adsabs/AstroCLIMB huggingface dataset:
Paper: Col-OSSOS: Probing Ice Line/Color Transitions within the Kuiper Belt’s Progenitor Populations
Authors: Laura E. Buchanan, Megan E. Schwamb, Wesley C. Fraser, Michele T. Bannister, Michaël Marsset, Rosemary E. Pike, David Nesvorný, J.J. Kavelaars, Susan D. Benecchi, Matthew J. Lehner, Shiang-Yu Wang, Nuno Peixinho, Kathryn Volk, Mike Alexandersen, Ying-Tung Chen, Brett Gladman, Stephen Gwyn, and Jean-Marc Petit
Figure caption: Example simulated Kuiper Belt population from the model of Nesvorný & Vokrouhlický (2016) after the dynamical cuts outlined in Section 5.1. This example has an inner red/outer neutral primordial disk and color transition at 26 au. The red points represent redder-surfaced KBOs, while the blue points represent neutral-surfaced KBOs. The left column shows the objects in the primordial disk, while the right column shows the objects post-Neptune migration from the model of Nesvorný & Vokrouhlický (2016).
Paper: Col-OSSOS: Probing Ice Line/Color Transitions within the Kuiper Belt’s Progenitor Populations
Authors: Laura E. Buchanan, Megan E. Schwamb, Wesley C. Fraser, Michele T. Bannister, Michaël Marsset, Rosemary E. Pike, David Nesvorný, J.J. Kavelaars, Susan D. Benecchi, Matthew J. Lehner, Shiang-Yu Wang, Nuno Peixinho, Kathryn Volk, Mike Alexandersen, Ying-Tung Chen, Brett Gladman, Stephen Gwyn, and Jean-Marc Petit
Figure caption: The left panel shows the cumulative Hr magnitude distribution. It follows the broken exponential outlined by Equations (1) and (2). The brightest magnitude limit is set by Brown (2008), and the dimmest magnitude limit is set by the limit of the simulated population that can be synthetically detected by the OSSOS survey simulator within the Col-OSSOS magnitude limit. The right panel shows the OSSOS cumulative Hr magnitude distribution in black. In gray is the cumulative Hr magnitude distribution of 50 simulated Kuiper Belt populations, biased by the OSSOS survey simulator.
Paper: Col-OSSOS: Probing Ice Line/Color Transitions within the Kuiper Belt’s Progenitor Populations
Authors: Laura E. Buchanan, Megan E. Schwamb, Wesley C. Fraser, Michele T. Bannister, Michaël Marsset, Rosemary E. Pike, David Nesvorný, J.J. Kavelaars, Susan D. Benecchi, Matthew J. Lehner, Shiang-Yu Wang, Nuno Peixinho, Kathryn Volk, Mike Alexandersen, Ying-Tung Chen, Brett Gladman, Stephen Gwyn, and Jean-Marc Petit
Figure caption: How the percentage of simulations matching the color distribution changes with the position of the synthetic ice line in the primordial planetesimal disk. The initial disk ranges from 24 to 30.36 au. These probabilities are the percentage of the 40,000 simulations for each initial disk transition that match the observed surface colors seen in Col-OSSOS. The left panel shows the results for the inner red, outer neutral primordial disk, and the right panel shows the results for the inner neutral, outer red primordial disk. The gray shaded region shows the region in which the simulations cannot be ruled out to a 95% confidence level.
Paper: The Population of Interstellar Objects Detectable with the LSST and Accessible for In Situ Rendezvous with Various Mission Designs
Authors: Devin J. Hoover, Darryl Z. Seligman, and Matthew J. Payne
Figure caption: Sky map illustrating the probability that a future interstellar object will enter the solar system in a given direction, assuming b = 0. The probabilities use the Schwarzschild distributions for M dwarfs (upper panel), G dwarfs (middle panel), and O/B giants (lower panel). Warmer colors indicate a higher probability. In all panels, the initial position of ‘Oumuamua is indicated with a red star. The majority of the interstellar objects enter the solar system from the direction of the solar apex.
Paper: The Population of Interstellar Objects Detectable with the LSST and Accessible for In Situ Rendezvous with Various Mission Designs
Authors: Devin J. Hoover, Darryl Z. Seligman, and Matthew J. Payne
Figure caption: Schematic diagram showing the geometry of an incoming interstellar object. Each object has an initial velocity vector drawn from the galactic distribution, which maps to a location on the unit sphere in the heliocentric reference frame. We then place the interstellar object at a position given by an impact parameter, b, that we draw.
Paper: The Population of Interstellar Objects Detectable with the LSST and Accessible for In Situ Rendezvous with Various Mission Designs
Authors: Devin J. Hoover, Darryl Z. Seligman, and Matthew J. Payne
Figure caption: Initial positions of interstellar objects that are detectable by the LSST (i.e., their positions when they enter the 5 au sphere prior to detection). We list our detectability criteria in Section 4. While the majority of simulated interstellar objects enter the inner solar system in the direction of the solar apex, a significant yet smaller fraction enter from the antiapex. We include the ecliptic, the LSST’s nominal field of view, and the positions of ‘Oumuamua and Borisov at the moment that they entered the solar system for reference. While the LSST’s observational strategy is complex, with the Northern Ecliptic Spur extending to +30° decl. only for some right ascensions (Bianco et al. 2022), the Wide-Fast-Deep, which includes all right ascensions, is likely to extend its upper delineation boundary to +12° in most regions. Thus, we adopt +12° as the LSST’s nominal field of view.
Paper: The Population of Interstellar Objects Detectable with the LSST and Accessible for In Situ Rendezvous with Various Mission Designs
Authors: Devin J. Hoover, Darryl Z. Seligman, and Matthew J. Payne
Figure caption: Sky positions of interstellar objects when they are detectable for all-sky surveys with limiting magnitudes of 24 and 22. The top panel shows histograms of the sky positions for the simulated objects that satisfy our detectability criteria for the LSST the moment they reach m ≤ 24. The nominal field of view of the LSST is overplotted in red lines. Compared to Figure 3, the detectable objects are less clustered at the apex due to their subsequent motion along their trajectories prior to detection. The bottom panel displays the positions of objects that reach m ≤ 22 and satisfy our detectability criteria when they reach m ∼ 22. The brightest objects (m ≤ 22) are preferentially found along/near the ecliptic. The positions of ‘Oumuamua and Borisov when they were detected are shown, which is distinct from their positions in Figure 3.
Paper: The Population of Interstellar Objects Detectable with the LSST and Accessible for In Situ Rendezvous with Various Mission Designs
Authors: Devin J. Hoover, Darryl Z. Seligman, and Matthew J. Payne
Figure caption: Apparent magnitude, m, as a function of phase angle, θ, for an ‘Oumuamua-like object with dBO = 0.25 (red), 0.35 (green), and 0.45 au (blue). The apparent magnitude is minimized for θ ≈ 0°, when the object is in the ecliptic plane. The closer an object comes to the Earth in this range, the more sensitively its brightness depends on phase angle.
Paper: The Population of Interstellar Objects Detectable with the LSST and Accessible for In Situ Rendezvous with Various Mission Designs
Authors: Devin J. Hoover, Darryl Z. Seligman, and Matthew J. Payne
Figure caption: Orbital inclination of the initial population of interstellar objects and all of the objects that reach apparent magnitudes of 24 and 21.5. As the magnitude cutoff decreases, the distribution of inclinations trends toward uniformity. Our distribution for all objects is similar to that displayed in Figure 2(c) of Engelhardt et al. (2017). A typical interstellar object enters the solar system on a polar trajectory.
Paper: The Population of Interstellar Objects Detectable with the LSST and Accessible for In Situ Rendezvous with Various Mission Designs
Authors: Devin J. Hoover, Darryl Z. Seligman, and Matthew J. Payne
Figure caption: Seasonal variability with which interstellar objects are detected by the LSST (representing m ≤ 24) and Pan-STARRS (representing m ≤ 19). We show the fraction of objects that meet the observability criteria and reach m ≤ 24 and m ≤ 19 as a function of month detected in blue and red histograms. The frequency of interstellar object detections with the LSST is slightly higher than average close to December/January.
Paper: The Population of Interstellar Objects Detectable with the LSST and Accessible for In Situ Rendezvous with Various Mission Designs
Authors: Devin J. Hoover, Darryl Z. Seligman, and Matthew J. Payne
Figure caption: Minimum apparent magnitudes of each interstellar object belonging to the detectable and reachable (Δv ≤ 20 km s−1) populations. The black line indicates the minimum apparent magnitude that ‘Oumuamua reached. The majority of detectable interstellar objects do not get brighter than m ∼ 19.
Paper: The Population of Interstellar Objects Detectable with the LSST and Accessible for In Situ Rendezvous with Various Mission Designs
Authors: Devin J. Hoover, Darryl Z. Seligman, and Matthew J. Payne
Figure caption: The minimum distances to the Earth of each interstellar object in the detectable and reachable populations. Detectable and reachable objects pass within ∼1.5 and ∼1 au of the Earth, respectively.
Paper: The Population of Interstellar Objects Detectable with the LSST and Accessible for In Situ Rendezvous with Various Mission Designs
Authors: Devin J. Hoover, Darryl Z. Seligman, and Matthew J. Payne
Figure caption: Wait time, or time between detection and closest approach to the Earth, of each interstellar object in the detectable and reachable populations. Most interstellar objects have wait times of ∼50 days or less, while the upper limit is ∼150 days for both populations.
Paper: The Population of Interstellar Objects Detectable with the LSST and Accessible for In Situ Rendezvous with Various Mission Designs
Authors: Devin J. Hoover, Darryl Z. Seligman, and Matthew J. Payne
Figure caption: The Δv required to guarantee an interception for each interstellar object belonging to the detectable population. We consider any object that satisfies Δv < 20 km s−1 as reachable, which corresponds to ∼20% of detectable objects.
Paper: The Population of Interstellar Objects Detectable with the LSST and Accessible for In Situ Rendezvous with Various Mission Designs
Authors: Devin J. Hoover, Darryl Z. Seligman, and Matthew J. Payne
Figure caption: Distributions of dynamical impact parameters for the detectable and reachable populations. Although the population is drawn from a b2 distribution, the LSST will detect objects out to 3 au. Note that b here represents the orbital impact parameter and is not related to a hypothetical impactor mission.
Paper: The Population of Interstellar Objects Detectable with the LSST and Accessible for In Situ Rendezvous with Various Mission Designs
Authors: Devin J. Hoover, Darryl Z. Seligman, and Matthew J. Payne
Figure caption: Distributions of perihelia for the detectable and reachable populations. The LSST will detect objects with perihelia out to 2 au.
Paper: The Population of Interstellar Objects Detectable with the LSST and Accessible for In Situ Rendezvous with Various Mission Designs
Authors: Devin J. Hoover, Darryl Z. Seligman, and Matthew J. Payne
Figure caption: Distributions of eccentricities for the detectable and reachable populations. The eccentricities of ‘Oumuamua and Borisov are shown in black and gray dashed lines, respectively. The typical interstellar object has eccentricity close to unity.
Paper: The Population of Interstellar Objects Detectable with the LSST and Accessible for In Situ Rendezvous with Various Mission Designs
Authors: Devin J. Hoover, Darryl Z. Seligman, and Matthew J. Payne
Figure caption: Distribution of hyperbolic excess velocity (incoming velocity) for the detectable and reachable populations. The incoming velocities of ‘Oumuamua and Borisov are shown in black and gray dashed lines, respectively.
Paper: Student Dust Counter Status Report: The First 50 au
Authors: Edwin Bernardoni, Mihály Horányi, Alex Doner, Marcus Piquette, Jamey R. Szalay, Andrew R. Poppe, David James, Silvan Hunziker, Veerle Sterken, Peter Strub, Cathy Olkin, Kelsi N. Singer, John Spencer, Alan Stern, and Harold Weaver
Figure caption: Stimulus tests are autonomously executed periodically throughout the mission and are compared to preflight calibration curves. An example of one such comparison is plotted for Channel 4. No aging effects of SDC’s electronics are apparent to date.
Paper: Student Dust Counter Status Report: The First 50 au
Authors: Edwin Bernardoni, Mihály Horányi, Alex Doner, Marcus Piquette, Jamey R. Szalay, Andrew R. Poppe, David James, Silvan Hunziker, Veerle Sterken, Peter Strub, Cathy Olkin, Kelsi N. Singer, John Spencer, Alan Stern, and Harold Weaver
Figure caption: History of events tagged as noise due to coincidence with thruster firings (top), stimulus tests (middle), and other recorded events (bottom). Different line styles indicate the time window used for determining coincidence.
Paper: Student Dust Counter Status Report: The First 50 au
Authors: Edwin Bernardoni, Mihály Horányi, Alex Doner, Marcus Piquette, Jamey R. Szalay, Andrew R. Poppe, David James, Silvan Hunziker, Veerle Sterken, Peter Strub, Cathy Olkin, Kelsi N. Singer, John Spencer, Alan Stern, and Harold Weaver
Figure caption: The trajectory of New Horizons past 50 au. The decrease in spacecraft speed with increasing distance results in a higher minimum detectable mass, clearly identified in the bottom panel of Figure 4. New Horizons is now heading along an ecliptic longitude λNH = 293° compared to the ISD inflow of λISD ≃ 259°, indicated by the parallel upward-pointing arrows at the bottom. Considering the magnitude of their speeds results in an impact angle of ISD onto SDC of α ≃ 23°.
Paper: Student Dust Counter Status Report: The First 50 au
Authors: Edwin Bernardoni, Mihály Horányi, Alex Doner, Marcus Piquette, Jamey R. Szalay, Andrew R. Poppe, David James, Silvan Hunziker, Veerle Sterken, Peter Strub, Cathy Olkin, Kelsi N. Singer, John Spencer, Alan Stern, and Harold Weaver
Figure caption: Plot of impact charges (top) and mass estimates (bottom) of all dust events recorded by SDC up to 50 au, excluding those flagged as coincidence events. Gray bars indicate Pluto and Arrokoth flybys, and a black horizontal line in the bottom plot indicates the 0.63 μm cutoff used for flux and density estimates, allowing for both A and B sides to contribute (Piquette et al. 2019b). Mass estimates assume silicate-dominated IDP grains with a density of 2.5 g cm−3 following circular Kepler orbits modified by radiation pressure. Histogram bars are included for both distance and charge/mass bins on their corresponding axes.
Paper: Student Dust Counter Status Report: The First 50 au
Authors: Edwin Bernardoni, Mihály Horányi, Alex Doner, Marcus Piquette, Jamey R. Szalay, Andrew R. Poppe, David James, Silvan Hunziker, Veerle Sterken, Peter Strub, Cathy Olkin, Kelsi N. Singer, John Spencer, Alan Stern, and Harold Weaver
Figure caption: Plot of estimated dust flux average rates for all science channels (blue) compared to their corresponding reference channel (red) for electronic chain sides A (left) and B (right). While side B is noticeably less sensitive than side A, we currently have no means of definitively picking one side over the other as the “true” flux, and as such we continue with the average of the two. Note that the values plotted in Figure 6 are not simply the difference between the blue and the red, as the blue represents the mean of all science channel rates on that electronic chain. Instead, each science channel flux rate is individually reduced by the corresponding reference channel rate, with extra considerations outlined in Section 4.
Paper: Student Dust Counter Status Report: The First 50 au
Authors: Edwin Bernardoni, Mihály Horányi, Alex Doner, Marcus Piquette, Jamey R. Szalay, Andrew R. Poppe, David James, Silvan Hunziker, Veerle Sterken, Peter Strub, Cathy Olkin, Kelsi N. Singer, John Spencer, Alan Stern, and Harold Weaver
Figure caption: Plot of the estimated dust flux onto SDC for grains with IDP radii >0.63 μm. The two red curves demonstrate the model given by Poppe (2016) and Poppe et al. (2019) fitted to SDC measurements assuming only IDP detections (solid curve) or an updated version assuming an additional ISD contribution (dashed curve), with the ISD component simulated with the IMEX model and the resulting ISD flux values calibrated using ISD measurements from Ulysses indicated by the horizontal dotted line (Sterken et al. 2015; Strub et al. 2019).
Paper: Student Dust Counter Status Report: The First 50 au
Authors: Edwin Bernardoni, Mihály Horányi, Alex Doner, Marcus Piquette, Jamey R. Szalay, Andrew R. Poppe, David James, Silvan Hunziker, Veerle Sterken, Peter Strub, Cathy Olkin, Kelsi N. Singer, John Spencer, Alan Stern, and Harold Weaver
Figure caption: The impact speed of ISD and IDP (top) and the SDC pipeline’s IDP size interpretation of 0.28 and 0.3 μm radius ISD impacts (bottom) as a function of heliocentric distance. Due to their higher speeds, ISD impacts on the detector panels are interpreted as larger-sized IDP detections as a result of the assumed dust velocity used in the conversion from charge to mass in Equation (2). Consequently, ISD is expected to contribute to SDC’s flux estimates for the nominal IDP size cutoff of 0.68 μm indicated by the horizontal line.
Paper: Student Dust Counter Status Report: The First 50 au
Authors: Edwin Bernardoni, Mihály Horányi, Alex Doner, Marcus Piquette, Jamey R. Szalay, Andrew R. Poppe, David James, Silvan Hunziker, Veerle Sterken, Peter Strub, Cathy Olkin, Kelsi N. Singer, John Spencer, Alan Stern, and Harold Weaver
Figure caption: Plot of the estimated dust flux onto SDC for grains with IDP radii >0.63 μm. The three curves demonstrate the model given by Poppe (2016) and Poppe et al. (2019) fitted to SDC measurements assuming only an EKB distribution (red), a putative OKB distribution with initial semimajor axes between 80 and 110 au (blue), and a combination of the two with equal weight (purple). All three include an ISD contribution (dotted line) as in Figure 6.
Paper: Student Dust Counter Status Report: The First 50 au
Authors: Edwin Bernardoni, Mihály Horányi, Alex Doner, Marcus Piquette, Jamey R. Szalay, Andrew R. Poppe, David James, Silvan Hunziker, Veerle Sterken, Peter Strub, Cathy Olkin, Kelsi N. Singer, John Spencer, Alan Stern, and Harold Weaver
Figure caption: Plot of estimated IDP density for grains with radii >0.63 μm (top), >0.68 μm (second), >0.82 μm (third), and >1.5 μm (bottom) as measured by SDC. Note that these estimates are upper limits on the IDP density, as the contribution from ISD impacts is not removed. However, ISD impacts are not expected to contribute for thresholds above 0.68 μm. Voyager estimates from plasma wave data are indicated by dashed lines in the top panel. Points plotted at 0 with no error bars are empty bins from containing either no valid detections for any panel or no valid time coverage for any panel within that time period.
Paper: Student Dust Counter Status Report: The First 50 au
Authors: Edwin Bernardoni, Mihály Horányi, Alex Doner, Marcus Piquette, Jamey R. Szalay, Andrew R. Poppe, David James, Silvan Hunziker, Veerle Sterken, Peter Strub, Cathy Olkin, Kelsi N. Singer, John Spencer, Alan Stern, and Harold Weaver
Figure caption: The total modeled IDP density in the Neptune-rotated frame. The dotted and dashed lines denote the position and orbit of Neptune, respectively, while the solid line denotes the trajectory of the New Horizons spacecraft.
Paper: Blue Marble, Stagnant Lid: Could Dynamic Topography Avert a Waterworld?
Authors: Claire Marie Guimond, John F. Rudge, and Oliver Shorttle
Figure caption: The four major endogenic sources of topography on a stagnant lid planet. (a) The component of dynamic topography due to flow-induced traction on the lithosphere. (b) Tectonic crustal thickening caused by tension over cold downwellings. (c) The component of dynamic topography due to thermal isostasy over thinned lithosphere. (d) Magmatic crustal thickening caused by melting of upwelling plumes.
Paper: Blue Marble, Stagnant Lid: Could Dynamic Topography Avert a Waterworld?
Authors: Claire Marie Guimond, John F. Rudge, and Oliver Shorttle
Figure caption: Snapshot from a single time step of the dimensionless temperature field (bottom), surface dynamic topography, ﹩{h}^{{\prime} }﹩ (top), and temperature profile, ﹩{T}^{{\prime} }﹩ (right), for chaotically time-dependent convection in the stagnant lid regime. This example shows Ra1 = 1 × 108 and Δη = 1 × 108. The dimensionless temperatures range from 0 (cold; blue) to 1 (hot; red). The gray box in the temperature profile shows the instantaneous location and thickness of the upper thermal boundary layer. The vertical scale of ﹩{h}^{{\prime} }﹩ is exaggerated.
Paper: Blue Marble, Stagnant Lid: Could Dynamic Topography Avert a Waterworld?
Authors: Claire Marie Guimond, John F. Rudge, and Oliver Shorttle
Figure caption: Fitted scaling relationship for dimensionless rms dynamic topography, ﹩{h}_{\mathrm{rms}}^{{\prime} }﹩, from 2D numerical convection simulations (n = 11). Topography is given by a four-parameter linear model, which depends on the interior Rayleigh number, Rai, and the viscosity temperature prefactor, ﹩b=\mathrm{ln}({\rm{\Delta }}\eta )﹩. Markers represent individual cases (see Table 1) and are colored according to Δη. The uncertainties on ﹩{h}_{\mathrm{rms}}^{{\prime} }﹩, taken to be the standard errors of the mean, are smaller than the marker size. Dashed lines represent the best-fit parameter combination at discrete ﹩\mathrm{ln}({\rm{\Delta }}\eta )﹩. Swaths span one standard deviation of the response variable, propagated from the covariance matrix of the fit.
Paper: Blue Marble, Stagnant Lid: Could Dynamic Topography Avert a Waterworld?
Authors: Claire Marie Guimond, John F. Rudge, and Oliver Shorttle
Figure caption: Thermal evolutions sampled from the 1D model ensemble, as a function of time in Gyr. From top to bottom: mantle temperature, Tm in K, mantle viscosity, ηm in Pa s, dimensionless inverse viscous temperature scale, b, interior Rayleigh number, Rai, topography dimensionalization factor, dΔTαm in m, and rms dynamic topography, hrms in m. Columns compare planet masses from 0.1 M⊕ (left), through 1 M⊕ (middle), to 5 M⊕ (right). Each thin black line (n = 500) represents a single evolution, drawing random values of the unknown viscosity activation energy and prefactor, hence an evolutionary spread. Green lines follow the ensemble mean (for Rai, which is lognormally distributed, this is the lognormal mean). All runs use baseline values of the core mass fraction and radioisotope budget. Parameter values and random variable distributions are given in Table 2.
Paper: Blue Marble, Stagnant Lid: Could Dynamic Topography Avert a Waterworld?
Authors: Claire Marie Guimond, John F. Rudge, and Oliver Shorttle
Figure caption: Variations of the rms dynamic topography based on 1D thermal histories, as a function of select exoplanetary properties that might be constrained in the future: from left to right these are the planet age in Gyr, mass in M⊕, core mass fraction, and abundance of radioactive U and Th relative to the solar value. Solid lines show the ensemble mean of 10,000 test planets with uniformly random viscosity activation energies and prefactors, and with normally random topography scaling coefficients. Swaths span one standard deviation from the mean. These calculations show subaerial topography; water-loaded topography would be ∼1.5 × higher. Parameter values and random variable distributions are given in Table 2.