The Raman laser spectrometer ExoMars simulator (RLS Sim): A heavy‐duty Raman tool for ground testing on ExoMars
Bibliographic record
Abstract
Abstract The Raman laser spectrometer (RLS) instrument onboard the Rosalind Franklin rover of the ExoMars 2022 mission will analyze powdered samples on Mars to search for traces of life. To prepare for the mission, the RLS scientific team has developed the RLS ExoMars Simulator (RLS Sim), a flexible model of RLS that operates similarly to the actual instrument, both in laboratory and field conditions, while also emulating the rover operational constraints in terms of sample distribution that are relevant to the Raman analysis. This system can operate autonomously to perform RLS‐representative analysis in one or several samples, making it very useful to perform heavy experimental tasks that would otherwise be impossible using a flight‐representative model of the instrument. In this work, we introduce the current configuration of the RLS Sim that has incorporated new hardware elements such as the RAman Demonstrator 1 (RAD1) spectrometer with the objective of approaching its performance to that of the actual RLS instrument. To evaluate the scientific capability of the RLS Sim, we have compared it with a replica model of RLS, the RLS Flight Spare (FS). Several acquisition aspects have been evaluated based on the analysis of select samples, assessing the performance in terms of spectral range and resolution and also studying several issues related to the evolution of signal‐to‐noise ratio (SNR) with different acquisition parameters, especially the number of accumulations. This performance analysis has shown that the RLS Sim in its updated configuration will be a key model to perform support science for the ExoMars mission and the RLS instrument on the Rosalind Franklin rover. Designed to work intensively, the use of the RLS Sim in combination with the RLS FS will facilitate maximizing the scientific return of the RLS spectrometer during Martian operations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".