Paper: Revisiting Electronic and Nuclear Sputtering from Ions at Mercury Using Linear Cascade Theory
Authors: Orenthal J. Tucker, Liam S. Morrissey, Rosemary M. Killen, Matthew H. Burger, Ronald J. Vervack Jr., Daniel W. Savin
Figure caption: Comparison of the nuclear Na yield plotted as a function of Na SBE for (a) 1 keV H ions and (b) 4 keV He ions impacting albite, as obtained from LCT here and in M86 and from SDTrimSP BCA simulations (L. S. Morrissey et al. 2024). The gray-shaded regions for the LCT results and the error bars for the SDTrimSP results indicate the range of sputtering yields for the MESSENGER-derived Na surface fraction of f Na = 0.02–0.05. See text for details.
Paper: Revisiting Electronic and Nuclear Sputtering from Ions at Mercury Using Linear Cascade Theory
Authors: Orenthal J. Tucker, Liam S. Morrissey, Rosemary M. Killen, Matthew H. Burger, Ronald J. Vervack Jr., Daniel W. Savin
Figure caption: The nuclear (solid line) and electronic (dashed line) stopping-power cross sections for H (left) and He (right) obtained for albite using SRIM 2008.
Paper: Revisiting Electronic and Nuclear Sputtering from Ions at Mercury Using Linear Cascade Theory
Authors: Orenthal J. Tucker, Liam S. Morrissey, Rosemary M. Killen, Matthew H. Burger, Ronald J. Vervack Jr., Daniel W. Savin
Figure caption: The Na sputtering yield evaluated using LCT as a function of incident ion energy for H and He ions evaluated using Equations (15) and (16) and compared to M86. Equations (15) and (16) were evaluated using the parameters in Table 1. For our comparison to M86, we also used SBE = 2 eV.
Paper: Revisiting Electronic and Nuclear Sputtering from Ions at Mercury Using Linear Cascade Theory
Authors: Orenthal J. Tucker, Liam S. Morrissey, Rosemary M. Killen, Matthew H. Burger, Ronald J. Vervack Jr., Daniel W. Savin
Figure caption: Ratio of the SRIM 2008 to M86 values for the nuclear (solid line) and electronic (dashed curve) stopping energy cross sections in albite for H (left) and He (right).
Paper: Isolating the Gravitational Influence of Uranus’s Winds Requires Close Passages Inward of the Rings
Authors: Christopher R. Mankovich, Marzia Parisi, Damon F. Landau, Janosz W. Dewberry
Figure caption: Panel (a) shows gravity moments for the full sample of models constrained by J2 and J4, assuming wind profile Asym. 5, with color mapped to the spin depth d (see inset color bar). For each value of n, the points are also slightly spread out horizontally so that wind depth d increases linearly from left to right within a single cluster. Odds are plotted with smaller symbols. The colored curves mark 3σ sensitivity for candidate UOP trajectories. J14 and F24 mark the 3σ uncertainties on the measured moments J2 and J4 (R. A. Jacobson 2014; R. G. French et al. 2024). The y-axis switches from logarithmic to linear scale in the range ±10−10 (gray shaded region). Panel (b) compares different samples obtained assuming each of the four wind profiles in turn; see Table 1. Here for each n value, the points are spread horizontally such that the depth increases linearly from 0.01Req = 256 km at ﹩n-\frac{1}{2}﹩ to 0.10Req =2560 km at ﹩n+\frac{1}{2}﹩. Black points are omitted at odd n, where even winds generate no moment. At even n, black and blue points are obscured by red points, since the differences between these fits are purely odd and hence do not affect the even Jn.
Paper: Isolating the Gravitational Influence of Uranus’s Winds Requires Close Passages Inward of the Rings
Authors: Christopher R. Mankovich, Marzia Parisi, Damon F. Landau, Janosz W. Dewberry
Figure caption: Comparison of the baseline retrieval to J2 and J4 (black points and histograms) with two retrievals incorporating possible measurements of J8 with values given in the legend (pink and green points/histograms). Results are shown in the space of central density versus wind truncation depth. The 0.03 × 10−7 uncertainties quoted for J8 in the legend reflect the 1σ standard deviation of the Gaussian likelihood in J8.
Paper: Isolating the Gravitational Influence of Uranus’s Winds Requires Close Passages Inward of the Rings
Authors: Christopher R. Mankovich, Marzia Parisi, Damon F. Landau, Janosz W. Dewberry
Figure caption: As in Figure 7 but showing the moment of inertia about Uranus’s spin axis as a function of the central ice fraction (Equation (4)) from simulated retrievals to different data. Superunity ice fractions Xice > 1 correspond to densities lower than that of the adopted ice mixture (see text), suggesting, for example, dilution by hydrogen. Values close to zero correspond to expectations for pure rock, with values less than zero indicative of a higher fraction of heavier elements like iron. A measurement of J8 leads to a stronger preference for ice- or rock-dominated cores, with the larger magnitude for J8 (green distribution) ruling out ice-dominated compositions entirely. Panels (a)–(c) show the same samples as in Figure 7; panels (d)–(f) show the corresponding samples that hold the bulk spin period fixed at 17.25 hr.
Paper: Isolating the Gravitational Influence of Uranus’s Winds Requires Close Passages Inward of the Rings
Authors: Christopher R. Mankovich, Marzia Parisi, Damon F. Landau, Janosz W. Dewberry
Figure caption: J3 as a function of wind depth (a) and J3 histograms (b) for three wind profiles with different degrees of equatorial asymmetry. Horizontal lines mark the ﹩3{\sigma }_{{J}_{3}}﹩ detection limits for the five trajectories in Table 2; in each scenario, points falling below the line would evade detection. The assumed wind profile bears strongly on the value of J3, making it difficult to uniquely constrain the wind depth using J3 alone.