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Record W3100382986 · doi:10.5194/epsc2020-388

Monte Carlo simulations of the expospheric transport of cometary volatiles on the Moon

2020· article· en· W3100382986 on OpenAlexaff
Jacob L. Kloos, John E. Moores, Norbert Schörghofer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsYork University
Fundersnot available
KeywordsChemistry

Abstract

fetched live from OpenAlex

Permanently shadowed regions (PSRs) are areas of a planetary surface that lie in continual shadow from direct sunlight. Their existence at the lunar polar regions has been recognized for nearly 70 years [1] and in the intervening time much has been learned about their unique thermal environment and capacity for volatile preservation [2]. In the absence of direct sunlight and without an atmosphere to transport and trap heat, lunar PSRs remain cold throughout the year, with maximum temperatures typically below ~110 K, although temperatures as low as 45 K have been reported in some areas [3]. At these low temperatures, PSRs can act as cold traps for H2O water ice as sublimation rates are negligibly low (~1 mm Gyr-1). In addition to H2O, other volatile species, such as CO2, NH3, H2S, SO2, and CH4 are regularly supplied to the Moon through cometary impacts or are created through solar wind interactions. These species have been observed in varying abundances by the Lunar CRater Observation and Sensing Satellite (LCROSS) experiment within Cabeus crater near the south pole [4]. Once delivered or produced, these molecules may migrate about the lunar surface through a series of ballistic hops and potentially accumulate within cold traps near the poles if temperatures are sufficiently low. Relative to H2O, however, these volatiles have higher vapor pressures and thus require lower temperatures for long-term thermodynamic stability; thus, not all volatiles detected in the LCROSS plume are expected to be cold trapped in the current lunar thermal environment. CH4, for example, which has been detected in the lunar exosphere [5], is stable at temperatures below ~25 K [6], which is too low to be cold trapped, although it can be adsorbed on the surface. Other volatiles, in contrast, such as CO2, are stable at relatively higher temperatures (Tmax < 55 K) and potentially accumulate within the coldest regions of permanent shadow. Observational evidence for CO2 frost has recently been provided by the Lyman Alpha Mapping Project (LAMP) instrument on the Lunar Reconnaissance Orbiter (LRO) [6]. Although Diviner temperature data do not indicate significantly large regions where CO2 is stable, micro cold traps (at cm scales) will provide additional cold trapping area. Modelling the diurnal and seasonal migration patterns of different exospheric volatiles can shed light on geotemporal trends in volatile dispersion and cold trapping [7, 8, 9, 10], and may additionally aid in the interpretation of orbital remote sensing data. In this work, we use a Monte Carlo model to simulate the ballistic migration of the aforementioned cometary volatiles to understand differences in their migration, destruction and cold trap capture. The model utilized here is similar to that described in Kloos et al. [11]. Individual molecules of a given volatile are placed on the surface at non-polar latitudes (equatorward of ±80°) using a randomized production scheme. The molecule is assumed to achieve instantaneous thermal equilibrium with the lunar regolith and acquire the local surface temperature. For surface locations equatorward of ±80°, temperatures are obtained using g­­­­lobal, topographically resolved Diviner temperature maps [12]. Due to the slight obliquity of the Moon (< 1.59°), however, the polar temperatures can vary significantly throughout the year. Thus, we have updated the model to include the recently available seasonal Diviner polar temperature data created by Williams et al. [13]. These maps enable more realistic simulations of the ballistic polar migration than that reported by Kloos et al. [11]. To calculate the adsorption residence time, τ, for a molecule, we use the relationship defined by Langmuir [14]: τ = (1/ν0)exp(Ea/kBTsurf), (1) where ν0 is the vibrational frequency, Ea is the activation energy and Tsurf is the surface temperature. The variables ν0 and Ea are obtained for each volatile using data from Sandford and Allamandola [15]. Once molecules are released, they inherit a velocity vector using three-dimensional cartesian coordinates, where the vector direction is randomized and the speed is drawn from an Armand distribution. Molecules ejected outward from the surface may be photodissociated through interaction with solar UV photons. Photo-destruction rates for each species are determined using data compiled by Huebner et al., [16], derived for normal sun activity. The effects of surface roughness, which may delay the pole-ward migration of molecules by increasing the number of hops at a given location, are incorporated into the model and we quantify these effects on the velocity distribution for different volatile species. Figure 1 shows the north and south geographic delivery patterns for H2O, where the y-axis gives the PSR particle concentration σp normalized by the production rate γ. It is found that the north/south asymmetry in PSR capture reported by Kloos et al., [11] persists using the updated Diviner polar temperature data. The bulk majority (~82%) of H2O molecules are destroyed through photolysis, while the remaining are cold trapped in PSRs (

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.025
GPT teacher head0.190
Teacher spread0.165 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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