adsabs/AstroCLIMB huggingface dataset:
Paper: Heat-flux-limited Cloud Activity and Vertical Mixing in Giant Planet Atmospheres with an Application to Uranus and Neptune
Authors: Huazhi Ge, 华志 葛, Cheng Li, Xi Zhang, Chris Moeckel
Figure caption: Horizontally averaged mass mixing ratio (i.e., specific humidity) of CH4 (blue lines) and H2S (red lines) vapor and snapshots with temporal and spatial variations (shaded region). Note that snapshots do not collect the specific humidity at every time step and may miss the variation caused by short-lived intermittent storms.
Paper: Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions
Authors: Bastian Knieling, Karsten Schindler, Amanda A. Sickafoose, Michael J. Person, Stephen E. Levine, Alfred Krabbe
Figure caption: Generated ECMs for Pluto. The red circles represent the input data for each model. The center-of-light ECM (left) shows the observed offsets by ZTF (red points) to the JPL reference ephemeris (black line). For the corrected ECM (right), the observed offsets were corrected by the estimated center-of-light offset as described in Section 3.3 before serving as input data. The error bars show the estimated 1σ uncertainty as described in Section 3.2.1. The prediction of the ECM for the 2022 August 23 Pluto occultation is the estimated offset to the JPL reference ephemeris (blue line). For better visibility, the plots show only the time period from 2022.35 to 2022.85.(The data used to create this figure are available.)
Paper: Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions
Authors: Bastian Knieling, Karsten Schindler, Amanda A. Sickafoose, Michael J. Person, Stephen E. Levine, Alfred Krabbe
Figure caption: Examples of synthetic light curves—i.e., normalized flux vs. time t—of an occultation by a body with a clear, isothermal atmosphere (gray), exhibiting different S/Ns. The midtime of the simulated occultation was arbitrarily set to t = 250 s. We subsequently modeled these synthetic data sets with GP regression (blue) to assess how well half-light times can be extracted. Unsurprisingly, the quality of the regression fits increases with S/N, i.e., the uncertainty of the estimated half-light times decreases.
Paper: Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions
Authors: Bastian Knieling, Karsten Schindler, Amanda A. Sickafoose, Michael J. Person, Stephen E. Levine, Alfred Krabbe
Figure caption: Simulated center-of-light measurements of Pluto and two satellites. We visualized the ability of the GP model to detect the correct periodicity as a function of magnitude difference and angular separation. The green segments mark the detection of the correct orbital period. For details, see text.
Paper: Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions
Authors: Bastian Knieling, Karsten Schindler, Amanda A. Sickafoose, Michael J. Person, Stephen E. Levine, Alfred Krabbe
Figure caption: Residuals of evaluation stars (=measured position − Gaia DR3 catalog position) and their distributions to estimate Gaussian errors of the astrometric solution by ZTF referenced to Gaia DR1 (left) and our astrometric solution referenced to Gaia DR3 (right). The mean proper motions in decl. of the evaluation stars are negative; since the ZTF solution shown on the left does not take proper motions into account (which were still missing in Gaia DR1), there is a systematic error in decl. This systematic error is reduced in our astrometric solution taking proper motions into account.
Paper: Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions
Authors: Bastian Knieling, Karsten Schindler, Amanda A. Sickafoose, Michael J. Person, Stephen E. Levine, Alfred Krabbe
Figure caption: Prediction of a stellar occultation by Pluto on 2022 August 23 UT based on JPL DE440, PLU058, and our ECM. The provided time and shadow velocity are calculated for the geocentric closest approach (black circle on the center line). The middle line represents the center of the shadow path, the outer solid lines Pluto’s solid-body radius, and the dashed lines the 3σ confidence interval of our prediction (not considering refraction by Pluto’s atmosphere). The light- and dark-gray regions indicate day and night (Sun above/below 0° elevation). The sites that observed the event are each marked by a white cross.
Paper: Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions
Authors: Bastian Knieling, Karsten Schindler, Amanda A. Sickafoose, Michael J. Person, Stephen E. Levine, Alfred Krabbe
Figure caption: Generation of a GP model, where x is the independent variable and y is the observed value. The top panel shows samples (blue lines) of an unconditioned GP model using the GK as the covariance function, i.e., the prior distribution. The middle panel shows the samples of the same GP model after it was fitted to a data set (red circles), i.e., the posterior distribution. The bottom panel shows the underlying posterior distribution of the middle panel, which is eventually used as the regression model. The blue line represents the expectation value, and the light-blue area visualizes one standard deviation.
Paper: Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions
Authors: Bastian Knieling, Karsten Schindler, Amanda A. Sickafoose, Michael J. Person, Stephen E. Levine, Alfred Krabbe
Figure caption: Comparison of the MPC and ZTF data sets of Pluto observations. The markers represent the observed residuals of R.A. and decl. to the JPL ephemerides. The MPC data set is shown in gray, and the ZTF data set is shown in red.
Paper: Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions
Authors: Bastian Knieling, Karsten Schindler, Amanda A. Sickafoose, Michael J. Person, Stephen E. Levine, Alfred Krabbe
Figure caption: Flow chart illustrating the automated RK-generating process. The final RK is generated iteratively by extending the fixed kernels—in this example only an NK, thus making no prior assumptions—with provided free kernels until the marginal likelihood ﹩{ \mathcal L }﹩ does not improve further.
Paper: Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions
Authors: Bastian Knieling, Karsten Schindler, Amanda A. Sickafoose, Michael J. Person, Stephen E. Levine, Alfred Krabbe
Figure caption: Difference between the estimated half-light times and the true simulated half-light time as a function of the S/N of the data set. The error bars represent 1σ uncertainties.
Paper: Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions
Authors: Bastian Knieling, Karsten Schindler, Amanda A. Sickafoose, Michael J. Person, Stephen E. Levine, Alfred Krabbe
Figure caption: Sky-plane view of the 2022 August 23 Pluto occultation with the best circle fit to the LT and T1T half-light times (for a definition of the coordinate system, see, e.g., Elliot et al. 1993). The best circle fit is shown as the solid black line, with its 1σ confidence region shown in gray. The observed occultation chords are shown in color, with the half-light locations plotted as black points with error bars representing 1σ uncertainties. A dotted–dashed line represents the estimated center line of the occultation shadow, and the arrow illustrates the star’s motion relative to Pluto, i.e., immersion is on the right and emersion is on the left. Due to the very low S/N of the T1T light curve and the resulting large uncertainties of the immersion and emersion times, we kept the circle radius fixed at R = 1213.55 km, i.e., assumed Pluto’s half-light radius as determined by A. Sickafoose et al. (2023), keeping only the circle center coordinates as free parameters in our fit. The distance of each chord to the derived center line is provided in parentheses in the figure legend.
Paper: Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions
Authors: Bastian Knieling, Karsten Schindler, Amanda A. Sickafoose, Michael J. Person, Stephen E. Levine, Alfred Krabbe
Figure caption: Chiron’s occultation on 2018 November 28 UT. We compare the predictions (a) based on Chiron’s JPL ephemeris version #149 and JPL DE440 and (b) based on our ECM to (c) the postdicted shadow path based on the observation by A. A. Sickafoose et al. (2023). The solid lines mark the northern and southern limits of the shadow path assuming an equivalent radius of R = 105 km (Lellouch et al. 2017), and the dotted line is the center line. The dashed lines indicate the 3σ errors of the predicted shadow path. For visibility, we cropped the maps to the area where the observatories are located; the 3σ errors of our ECM are outside the plot limits. Yet the postdiction path is consistent with a positive detection from the SAAO and a negative detection from Boyden, as reported in Braga-Ribas et al. (2023).
Paper: Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions
Authors: Bastian Knieling, Karsten Schindler, Amanda A. Sickafoose, Michael J. Person, Stephen E. Levine, Alfred Krabbe
Figure caption: Light curves of Pluto’s occultation on 2022 August 23 UT observed by T1T (left) and LT (right) from A. Sickafoose et al. (2023). The top panels visualize the normalized, background-fraction-corrected stellar flux. The middle panels show the trend detected in the first iteration of our modeling. The bottom panels show the modeled light curves based on the detrended flux. We changed the y-scale in each plot for better visibility.(The data used to create this figure are available.)
Paper: Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions
Authors: Bastian Knieling, Karsten Schindler, Amanda A. Sickafoose, Michael J. Person, Stephen E. Levine, Alfred Krabbe
Figure caption: Chiron’s occultation on 2019 September 8 UT. We compare the predictions (a) based on Chiron’s JPL ephemeris version #149 and JPL DE440 and (b) made with our ECM. Five stations participated in the observing campaign for this occultation: one in Spain, two in France, and two in Belgium. Only the station in Spain did not detect a positive occultation, which is consistent with the prediction with our ECM.
Paper: Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions
Authors: Bastian Knieling, Karsten Schindler, Amanda A. Sickafoose, Michael J. Person, Stephen E. Levine, Alfred Krabbe
Figure caption: Chiron’s occultation on 2022 December 15 UT. We compare the predictions (a) based on Chiron’s JPL ephemeris version #149 and JPL DE440 and (b) made with our ECM. Of the three observing stations, only the Wise Observatory reported a solid-body observation of the event.
Paper: Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions
Authors: Bastian Knieling, Karsten Schindler, Amanda A. Sickafoose, Michael J. Person, Stephen E. Levine, Alfred Krabbe
Figure caption: Generated ECM for Haumea. The top panel shows the prediction of the R.A. residuals, and the bottom panel shows the prediction of the decl. residuals with respect to Haumea’s JPL ephemeris version #116.(The data used to create this figure are available.)
Paper: Dynamic Secondary Illumination in Permanent Shadows within Artemis III Candidate Landing Regions
Authors: Prasun Mahanti, Jean-Pierre Williams, Mark S. Robinson, Robert Wagner, Erwan Mazarico, Megan Henriksen, Holly Brown, Nandita Kumari
Figure caption: Slope and ease of approachability to the least illuminated zones of the PSRs (A: PSR 1, B: PSR 2, C: PSR 3, and D: PSR 4). The yellow arrow shows the direction of the south pole. Spatial X- and Y-axes are in kilometer distance from the south pole. The filled contour plot indicates the slope computed over a 180 m × 180 m box. The slope color scale is in degrees; brighter colors indicate higher slopes, and darker shades indicate lower slopes. Green markers indicate the locations with the lowest levels of secondary illumination during summer (see Figure 5).
Paper: Dynamic Secondary Illumination in Permanent Shadows within Artemis III Candidate Landing Regions
Authors: Prasun Mahanti, Jean-Pierre Williams, Mark S. Robinson, Robert Wagner, Erwan Mazarico, Megan Henriksen, Holly Brown, Nandita Kumari
Figure caption: Diurnal variation of average secondary and primary illumination for the four PSRs for summer. The average primary illumination is 2–3 orders of magnitude higher than the secondary illumination at every point on the subsolar longitude axis. Blue arrows indicate the temporal location of the lowest secondary illumination value. Subsolar latitude classes for summer (midsummer, before/after midsummer, and early/late summer) are the same as in Figure 4.
Paper: Dynamic Secondary Illumination in Permanent Shadows within Artemis III Candidate Landing Regions
Authors: Prasun Mahanti, Jean-Pierre Williams, Mark S. Robinson, Robert Wagner, Erwan Mazarico, Megan Henriksen, Holly Brown, Nandita Kumari
Figure caption: Summer subsolar latitude variation for 2023–2029. The red dot indicates the position where the Sun is highest at the south pole. The ranges for midsummer, before and after midsummer, and early/late summer are indicated by red, orange, and yellow, respectively.
Paper: Dynamic Secondary Illumination in Permanent Shadows within Artemis III Candidate Landing Regions
Authors: Prasun Mahanti, Jean-Pierre Williams, Mark S. Robinson, Robert Wagner, Erwan Mazarico, Megan Henriksen, Holly Brown, Nandita Kumari
Figure caption: ShadowCam measurements of maximum illumination at the four PSRs overlaid on LROC NAC mosaics, cropped to 3 km distance from the PSR perimeter in all directions. Gray color scale is for the ShadowCam radiance within the PSR, and LROC NAC mosaic radiance is typically 10 times higher. Note (A) PSR 1 near de Gerlache crater, (B) PSR 2 in Marvin crater, (C) PSR 3 on Leibnitz plateau (Mons Mouton), and (D) PSR 4 in peak near Shackleton.
Paper: Dynamic Secondary Illumination in Permanent Shadows within Artemis III Candidate Landing Regions
Authors: Prasun Mahanti, Jean-Pierre Williams, Mark S. Robinson, Robert Wagner, Erwan Mazarico, Megan Henriksen, Holly Brown, Nandita Kumari
Figure caption: The mean sublimation rates for water from Schorghofer & Williams (2020) for the four PSRs (A: PSR 1, B: PSR 2, C: PSR 3, and D: PSR 4) in Figure 5.
Paper: Dynamic Secondary Illumination in Permanent Shadows within Artemis III Candidate Landing Regions
Authors: Prasun Mahanti, Jean-Pierre Williams, Mark S. Robinson, Robert Wagner, Erwan Mazarico, Megan Henriksen, Holly Brown, Nandita Kumari
Figure caption: The four PSRs (PSR shape outlined in yellow) at the five A3CLRs. Color-coded topographic elevation proximal to each PSR within an ROI (black square) box for each of the PSRs, the A3CLR regions are delimited with yellow square boxes. Secondary illumination analysis for each PSR is conducted for the ROI box map area.
Paper: Dynamic Secondary Illumination in Permanent Shadows within Artemis III Candidate Landing Regions
Authors: Prasun Mahanti, Jean-Pierre Williams, Mark S. Robinson, Robert Wagner, Erwan Mazarico, Megan Henriksen, Holly Brown, Nandita Kumari
Figure caption: The 13 A3CLRs with the five regions considered in this work (pink). PSR 3 is overlapped by edges of two regions in the Leibnitz Beta plateau. PSRs 1, 2, and 4 are also near the edges of the de Gerlache Rim 2, Connecting Ridge Extension, and peak near Shackleton regions, respectively.
Paper: Dynamic Secondary Illumination in Permanent Shadows within Artemis III Candidate Landing Regions
Authors: Prasun Mahanti, Jean-Pierre Williams, Mark S. Robinson, Robert Wagner, Erwan Mazarico, Megan Henriksen, Holly Brown, Nandita Kumari
Figure caption: Outlines of the four PSRs in red overlaid on the LROC NAC-controlled mosaics at 20 m pixel scale. (A) PSR 1 is near de Gerlache crater, (B) PSR 2 is in Marvin crater, (C) PSR 3 is on Leibnitz plateau (Mons Mouton), and (D) PSR 4 is within the peak near Shackleton region.
Paper: Pre-impact Thermophysical Properties and the Yarkovsky Effect of NASA DART Target (65803) Didymos
Authors: Benjamin Rozitis, Simon F. Green, Samuel L. Jackson, Colin Snodgrass, Cyrielle Opitom, Thomas G. Müller, Ulrich C. Kolb, Steven R. Chesley, R. Terik Daly, Cristina A. Thomas, Andrew S. Rivkin
Figure caption: Global temperature distributions for the Didymos primary body. (a), (b) Maximum surface temperature attained at any point during its rotation and orbit. (c), (d) Time-averaged temperature computed from all rotations throughout its orbit. The highlighted facets have average temperatures below 145 K where subsurface water ice is potentially stable over geologic timescales if present. (e), (f) Minimum surface temperature attained at any point during its rotation and orbit. In all panels, the temperatures have been computed for the smooth surface model component assuming a thermal inertia of 320 J m−2 K−1 s−1/2.
Paper: Pre-impact Thermophysical Properties and the Yarkovsky Effect of NASA DART Target (65803) Didymos
Authors: Benjamin Rozitis, Simon F. Green, Samuel L. Jackson, Colin Snodgrass, Cyrielle Opitom, Thomas G. Müller, Ulrich C. Kolb, Steven R. Chesley, R. Terik Daly, Cristina A. Thomas, Andrew S. Rivkin
Figure caption: Latitude distributions of observed flux from the Didymos system for the pre-impact VLT (this work) and post-impact JWST (Rivkin et al. 2023) infrared observations.
Paper: Beyond Point Masses. III. Detecting Haumea’s Nonspherical Gravitational Field
Authors: Benjamin C. N. Proudfoot, Darin A. Ragozzine, William Giforos, Will M. Grundy, Mariah MacDonald, William J. Oldroyd
Figure caption: Change in orbit fit quality due to the nonspherical shapes of Haumea’s satellites. In this plot, ﹩{\rm{\Delta }}{\chi }^{2}={\sum }^{i}({x}_{i,{J}_{2}}-{x}_{i,{J}_{2}=0})/{\sigma }_{i,\mathrm{obs}}﹩, where ﹩{x}_{i,{J}_{2}}﹩ and ﹩{x}_{i,{J}_{2}=0}﹩ are synthetic astrometric measurements from models including satellite J 2 and those without. σ  i,obs are the true measurement uncertainties for the system as tabulated in Table 1. This formulation allows us to directly determine whether the satellites’ J 2 values produce detectable changes in the system relative astrometry, given our current observations (HST+Keck). The parameters for the models used were taken from the best fit of our orbit fits. The rotational poles of the satellites were chosen to have high obliquities (with respect to their Haumea-centric orbits) to enhance the effect of J 2. The three separate lines show each satellite’s individual contribution, as well as a comparison where both satellites have (the same) J 2. For reasonable values, J 2 ≲ 0.5, very little change in fit quality is found, although for extremely nonspherical shapes, Hi’iaka’s J 2 could begin to alter the fit.
Paper: Beyond Point Masses. III. Detecting Haumea’s Nonspherical Gravitational Field
Authors: Benjamin C. N. Proudfoot, Darin A. Ragozzine, William Giforos, Will M. Grundy, Mariah MacDonald, William J. Oldroyd
Figure caption: The joint Haumea J 2–Hi’iaka mass posterior distribution. Instead of displaying the density of sampled points as in Figure 1, we show the maximum fit quality (as measured by reduced χ 2) in a small bin. Bins without any sampled points, indicating extremely poor-quality fits, were set to the minimum bin value, although the true value is likely much worse. We find that the best fit with ﹩{\chi }_{\nu }^{2}\sim 1.1﹩ has a p-value of 0.23, meaning there is a 23% chance that random chance would produce a worse fit. A ﹩{\chi }_{\nu }^{2}\sim 1.25﹩ corresponds to a p-value of 0.05. Dashed red lines show the expected J 2 values from different internal density models. The undifferentiated model assumes a homogeneous interior along with the occultation-derived shape model (Ortiz et al. 2017). The differentiated model is a two-layer model proposed by Dunham et al. (2019). The posterior shows that both models are consistent with the data, although the differentiated model is slightly disfavored.
Paper: Beyond Point Masses. III. Detecting Haumea’s Nonspherical Gravitational Field
Authors: Benjamin C. N. Proudfoot, Darin A. Ragozzine, William Giforos, Will M. Grundy, Mariah MacDonald, William J. Oldroyd
Figure caption: A corner plot for the HST-only orbit fit to the Haumea system. We include all 18 fitted parameters along with two derived parameters. To facilitate easy interpretation, we list J 2 rather than J 2 R 2 by taking the volumetric radius R from the occultation-derived shape model (Ortiz et al. 2017). We also show the inclinations of each satellite with respect to Haumea’s equator in the last two columns. Along the top of each column is the marginal posterior distribution for each parameter in our fit. Below the marginal distributions are the two-dimensional joint posterior distributions for every pair of parameters. Contours correspond to 1, 2, and 3σ levels. Small black points mark individual samples from our Markov Chain Monte Carlo (MCMC) chains. The best-fit parameter set in our MCMC chains corresponds to a χ 2 of 99.1 with 90 degrees of freedom. Of particular note is the strong exclusion of J 2 = 0 in the marginal posterior for Haumea’s J 2, alongside strong correlations between J 2 and a variety of other parameters.
Paper: Beyond Point Masses. III. Detecting Haumea’s Nonspherical Gravitational Field
Authors: Benjamin C. N. Proudfoot, Darin A. Ragozzine, William Giforos, Will M. Grundy, Mariah MacDonald, William J. Oldroyd
Figure caption: The spin precession of Haumea over a 1000 yr integration. In the top two panels, we show the spin precession of Haumea in terms of pole ecliptic longitude and latitude. In the bottom two panels, we show the precession of the orbit normal of Hi’iaka and Namaka, again in terms of ecliptic longitude and latitude. The integration parameters have been chosen to be representative of the posterior found in Table 2. Similar integrations with different values for Haumea’s J 2 change the long-term precession period, but are qualitatively similar. Haumea’s spin precession is coupled with the nodal precession of the satellites. High-frequency, low-amplitude variations in Haumea’s pole direction are caused by Namaka’s rapid precession, while low-frequency, high-amplitude variations are coupled with Hi’iaka’s J 2 precession. As can be seen, the precession of all components are strongly coupled, both through Hi’iaka–Namaka gravitational interactions and interactions with Haumea’s J 2.