adsabs/AstroCLIMB huggingface dataset:
Paper: Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor
Authors: Mark Wronkiewicz, Jake Lee, Lukas Mandrake, Jack Lightholder, Gary Doran, Steffen Mauceri, Taewoo Kim, Nathan Oborny, Thomas Schibler, Jay Nadeau, James K. Wallace, Eshaan Moorjani, Chris Lindensmith
Figure caption: Motion history images (MHIs) compress microscopy observations into a 								single, quickly interpretable image. Here, color indicates the time 								when each pixel changed maximally. Left: relatively straight color 								tracks in DHM (top left) and VFI (bottom left) observations depict 								particles that passively floated through the sample chamber with the 								background fluid flow. Right: curving/spiraling tracks in DHM (top 								right), and reversing tracks in VFI (bottom right) indicate the 								presence of living, motile cells.
Paper: Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor
Authors: Mark Wronkiewicz, Jake Lee, Lukas Mandrake, Jack Lightholder, Gary Doran, Steffen Mauceri, Taewoo Kim, Nathan Oborny, Thomas Schibler, Jay Nadeau, James K. Wallace, Eshaan Moorjani, Chris Lindensmith
Figure caption: Autofluorescent cells captured at depths of 5 m (left) and 35 m (right) 							in MHIs at Mono Lake testing. The OSIA detected approximately 23× more 							autofluorescent particles in the 5 m sample (where light is more 							abundant). The large majority of fluorescent particles had spectral 							signatures consistent with chlorophyll. No motile autofluorescent 							microorganisms were detected at Mono Lake during the field trial.
Paper: Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor
Authors: Mark Wronkiewicz, Jake Lee, Lukas Mandrake, Jack Lightholder, Gary Doran, Steffen Mauceri, Taewoo Kim, Nathan Oborny, Thomas Schibler, Jay Nadeau, James K. Wallace, Eshaan Moorjani, Chris Lindensmith
Figure caption: A variety of prioritization strategies are achieved through different 								weighting of the SUE (indicated by marker color) and the DD 								(indicated by marker position). These plots indicate the top 10 								prioritized observations (in red text) from 41 lab training data 								sets using three different strategies: scientific utility-only 								(left), a balance between utility and diversity (middle), and 								diversity-only (right). The utility-only prioritization selects 								high-SUE experiments without regard for the DD and chooses several 								observations judged to contain highly similar in content (as 								indicated by their proximity in this plot). The middle plot 								demonstrates a balanced prioritization (with α = 1.0 in Equation (19)) where selected experiments have 								a higher mean SUE, but also provide a sampling of the DD space. The 								right plot shows diversity-only prioritization with selections 								spanning the DD space without regard for the SUE. Note: the true DD 								vector is 9D, as specified in Section 3.3.6. We artificially chose two of 								the DD elements here—the 90th percentile of the track particle size 								and 50th percentile track speed—to visually convey the 								prioritization concept.
Paper: Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor
Authors: Mark Wronkiewicz, Jake Lee, Lukas Mandrake, Jack Lightholder, Gary Doran, Steffen Mauceri, Taewoo Kim, Nathan Oborny, Thomas Schibler, Jay Nadeau, James K. Wallace, Eshaan Moorjani, Chris Lindensmith
Figure caption: Data validation products help identify quality issues in observations. 							Here, time traces indicate instrument vibration and shaking (top; red 							and orange, respectively) as well as excessive particle density in DHM 							and FLFM observations from the Mono Lake field campaign (bottom; red and 							orange, respectively). The blue trace and shaded region indicates the 							(expected) mean and interquartile ranges of these same metrics for the 							training data. The threshold for each validation check (indicated by the 							black dotted line) is a tunable parameter that determines if each 							observation passes or fails. Large frame-to-frame changes and high 							particle density can both degrade tracking performance and downstream 							biosignature analysis, so detecting these problems can help influence 							prioritization and alert ground teams to potential instrument 							issues.
Paper: Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor
Authors: Mark Wronkiewicz, Jake Lee, Lukas Mandrake, Jack Lightholder, Gary Doran, Steffen Mauceri, Taewoo Kim, Nathan Oborny, Thomas Schibler, Jay Nadeau, James K. Wallace, Eshaan Moorjani, Chris Lindensmith
Figure caption: We evaluated the performance of three classical ML models for 								identifying motility on a held-out test set: SVC, RF, and GBT. Top: 								precision-recall curve displays the balance between these two 								metrics. Here, perfect performance corresponds to the top-right 								corner. The circular marker on each classifier trace corresponds to 								a 50% confidence threshold. Bottom: DET curve displays the balance 								between false negatives and false positives when traversing a range 								of confidence thresholds. Perfect performance corresponds to the 								lower-left corner.
Paper: Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor
Authors: Mark Wronkiewicz, Jake Lee, Lukas Mandrake, Jack Lightholder, Gary Doran, Steffen Mauceri, Taewoo Kim, Nathan Oborny, Thomas Schibler, Jay Nadeau, James K. Wallace, Eshaan Moorjani, Chris Lindensmith
Figure caption: Comparison of OSIA-estimated SUE values to those calculated from 								ground truth labels on the lab DHM data set. The estimated SUEs 								exhibit an upward trend with respect to the “true” SUEs calculated 								from labeled tracks. However, our system slightly overestimates 								observations with true SUE values <0.2 and moderately 								underestimates observations with true SUE values >0.4. Three 								observations are highlighted (triangle, star, diamond) to facilitate 								deeper discussion into system imperfections and their driving 								causes.
Paper: Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor
Authors: Mark Wronkiewicz, Jake Lee, Lukas Mandrake, Jack Lightholder, Gary Doran, Steffen Mauceri, Taewoo Kim, Nathan Oborny, Thomas Schibler, Jay Nadeau, James K. Wallace, Eshaan Moorjani, Chris Lindensmith
Figure caption: Track features contribute to the motility prediction in different 								ways, but relative and directional features were frequently ranked 								among the most important. Using SHAP, this plot illustrates how the 								10 most impactful features (shown in descending order) contribute 								toward the RF model’s final motility predictions. For each feature 								row, the corresponding impact of each feature on the final decision 								is shown for all tracks (each plotted as one point per row). Note 								that more important features tend to have wider distributions (along 								the horizontal axis) corresponding to larger effects on the output 								probabilities. The color of the points indicates whether a specific 								track’s feature was relatively high or low compared to all others. 								Taken together, this visualizes how track features contributed to 								the output classification decisions.
Paper: Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor
Authors: Mark Wronkiewicz, Jake Lee, Lukas Mandrake, Jack Lightholder, Gary Doran, Steffen Mauceri, Taewoo Kim, Nathan Oborny, Thomas Schibler, Jay Nadeau, James K. Wallace, Eshaan Moorjani, Chris Lindensmith
Figure caption: Tracking performance increases with SNR and decreases with particle 								density. However, changes are limited above SNR = 0.5 for the 								crowded observations, consistently resulting in a >50% reduction 								in tracking performance. Note that the maximum value of the beta 								metric is the alpha metric.
Paper: Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor
Authors: Mark Wronkiewicz, Jake Lee, Lukas Mandrake, Jack Lightholder, Gary Doran, Steffen Mauceri, Taewoo Kim, Nathan Oborny, Thomas Schibler, Jay Nadeau, James K. Wallace, Eshaan Moorjani, Chris Lindensmith
Figure caption: Model calibration provides a mechanism to correct bias in our trained 								motility classifiers and normalize to a true empirical likelihood 								estimate. Top: uncalibrated GBT, RF, and SVC classifiers generate 								predicted probabilities that do not always align with the true class 								on a held-out test set. Points above the diagonal line indicate low 								confidence, while points below the diagonal line indicate 								overconfidence. Bottom: after cross-validated, isotonic calibration 								the model outputs more reliably represent the true probabilities 								within each bin.
Paper: Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor
Authors: Mark Wronkiewicz, Jake Lee, Lukas Mandrake, Jack Lightholder, Gary Doran, Steffen Mauceri, Taewoo Kim, Nathan Oborny, Thomas Schibler, Jay Nadeau, James K. Wallace, Eshaan Moorjani, Chris Lindensmith
Figure caption: The tracker identifies particles and links them through time to form 								tracks. Here, tracks are overlaid on the 80th frame of a DHM 								observation. ML-derived motility classifications are indicated by 								track color. Tracks classified as motile are magenta while tracks 								classified as nonmotile are blue.
Paper: Impact Momentum Transfer—Insights from Numerical Simulation of Impacts on Large Boulders of Asteroids
Authors: Kaiyi Dai, Xi-Zi Luo, Meng-Hua Zhu, Gareth S. Collins, Thomas Davison, Robert Luther, Kai Wünnemann
Figure caption: Model setting snapshots for different subsurface boulder arrangements. (a) Impacts on a homogeneous layer without boulders. (b) Impacts on a fully buried boulder (R  b  = 1.5 m) with changing burial depth, 1/3 R  b  deeper each time. (c) Impacts on a fully buried boulder surrounded by two other boulders with the same size and separated by 1/3 R  b . (d) Impacts on a fully buried boulder surrounded by two other small boulders with radii of 1/3 R  b ; each simulation increases the separation by 0.5 m. The direct-contact boulder is the primary boulder, and the rest are secondary boulders. The impact direction is from the right to the left.
Paper: Impact Momentum Transfer—Insights from Numerical Simulation of Impacts on Large Boulders of Asteroids
Authors: Kaiyi Dai, Xi-Zi Luo, Meng-Hua Zhu, Gareth S. Collins, Thomas Davison, Robert Luther, Kai Wünnemann
Figure caption: Vertical momentum transfer efficiency β − 1 (at 100 ms after impact) as functions of impact angle for different subsurface boulder arrangements (see Figure 1 for details). Values of β − 1 for impacts on a homogeneous regolith (dashed black line) and on a single buried boulder just below the surface (dark blue line) are plotted as reference cases in panels (a)–(c).
Paper: Impact Momentum Transfer—Insights from Numerical Simulation of Impacts on Large Boulders of Asteroids
Authors: Kaiyi Dai, Xi-Zi Luo, Meng-Hua Zhu, Gareth S. Collins, Thomas Davison, Robert Luther, Kai Wünnemann
Figure caption: iSALE-3D results for (a) cumulative vertical momentum transfer efficiency β − 1 against normalized vertical ejecta velocity (V  z /U) for impacts on the homogeneous regolith with different impact angles (θ). (b) Ejecta provenance plots for various impact angles. Colors represent the normalized vertical velocity (V  z /U). Tracer ejection velocity is plotted at the pre-impact position.
Paper: Impact Momentum Transfer—Insights from Numerical Simulation of Impacts on Large Boulders of Asteroids
Authors: Kaiyi Dai, Xi-Zi Luo, Meng-Hua Zhu, Gareth S. Collins, Thomas Davison, Robert Luther, Kai Wünnemann
Figure caption: Shock-wave-induced impact melt for oblique and vertical (90°) impacts on (a) homogenous regolith and (b) regolith with a buried boulder (R  b  = 1.5 m). The figures show the plane-of-symmetry cross section for 3D simulations. Circles denote pre-impact positions of melt. Blue circles denote ejected melt, and red circles denote melt retained inside the transient crater until the end of simulation at 100 ms.
Paper: Impact Momentum Transfer—Insights from Numerical Simulation of Impacts on Large Boulders of Asteroids
Authors: Kaiyi Dai, Xi-Zi Luo, Meng-Hua Zhu, Gareth S. Collins, Thomas Davison, Robert Luther, Kai Wünnemann
Figure caption: Cumulative vertical momentum transfer efficiency, β − 1, as a function of normalized vertical ejecta speed (V  z /U) for impact on to a single large boulder, which is buried in the regolith (a) just below the surface, (b) 1/3 R  b  (0.5 m) under the surface, and (c) 2/3 R  b  (1 m) under the surface. The solid blue line is the reference case for a vertical impact onto a homogenous layer, as shown in Figure 1(a).
Paper: Impact Momentum Transfer—Insights from Numerical Simulation of Impacts on Large Boulders of Asteroids
Authors: Kaiyi Dai, Xi-Zi Luo, Meng-Hua Zhu, Gareth S. Collins, Thomas Davison, Robert Luther, Kai Wünnemann
Figure caption: iSALE-3D results for (a, b) peak pressure contour plots for oblique impact angle 45° and 75° and (c, d) ejecta provenance plots for oblique impact angle 45° and 75° with normalized vertical velocity (V  z /U). Ejecta is placed at the pre-impact position. (e) Contrast of the peak pressure between targets with three boulders and targets without any boulders.
Paper: Impact Momentum Transfer—Insights from Numerical Simulation of Impacts on Large Boulders of Asteroids
Authors: Kaiyi Dai, Xi-Zi Luo, Meng-Hua Zhu, Gareth S. Collins, Thomas Davison, Robert Luther, Kai Wünnemann
Figure caption: Horizontal momentum transfer efficiency, γ − 1, against normalized horizontal ejecta velocity (V  x /U) for (a) uprange ejecta, (b) downrange ejecta, and (c) all ejecta. Negative values of γ − 1 represent ejecta momentum toward the downrange of the impact direction. Solid lines denote impacts on a homogeneous regolith. Different styles of dashed lines denote impacts on targets with a buried boulder (see the target configuration in Figure 2(b)).
Paper: Impact Momentum Transfer—Insights from Numerical Simulation of Impacts on Large Boulders of Asteroids
Authors: Kaiyi Dai, Xi-Zi Luo, Meng-Hua Zhu, Gareth S. Collins, Thomas Davison, Robert Luther, Kai Wünnemann
Figure caption: Volumes of (a) total impact-induced melt, (b) ejected melt, and (c) melt retained inside the transient crater for various impact angles and subsurface boulder arrangements in Figures 1(a)–(c). Retained and ejected melts are recorded until the end of simulation at 100 ms. Note that the blue, yellow, and green lines overlap, as do the red and purple lines, in panel (a).
Paper: Impact Momentum Transfer—Insights from Numerical Simulation of Impacts on Large Boulders of Asteroids
Authors: Kaiyi Dai, Xi-Zi Luo, Meng-Hua Zhu, Gareth S. Collins, Thomas Davison, Robert Luther, Kai Wünnemann
Figure caption: Comparison of total β − 1 (at 100 ms after impact) against burial depth or boulder separation for 2D and 3D vertical impact simulation as shown in Figure 1 (top) and total β − 1 against normalized ejecta velocity for 2D and 3D vertical impact as shown in Figures 1(c) and (d) (bottom). 2D simulations have the same material properties and resolution (8 CPPR) as 3D models.
Paper: Impact Momentum Transfer—Insights from Numerical Simulation of Impacts on Large Boulders of Asteroids
Authors: Kaiyi Dai, Xi-Zi Luo, Meng-Hua Zhu, Gareth S. Collins, Thomas Davison, Robert Luther, Kai Wünnemann
Figure caption: (a) Final step of the simulation showing locations of boulder (brown) and regolith (gray) materials for a boulder buried at 1/3 R  b  under the surface. (b) Boulder mass retained below the surface and below the burial depth as functions of the burial depth, z  b /R  b . The data points of D. D. Durda et al. (2011) are the largest remnant boulder mass divided by total boulder mass.
Paper: Impact Momentum Transfer—Insights from Numerical Simulation of Impacts on Large Boulders of Asteroids
Authors: Kaiyi Dai, Xi-Zi Luo, Meng-Hua Zhu, Gareth S. Collins, Thomas Davison, Robert Luther, Kai Wünnemann
Figure caption: Cumulative vertical momentum transfer efficiency, β − 1, as a function of normalized vertical ejecta speed (V  z /U) for impact simulation setting as in Figure 1(c). Results for a single boulder (Figure 4(a)) are plotted for comparison, as shown by dashed lines. The solid blue line is the reference case for a vertical impact onto a homogenous layer, as shown in Figure 1(a).
Paper: Impact Momentum Transfer—Insights from Numerical Simulation of Impacts on Large Boulders of Asteroids
Authors: Kaiyi Dai, Xi-Zi Luo, Meng-Hua Zhu, Gareth S. Collins, Thomas Davison, Robert Luther, Kai Wünnemann
Figure caption:  β − 1 as a function of separations between the primary and the secondary small boulders, where R  b  is the radius of the primary boulder (1.5 m). The dashed blue line is the reference case for a vertical impact onto a homogenous layer, as shown in Figure 1(a).
Paper: Impact Momentum Transfer—Insights from Numerical Simulation of Impacts on Large Boulders of Asteroids
Authors: Kaiyi Dai, Xi-Zi Luo, Meng-Hua Zhu, Gareth S. Collins, Thomas Davison, Robert Luther, Kai Wünnemann
Figure caption: Comparison of crater diameter and the β − 1 parameter in 2D and 3D iSALE simulations (vertical impacts) ending at different post-impact moments. The target configuration is a single boulder buried beneath the surface (Figure 1(b)). (a) Growth of the crater diameter during the excavation stage, where the boulder is just beneath the surface. The 3D model ends at 0.1 s after impact. (b) Comparison of the β − 1 parameter in this work to the 2D results from previous literature (M. E. DeCoster et al. 2024). In their work, β − 1 is calculated at 2 s post-impact.
Paper: Impact Momentum Transfer—Insights from Numerical Simulation of Impacts on Large Boulders of Asteroids
Authors: Kaiyi Dai, Xi-Zi Luo, Meng-Hua Zhu, Gareth S. Collins, Thomas Davison, Robert Luther, Kai Wünnemann
Figure caption: Resolution test for the melt volume in our simulations. (a) Volumes of total melt production and (b) volumes normalized by that of the 60-CPPR (cell per projectile radius) case as functions of CPPR. Dashed gray lines: 45° impacts. Solid black lines: 90° impacts. No boulder (circles): impacts on a monolith. One boulder (squares): impacts on a boulder buried directly below the surface (R  b  = 1.5 m).
Paper: Impact Momentum Transfer—Insights from Numerical Simulation of Impacts on Large Boulders of Asteroids
Authors: Kaiyi Dai, Xi-Zi Luo, Meng-Hua Zhu, Gareth S. Collins, Thomas Davison, Robert Luther, Kai Wünnemann
Figure caption: High-resolution (60 CPPR) modeling results of the melt region for a vertical impact on (a) homogeneous regolith and (b) regolith with a buried boulder (R  b  = 1.5 m). Red denotes the melt region. Gray denotes the regolith, and brown denotes the impactor and boulder.
Paper: Impact Momentum Transfer—Insights from Numerical Simulation of Impacts on Large Boulders of Asteroids
Authors: Kaiyi Dai, Xi-Zi Luo, Meng-Hua Zhu, Gareth S. Collins, Thomas Davison, Robert Luther, Kai Wünnemann
Figure caption: Test cases for a fully buried boulder placed in the downrange direction of the impact point. (a) Model setting snapshot. (b) Cumulative vertical momentum transfer efficiency β − 1 against normalized vertical ejecta velocity for impact angles of 45° and 90°. R  b  = 1.5 m.
Paper: MultIHeaTS: A Fast and Stable Thermal Solver for Multilayered Planetary Surfaces
Authors: Cyril Mergny, Frédéric Schmidt
Figure caption: Temperature distribution on Europa at lat./long. (0°, 180°) with albedo A = 0.8 for three strategic depths: at the top layer, close to the diurnal thermal skin depth, and at the bottom layer. The histogram shows the frequency of each temperature at each depth. Filled bars represent the 1 million yr simulation, and dotted lines represent a single Jupiter year.
Paper: MultIHeaTS: A Fast and Stable Thermal Solver for Multilayered Planetary Surfaces
Authors: Cyril Mergny, Frédéric Schmidt
Figure caption: Analytical validation for a stiff initial condition and homogeneous thermal properties on an irregular grid for different time steps. Here t f = n  t Δt. Despite the discontinuity at x = 0.5, the fully implicit solver MultIHeaTS can compute the evolution of temperature with a very close match (maximum error e + < 0.5%) to the analytic solution. (Bottom right) Zoom on the spurious oscillations of the Crank–Nicolson solver at the location of the initial discontinuity.
Paper: MultIHeaTS: A Fast and Stable Thermal Solver for Multilayered Planetary Surfaces
Authors: Cyril Mergny, Frédéric Schmidt
Figure caption: (Left) Surface temperature distributions at Europa’s equator for three different scenarios: ﹩{ \mathcal P }﹩ at longitude 0° (sub-Jovian with eclipse) and ﹩{ \mathcal Q }﹩ at longitude 180° (anti-Jovian without eclipse), both with albedo A = 0.4, and ﹩{ \mathcal Q }^{\prime} ﹩ at longitude 180° with equivalent albedo ﹩A^{\prime} =0.453﹩. (Right) rms distance between eclipse temperature distributions ﹩{ \mathcal P }﹩ and noneclipse distributions with unchanged albedo ﹩{ \mathcal Q }﹩ or with equivalent albedo ﹩{ \mathcal Q }^{\prime} ﹩ as a function of longitude and latitude. Using an equivalent albedo allows one to approximate, at first order, the surface temperature distribution of the sub-Jovian hemisphere without having to compute eclipses.
Paper: MultIHeaTS: A Fast and Stable Thermal Solver for Multilayered Planetary Surfaces
Authors: Cyril Mergny, Frédéric Schmidt
Figure caption: Relative errors produced by the fully implicit scheme when increasing the time step Δt (log scales). The reference is Spencer’s explicit thermal model computed with 104 points day–1. Time step Δt is represented as a fraction of the surface flux period P. The stability zone of the explicit scheme F < 1/2 is represented in light blue. (Left) Maximum error e +. (Right) Average error ﹩\overline{e}﹩ and relative computation time τ  r . The computational advantage of the fully implicit solver enables much faster temperature calculations.
Paper: MultIHeaTS: A Fast and Stable Thermal Solver for Multilayered Planetary Surfaces
Authors: Cyril Mergny, Frédéric Schmidt
Figure caption: Daily surface temperatures on Europa for four different ground profiles at lat./long. (0°, 180°). Dashed lines show monolayered profiles, and solid lines show bilayered profiles. No homogeneous ground profile could match the thermal signature of the bilayered ones. The gray vertical lines indicate the recommended measurement time for a space probe to discern between a homogeneous surface and a bilayered one.
Paper: MultIHeaTS: A Fast and Stable Thermal Solver for Multilayered Planetary Surfaces
Authors: Cyril Mergny, Frédéric Schmidt
Figure caption: Validation of MultIHeaTS against Spencer’s explicit algorithm. Temperature profiles for different times are plotted simultaneously: i = 0, 200, 14,050, 34,959, and 49,000. MultIHeaTS is as accurate as Spencer’s explicit solver with the advantage of stability. Note the uneven grid spacing of MultiHeaTS, denser at the surface, shown by the logarithmic horizontal axis.
Paper: MultIHeaTS: A Fast and Stable Thermal Solver for Multilayered Planetary Surfaces
Authors: Cyril Mergny, Frédéric Schmidt
Figure caption: Schematic representation of the target body and Sun system. The solar incidence angle θ  i  is defined as the angle between the normal vector at the target surface of the body r surf and the normal vector at the subsolar point r Sun.