adsabs/AstroCLIMB huggingface dataset:
Paper: Seasonal Evolution of Titan’s Stratospheric Tilt and Temperature Field at High Resolution from Cassini/CIRS
Authors: Lucy Wright, Nicholas A. Teanby, Patrick G. J. Irwin, Conor A. Nixon, Nicholas A. Lombardo, Juan M. Lora, Daniel Mitchell
Figure caption: Same as Figure 4, but in the solar-fixed frame.
Paper: Seasonal Evolution of Titan’s Stratospheric Tilt and Temperature Field at High Resolution from Cassini/CIRS
Authors: Lucy Wright, Nicholas A. Teanby, Patrick G. J. Irwin, Conor A. Nixon, Nicholas A. Lombardo, Juan M. Lora, Daniel Mitchell
Figure caption: Scaled PV (“Lait PV” = PV ×(Θ/100 K)−7/2) determined from observations. PV is plotted against potential temperature and is scaled by a factor of the potential temperature field, Θ, to reduce the vertical variation of PV while preserving its horizontal structure and conservative properties. PV is calculated from our zonal wind and potential temperature determined from the observed zonal mean temperature field. The PV field is calculated from the temperature field smoothed with cubic B-splines with a knot spacing of 10°. Regions where the scaled PV error exceeds the contour interval (>5 × 10−5 m2 K kg−1 s−1) have been removed. Right: Lait PV is plotted on isentropic surfaces: Θ = 350, 700, and 1000 K, which correspond to approximately 1, 0.1, and 0.01 mbar, respectively, in Titan’s atmosphere (solid lines). Shaded regions show the error on the Lait PV. An additional smoothing has been applied to the Lait PV line plots (right), by fitting cubic B-splines (N. A. Teanby 2007) with a knot spacing of 5°. The sign of PV in the southern hemisphere has been inverted so that the hemispheres can be directly compared on the plot.
Paper: Seasonal Evolution of Titan’s Stratospheric Tilt and Temperature Field at High Resolution from Cassini/CIRS
Authors: Lucy Wright, Nicholas A. Teanby, Patrick G. J. Irwin, Conor A. Nixon, Nicholas A. Lombardo, Juan M. Lora, Daniel Mitchell
Figure caption: Zonal mean temperature field in the stratosphere, averaged in 1-Earth-year bins. Left: observed temperature field in this study. Right: zonal mean temperature simulated by TAM (N. A. Lombardo & J. M. Lora 2023b, 2023a). The mean optimal stratospheric tilt found in Section 5.1, fixed in the inertial frame, has been applied to the observed temperature field (left). The TAM data are plotted over a pressure range corresponding to the vertical extent of the stratosphere and mesosphere probed by the CIRS data.
Paper: Seasonal Evolution of Titan’s Stratospheric Tilt and Temperature Field at High Resolution from Cassini/CIRS
Authors: Lucy Wright, Nicholas A. Teanby, Patrick G. J. Irwin, Conor A. Nixon, Nicholas A. Lombardo, Juan M. Lora, Daniel Mitchell
Figure caption: Eastward zonal mean wind speed, u. Left: wind speeds calculated using the TWE and the observed mean zonal temperature field. Right: wind speeds simulated using TAM (N. A. Lombardo & J. M. Lora 2023b, 2023a). The wind field has been averaged in 1-Earth-year bins. Wind speeds are calculated from the temperature field smoothed with cubic B-splines with a knot spacing of 10° (left) and assuming that the zonal wind speed at 10 mbar is 4 times Titan’s solid-body rotation rate. The TWE is unconstrained near the equator, and the zonal wind speed cannot be determined in some regions, typically due to significant noise in the data or lack of observation coverage. Regions where the wind speed error exceeds the contour interval (>10 m s−1) have not been plotted. The TAM data are plotted over a pressure range corresponding to the vertical extent of the stratosphere and mesosphere probed by the CIRS data.
Paper: Seasonal Evolution of Titan’s Stratospheric Tilt and Temperature Field at High Resolution from Cassini/CIRS
Authors: Lucy Wright, Nicholas A. Teanby, Patrick G. J. Irwin, Conor A. Nixon, Nicholas A. Lombardo, Juan M. Lora, Daniel Mitchell
Figure caption: Time series of the optimal tilt magnitude (left panels) and direction (right panels) for each 2 yr bin. The optimal tilt is that which minimizes the spread of temperature–latitude distributions within each 2 yr bin. Results from previous studies are plotted for comparison, although these vary slightly in altitude: J. Sharkey et al. (2020; S20), ∼1 mbar (∼190 km); R. A. West et al. (2016; W16), ∼0.08 mbar (∼300 km); R. K. Achterberg et al. (2008a; A08b), ∼1 mbar (∼190 km); N. W. Kutsop et al. (2022; K22 using north polar haze annulus (NP) and equatorial haze annulus (EQ)), at pressures ≲8 mbar (∼100 km); R. K. Achterberg et al. (2011; A11), ∼1 mbar (∼190 km); M. T. Roman et al. (2009; R09), at ∼15 mbar (∼80 km); C. Snell & D. Banfield (2024; SB24), ∼70–0.6 mbar (∼50–200  km). A linear fit is plotted for the tilt direction in the solar- and inertial-fixed frames (black dotted lines), and a sinusoid is fit to the tilt magnitude in the solar- and inertial-fixed frames (gray dotted line), ignoring three outliers.
Paper: Seasonal Evolution of Titan’s Stratospheric Tilt and Temperature Field at High Resolution from Cassini/CIRS
Authors: Lucy Wright, Nicholas A. Teanby, Patrick G. J. Irwin, Conor A. Nixon, Nicholas A. Lombardo, Juan M. Lora, Daniel Mitchell
Figure caption: Grid search for optimal tilt magnitude and direction in the Saturn (left), solar (middle), and inertial-fixed (right) reference frames. χ2 quantifies the spread of temperature–latitude distributions at the 1 mbar pressure level. χ2 contours are shown here averaged over the full mission (2004–2017). The location of the minimum χ2 value is indicated with a red cross, and the 1σ (red) and 3σ (orange) confidence bounds are plotted. Results from previous studies are plotted for comparison: J. Sharkey et al. (2020; S20), R. A. West et al. (2016; W16), R. K. Achterberg et al. (2008a; A08b), and M. T. Roman et al. (2009; R09).
Paper: Seasonal Evolution of Titan’s Stratospheric Tilt and Temperature Field at High Resolution from Cassini/CIRS
Authors: Lucy Wright, Nicholas A. Teanby, Patrick G. J. Irwin, Conor A. Nixon, Nicholas A. Lombardo, Juan M. Lora, Daniel Mitchell
Figure caption: Schematic of zonal jets observed in the mean zonal wind field (left) and interpretation of the Lait PV structure in relation to Titan’s polar vortices (right). The equatorial region is unconstrained. A purple ring indicates an annular vortex, and a solid oval indicates a conventional vortex shape. Jet and vortex strengths are indicated by color opacity: higher opacity indicates faster zonal winds (blue) or higher Lait PV (purple). Due to limited coverage of the highest latitudes, we cannot conclude whether the Lait PV structure is annular at some times.
Paper: Seasonal Evolution of Titan’s Stratospheric Tilt and Temperature Field at High Resolution from Cassini/CIRS
Authors: Lucy Wright, Nicholas A. Teanby, Patrick G. J. Irwin, Conor A. Nixon, Nicholas A. Lombardo, Juan M. Lora, Daniel Mitchell
Figure caption: Schematic showing the direction of Titan’s stratospheric tilt axis determined in this work, throughout the Cassini mission. Titan is shown at some example times in its orbit, during the time span of the Cassini mission (2004–2017). The tilt direction is determined to be approximately constant in the inertial reference frame, that is, fixed with respect to the Titan–Sun vector at the northern spring (vernal) equinox (Ls = 0°). The approximate size of the tilt magnitude, β, is indicated by font size.
Paper: Seasonal Evolution of Titan’s Stratospheric Tilt and Temperature Field at High Resolution from Cassini/CIRS
Authors: Lucy Wright, Nicholas A. Teanby, Patrick G. J. Irwin, Conor A. Nixon, Nicholas A. Lombardo, Juan M. Lora, Daniel Mitchell
Figure caption: Same as Figure 4, but in the inertial-fixed frame.
Paper: The Effect of the Io Plasma Torus on Precise Orbit Determination and Gravity Recovery: Application to Europa Clipper
Authors: Gael Cascioli, Erwan Mazarico, Flavio Petricca, Andrea Magnanini, Dustin R. Buccino, Martina Ciambellini, Antonio Genova, Luis Gomez Casajus, Marco Zannoni, Paolo Tortora
Figure caption: Trajectory error for each flyby. Both plots show the rms trajectory error for different cases. The top panel reports the trajectory error normalized by the trajectory uncertainty (indicated by σ) together with its standard deviation (error bars), while the bottom panel shows the absolute value. The black lines show the error when the IPT perturbation is fully calibrated (same as in Figure 7). The red and yellow lines are obtained by calibrating the perturbations with the Phipps+18t G and Moirano+21t models, respectively. The trajectory errors are worse when the compensation is given by the Moirano+21t model because this latter is structurally different from the model used as ground truth (Phipps+18t B). The horizontal black line in the top panel highlights the 3σ level.
Paper: The Effect of the Io Plasma Torus on Precise Orbit Determination and Gravity Recovery: Application to Europa Clipper
Authors: Gael Cascioli, Erwan Mazarico, Flavio Petricca, Andrea Magnanini, Dustin R. Buccino, Martina Ciambellini, Antonio Genova, Luis Gomez Casajus, Marco Zannoni, Paolo Tortora
Figure caption: Ground tracks of the 49 Europa Clipper flybys, color-coded by the IPT Doppler perturbation magnitude. The tracks are shown in a Mollweide equal-area projection centered at 180° longitude.
Paper: The Effect of the Io Plasma Torus on Precise Orbit Determination and Gravity Recovery: Application to Europa Clipper
Authors: Gael Cascioli, Erwan Mazarico, Flavio Petricca, Andrea Magnanini, Dustin R. Buccino, Martina Ciambellini, Antonio Genova, Luis Gomez Casajus, Marco Zannoni, Paolo Tortora
Figure caption: Trajectory reconstruction error due to model parameters uncertainties. For the three investigated cases (see text Section 4.3) we report the mean error and the standard deviation spread around the mean. These have been obtained by running 20 OD solutions for each investigated case (1σ, 2σ, and 3σ).
Paper: The Effect of the Io Plasma Torus on Precise Orbit Determination and Gravity Recovery: Application to Europa Clipper
Authors: Gael Cascioli, Erwan Mazarico, Flavio Petricca, Andrea Magnanini, Dustin R. Buccino, Martina Ciambellini, Antonio Genova, Luis Gomez Casajus, Marco Zannoni, Paolo Tortora
Figure caption: Gravity anomaly reconstruction error normalized by the formal uncertainty. This is the average reconstruction error when calibrating the tracking data with the Moirano+21t model perturbed at the 3σ level. The 1σ and 2σ results are similar and not reported here.
Paper: The Effect of the Io Plasma Torus on Precise Orbit Determination and Gravity Recovery: Application to Europa Clipper
Authors: Gael Cascioli, Erwan Mazarico, Flavio Petricca, Andrea Magnanini, Dustin R. Buccino, Martina Ciambellini, Antonio Genova, Luis Gomez Casajus, Marco Zannoni, Paolo Tortora
Figure caption: Gravity field spherical harmonics coefficients' reconstruction error normalized by their formal uncertainty (σ) (see Figure 8).
Paper: The Effect of the Io Plasma Torus on Precise Orbit Determination and Gravity Recovery: Application to Europa Clipper
Authors: Gael Cascioli, Erwan Mazarico, Flavio Petricca, Andrea Magnanini, Dustin R. Buccino, Martina Ciambellini, Antonio Genova, Luis Gomez Casajus, Marco Zannoni, Paolo Tortora
Figure caption: Gravity anomaly uncertainty.
Paper: The Effect of the Io Plasma Torus on Precise Orbit Determination and Gravity Recovery: Application to Europa Clipper
Authors: Gael Cascioli, Erwan Mazarico, Flavio Petricca, Andrea Magnanini, Dustin R. Buccino, Martina Ciambellini, Antonio Genova, Luis Gomez Casajus, Marco Zannoni, Paolo Tortora
Figure caption: Gravity anomaly error.
Paper: An Assessment of Organics Detection and Characterization on the Surface of Europa with Infrared Spectroscopy
Authors: Ishan Mishra, Nikole Lewis, Jonathan Lunine, Kevin P. Hand
Figure caption: Left panel: simulated, noise-free spectra of water ice (dashed dark-blue curve) and selected trace species of interest. Middle panel: simulated spectra of non-water-ice species mixed with water ice, at an abundance fraction of 5%. The dashed dark-blue curve is a pure water-ice spectrum for reference. Right panel: zoom-in of the (a) 3.2–3.6 μm and (b) 3.8–5.0 μm regions of four spectra from the middle panel. While organics are spectrally rich in the 3–5 μm wavelength range, on Europa we expect them to be present in trace amounts in a background of water ice, which leads to weak absorption signatures (on the order of a few percent in strength) on top of a mostly water-ice-rich continuum. Other heavily hydrated, non-ice materials may also be plausible background components, and exploring their effects on detectability is an interesting direction for future work. The key physical parameters used in these simulations are as follows: incidence angle = emergence angle = 45∘; phase angle = 90∘; average grain size = 50 μm. Reflectance is expressed in units of radiance factor.
Paper: An Assessment of Organics Detection and Characterization on the Surface of Europa with Infrared Spectroscopy
Authors: Ishan Mishra, Nikole Lewis, Jonathan Lunine, Kevin P. Hand
Figure caption: Reflectance spectrum models of two-component mixtures of water ice (80% abundance by number) and the labeled species (20% abundance by number) are shown in blue. The orange curve in each panel shows a pure water-ice spectrum for reference, while the green line is the residual, i.e., difference, between the blue and the orange models. The dashed gray line marks the zero reflectance level, as a reference for the residual curve. Qualitatively, large and “feature-rich” deviations of the residual curve from zero indicate a more dominant trace species, such as C2H6. The sharp features in the residual curve also highlight where the major absorption features of the trace species are present, since water’s features are pretty broad. All models are at MISE’s spectral resolution of 10 nm. Note: while the full 3–5 μm spectral range was used in all analyses, absorption features near 3.16 and 3.33 μm were not included in the σfs calculations owing to overlap with broad water-ice bands (see Table 1).
Paper: An Assessment of Organics Detection and Characterization on the Surface of Europa with Infrared Spectroscopy
Authors: Ishan Mishra, Nikole Lewis, Jonathan Lunine, Kevin P. Hand
Figure caption: Simulated spectrum of water ice mixed with 8 trace organic species (Table 1), CO2 and SO2, at an SNR of 100 and at MISE’s spectral resolution of 10 nm. Each of the 10 trace species has 1% abundance; hence, the mixture is 90% water ice and 10% non–water ice. Arrows point to two absorption features in the data near 4.25 and 4.42 μm, each with multiple possible trace species candidates. The 4.25 μm feature could also arise from CO2, which has been detected on Europa. This example illustrates how the presence of overlapping features can lead to spectral confusion and underscores the importance of using statistical model comparison to evaluate candidate species.
Paper: An Assessment of Organics Detection and Characterization on the Surface of Europa with Infrared Spectroscopy
Authors: Ishan Mishra, Nikole Lewis, Jonathan Lunine, Kevin P. Hand
Figure caption: Left panel: a “detectability heatmap” of the σfs metric of detection of HCN in a simulated spectrum of water and HCN mixture, derived from the average strength of the prominent absorption features of HCN (see Section 2.2). The abundance fraction of HCN is on the x-axis, and the SNR of the simulated data is on the y-axis. The white dashed lines mark the limits of expected SNR for Europa Clipper’s MISE spectrometer in the 3–5 μm wavelength range. The 3σ and 6σ contours, conventional limits for tentative and strong detection, respectively, are shown with black dotted lines. Right: detectability heatmap of the σbmc metric for detection of HCN in simulated data of water and HCN mixture, calculated using the BMC approach, as described in Section 2.3. The BMC-based detection pushes the 3σ and 6σ threshold to lower HCN abundances, closer to ∼1%, which is typically the organic abundance on icy surfaces.
Paper: An Assessment of Organics Detection and Characterization on the Surface of Europa with Infrared Spectroscopy
Authors: Ishan Mishra, Nikole Lewis, Jonathan Lunine, Kevin P. Hand
Figure caption: Best-fit or maximum likelihood solutions of a water+HCN model fit (left panel) and a water-only model fit (right panel) to simulated data (blue points). The simulated data are for a water and HCN mixture with 5% abundance fraction of HCN. Note that the Hapke model has been used to generate simulated data (see Figure 2), as well as for the model fits. The fit in the right panel is clearly inferior to the fit in the left panel, as illustrated by the residuals. Notably, the residuals in the right panel are poor and have a systematic trend caused by the missing HCN. The reduced χ2 values of the fits are also noted, which also indicate that the fit in the left panel is better. This example assumes perfect knowledge of the spectral end-members, which allows the correct model to fit the data extremely well. In real-world cases, imperfect end-members (e.g., wrong temperature or phase) may lead to residuals even in correct models, and such mismatches can provide valuable diagnostic information.
Paper: An Assessment of Organics Detection and Characterization on the Surface of Europa with Infrared Spectroscopy
Authors: Ishan Mishra, Nikole Lewis, Jonathan Lunine, Kevin P. Hand
Figure caption: Left panel: a reflectance spectrum model of water ice and HCN mixture is shown, with the abundance (by number) fraction of HCN (f_hcn) = 10%. Right panel: spectroscopic data simulated using the model shown in the left panel, by adding Gaussian noise to it at SNR = 50 and binning the model down to the 10 nm spectral resolution (Δλ) of MISE. The dashed vertical red lines indicate the locations of prominent HCN features that are also clearly visible in this MISE quality data. The physical parameters used in the model are as follows: incidence angle = emergence angle = 45∘; phase angle = 90∘; average grain size = 50 μm. Reflectance units is radiance factor.
Paper: An Assessment of Organics Detection and Characterization on the Surface of Europa with Infrared Spectroscopy
Authors: Ishan Mishra, Nikole Lewis, Jonathan Lunine, Kevin P. Hand
Figure caption: A collage of heatmaps for the σbmc metric for various trace organic species, in a two-component mixture with water ice, as in Figure 6, but now evaluated via the BMC approach (Section 3.2). All species have improved detectability as compared to the σfs metric of Figure 6. The latter only relies on the strength of the most prominent absorption features of a trace species, missing out on its effect at other wavelengths, which is picked up by the BMC approach.
Paper: An Assessment of Organics Detection and Characterization on the Surface of Europa with Infrared Spectroscopy
Authors: Ishan Mishra, Nikole Lewis, Jonathan Lunine, Kevin P. Hand
Figure caption: The process of determining evidence of a species in spectroscopic data using BMC. As described in Section 2.3, a Bayesian inference exercise allows us to calculate the Bayesian evidence ﹩{ \mathcal Z }﹩ for any model, i.e., the model’s probability given the data. We compare the ﹩{ \mathcal Z }﹩ values of two models: Model 1 with all our candidate species, and Model 2 with our species of interest X excluded. If Model 1’s ﹩{ \mathcal Z }﹩ is greater than Model 2’s ﹩{ \mathcal Z }﹩, then we conclude that the presence of species X in Model 1 is helping it outperform Model 2, and hence there is evidence for species X at an nσ significance. The value of n will be higher if the difference in the ﹩{ \mathcal Z }﹩ values of the two models is higher. On the other hand, if Model 2’s ﹩{ \mathcal Z }﹩ is greater than Model 1’s ﹩{ \mathcal Z }﹩, then we conclude that the exclusion of X in Model 2 helped it outperform Model 1 and thus there is no evidence for species X.
Paper: An Assessment of Organics Detection and Characterization on the Surface of Europa with Infrared Spectroscopy
Authors: Ishan Mishra, Nikole Lewis, Jonathan Lunine, Kevin P. Hand
Figure caption: Simulated spectra of CO2 (left) and SO2 (right) mixed with water ice (blue) and their residuals (green) with a pure water-ice spectrum (orange), as in Figure 5. CO2’s prominent features at 4.19, 4.38, 4.29, 4.90, and 4.06 μm and the SO2’s prominent features at 4.06 and 4.36 μm stand out in the residual curves.
Paper: An Assessment of Organics Detection and Characterization on the Surface of Europa with Infrared Spectroscopy
Authors: Ishan Mishra, Nikole Lewis, Jonathan Lunine, Kevin P. Hand
Figure caption: A collage of σfs heatmaps, as in Figure 4, of various trace organic species in a two-component model with water ice. The spectroscopic features used for each species in the calculation of the σfs metric are shown in Table 1. The white dashed lines mark the limits of expected SNR for MISE data in the 3–5 μm, and the black dashed lines mark the 3σ and 6σ contours.
Paper: Thermal Inertia Controls on Titan’s Surface Temperature and Planetary Boundary Layer Structure
Authors: Sooman Han, Juan M. Lora
Figure caption: Surface energy balance components and temperature variations at diurnal timescales at the model grid point nearest the Huygens landing site (167.7°E, 10.3°S) shown for four simulation cases with varying surface thermal inertia values. Panel (a): Case 1 (I = 173 TIU); (b) Case 2 (I = 548 TIU); (c) Case 3 (I = 1732 TIU); and (d) Case 4 (I = 601 TIU, where plains terrain type is assumed; S. M. MacKenzie et al. 2019). TIU denotes thermal inertia unit J m−2 s−0.5 K−1. Results are sampled at 16 Earth-hour intervals (i.e., 1/24 of a Titan day) during midsummer conditions (Ls = 300∘, where Ls is solar longitude) in the final simulation year. The time origin (t = 0) corresponds to local midnight. Net shortwave (SW) flux and net energy flux are defined as positive downward (into the surface), while net longwave (LW) flux and sensible heat flux are defined as positive upward (away from the surface). The simulated surface temperatures are approximately 2 K higher than the Huygens observations, primarily because our dry configuration lacks evaporative cooling. This warm bias persists throughout the simulations and is evident in other figures as well.
Paper: Thermal Inertia Controls on Titan’s Surface Temperature and Planetary Boundary Layer Structure
Authors: Sooman Han, Juan M. Lora
Figure caption: Simulated zonal-mean surface temperature as a function of latitude and time for four simulation cases. Cases 1–3 assume globally uniform thermal inertia, while Case 4 adopts a spatially varying thermal inertia map based on the convective lakes scenario from S. M. MacKenzie et al. (2019). Time is referenced to the southern spring equinox (t = 0 corresponds to Ls = 180°). Each panel displays the final 2 yr of a 200 yr simulation, reflecting equilibrated seasonal temperature patterns. The corresponding latitudes and seasonal timings of the Huygens and Dragonfly landing sites are marked with stars.
Paper: Thermal Inertia Controls on Titan’s Surface Temperature and Planetary Boundary Layer Structure
Authors: Sooman Han, Juan M. Lora
Figure caption: Similar to Figure 1, but for seasonal timescales, where diurnally averaged values are plotted. Results are taken from the final 2 yr of the 200 yr simulations, representing equilibrated seasonal cycles at the Huygens landing site. The seasonal phases corresponding to the Huygens and Dragonfly landing sites occur at t = 0.33.