RIME-REASON synergistic opportunities for surface and near-surface investigations of icy moons
Bibliographic record
Abstract
The near-surface (i.e., depths of tens of meters from the surface) of icy environments is subject to various processes resulting in changes to its structure, density, and composition. On Earth, surface meltwater can refreeze in firn to form meters-thick ice layers, which can inhibit subsequent vertical infiltration in favor of lateral runoff. On icy worlds such as Ganymede, landform degradation processes, such as mass wasting and impact erosion, could leave behind layered deposits of dark material of varying density and thickness. Therefore, characterizing such heterogeneity (layering) can reveal much about the different processes acting on the near-surface environment. These processes can be studied with a multi-frequency/bandwidth approach applied to surface radar reflectometry measurements.Airborne ice-penetrating radar, traditionally designed to study the subsurface of ice sheets on Earth, can also be used to study the surface and near-surface ice. Upcoming missions to the Jovian icy moons will carry ice-penetrating radars, namely the Radar for Europa Assessment and Sounding: Ocean to Near-surface (REASON) on the Europa Clipper mission and the Radar for Icy Moons Exploration (RIME) on the JUpiter ICy moons Explorer (JUICE) mission. REASON will operate at center frequencies of 60 MHz and 9 MHz, with bandwidths of 10 MHz and 1 MHz, respectively. RIME will operate with only a center frequency of 9 MHz but with dual-bandwidth capabilities of 2.8 MHz and 1 MHz.The Radar Statistical Reconnaissance method was applied to dual-frequency/bandwidth radar observations collected over Devon Ice Cap, Canadian Arctic, to deconvolve the total surface power into its coherent (Pc) and incoherent (Pn) components. Both Pc and Pn are used to map the spatial distribution and constrain the vertical thickness of ice layers embedded within firn. We extend this approach to Ganymede and assess its utility for studying near-surface layering in the context of rough surfaces. We simulate the radar surface echo with a generalized version of the multilayer Stratton-Chu coherent simulator previously published, but now compute the scattering contributions from every frequency component within the bandwidth of the emitted chirp. Simulated data are shown to validate the assumptions of the insensitivity to surface roughness parameters representative of Ganymede, when observing with different bandwidths but at the same center frequencies. Finally, we outline strategies for using RIME and REASON together for near-surface reflectometry studies over planned observations of the Jovian icy moons. Using observations obtained with the frequencies and bandwidths from both radars, particularly at crossover locations, can provide valuable knowledge of the near-surface structure, even when the surface may appear rough.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".