In situ detection of boron by ChemCam on Mars

We report the first in situ detection of boron on Mars. Boron has been detected in Gale crater at levels Curiosity rover ChemCam instrument in calcium-sulfate-filled fractures, which formed in a late-stage groundwater circulating mainly in phyllosilicate-rich bedrock interpreted as lacustrine in origin. We consider two main groundwater-driven hypotheses to explain the presence of boron in the veins: leaching of borates out of bedrock or the redistribution of borate by dissolution of borate-bearing evaporite deposits. Our results suggest that an evaporation mechanism is most likely, implying that Gale groundwaters were mildly alkaline. On Earth, boron may be a necessary component for the origin of life; on Mars, its presence suggests that subsurface groundwater conditions could have supported prebiotic chemical reactions if organics were also present and provides additional support for the past habitability of Gale crater

The SuperCam Instrument Suite on the Mars 2020 Rover: Science Objectives and Mast-Unit Description

On the NASA 2020 rover mission to Jezero crater, the remote determination of the texture, mineralogy and chemistry of rocks is essential to quickly and thoroughly characterize an area and to optimize the selection of samples for return to Earth. As part of the Perseverance payload, SuperCam is a suite of five techniques that provide critical and complementary observations via Laser-Induced Breakdown Spectroscopy (LIBS), Time-Resolved Raman and Luminescence (TRR/L), visible and near-infrared spectroscopy (VISIR), high-resolution color imaging (RMI), and acoustic recording (MIC). SuperCam operates at remote distances, primarily 2-7 m, while providing data at sub-mm to mm scales. We report on SuperCam's science objectives in the context of the Mars 2020 mission goals and ways the different techniques can address these questions. The instrument is made up of three separate subsystems: the Mast Unit is designed and built in France; the Body Unit is provided by the United States; the calibration target holder is contributed by Spain, and the targets themselves by the entire science team. This publication focuses on the design, development, and tests of the Mast Unit; companion papers describe the other units. The goal of this work is to provide an understanding of the technical choices made, the constraints that were imposed, and ultimately the validated performance of the flight model as it leaves Earth, and it will serve as the foundation for Mars operations and future processing of the data.In France was provided by the Centre National d'Etudes Spatiales (CNES). Human resources were provided in part by the Centre National de la Recherche Scientifique (CNRS) and universities. Funding was provided in the US by NASA's Mars Exploration Program. Some funding of data analyses at Los Alamos National Laboratory (LANL) was provided by laboratory-directed research and development funds

Calibration and Evaluation of Blackbeard Time Tagging Capability

The Blackbeard instrument is a broadband radio receiver on the ALEXIS (Array of Low Energy X-ray Imaging Sensors) satellite. It detects and records transient VHF radio signals. During November and December of 1996, the Los Alamos Portable Pulser (LAPP) facility was used to transmit 31 broadband pulses to Blackbeard to evaluate the instrument\u27s time tagging accuracy. LAPP firing times were used in conjunction with propagation delays to compute estimated times of arrival (ETOAs) for pulses reaching Blackbeard. ETOAs were compared to Blackbeard reported times of arrival (RTOAs), which were computed using information returned by Blackbeard and an algorithm presented in this paper. For the 31 pulser shots received by Blackbeard, the mean difference between ETOA and RTOA was 1.97 milliseconds, with RTOAs occurring later than ETOAs. The standard deviation of the difference was 0.43 milliseconds. As a result of the study, the algorithm used for accurate Blackbeard time tag studies has been modified to subtract 1.97 milliseconds from reported times of arrival. The 0.43 ms error standard deviation is now used to describe the uncertainty of Blackbeard time tags

MULTIVARIATE AND ENSEMBLE MANGANESE CALIBRATION MODELS FOR SUPERCAM

International audienceOperator (LASSO) multivariate techniques with blended submodels; similar to the calibration model used from ChemCam [6], and then compared this model to ensemble methods [5,7]. Blended submodels split the data into smaller portions, trains linear models on these portions, and then optimizes the blend ranges of the submodels to cover the full data range [8]. The process of creating optimized submodels is time consuming, and may not yield the best model possible. Ensemble methods are non-linear, and would negate the need to train and optimize submodels. The response of the instrument to atomic emission is likely non-linear, and thus ensemble methods are likely to have better success in calibration than our previous attempts using LASSO, PLS, etc. Ensemble methods tested include Gradient Boosting, Random Forests, and Extra Trees [9]. Methods: Data Collection and Pre-processing. A standard set, for which MnO content is known, consisting of 252 training and 70 test standards, was analyzed using the SuperCam flight model from 1.6 m distance (3 average spectra were collected on each standard consisting of 50 shots averaged in each point) under a Mars-like atmosphere [5]. The standard set covers a range of Mn compositions from 0.0009-76 wt% MnO and contains a variety of rock matrices (e.g., rock, mineral, Mn ores). No outliers were removed. We use the Python Hyperspectral Analysis Tool [10] and the associated graphical interface for point spectra analysis [10] to preprocess the data and evaluate multivariate regression models. Ensemble methods were trained using Python scikit-learn [7,9]. Each spectrum is normalized by the sum of the total emission for each detector [5]. A "peak area" (PA) preprocessing technique is used [6], where local minima and maxima of the average spectra of the dataset is determined. The process then bins the emission between each pair of minima and assigns the result to the wavelength of the corresponding maximum. We compared full spectra with peak area spectra for this work. Based on preliminary work, we masked wavelengths ≥750 nm, where there are no Mn emission lines, to remove lines from alkali, minor elements, and oxygen, all of which had some influence on the LASSO model

MULTIVARIATE AND ENSEMBLE MANGANESE CALIBRATION MODELS FOR SUPERCAM

International audienceOperator (LASSO) multivariate techniques with blended submodels; similar to the calibration model used from ChemCam [6], and then compared this model to ensemble methods [5,7]. Blended submodels split the data into smaller portions, trains linear models on these portions, and then optimizes the blend ranges of the submodels to cover the full data range [8]. The process of creating optimized submodels is time consuming, and may not yield the best model possible. Ensemble methods are non-linear, and would negate the need to train and optimize submodels. The response of the instrument to atomic emission is likely non-linear, and thus ensemble methods are likely to have better success in calibration than our previous attempts using LASSO, PLS, etc. Ensemble methods tested include Gradient Boosting, Random Forests, and Extra Trees [9]. Methods: Data Collection and Pre-processing. A standard set, for which MnO content is known, consisting of 252 training and 70 test standards, was analyzed using the SuperCam flight model from 1.6 m distance (3 average spectra were collected on each standard consisting of 50 shots averaged in each point) under a Mars-like atmosphere [5]. The standard set covers a range of Mn compositions from 0.0009-76 wt% MnO and contains a variety of rock matrices (e.g., rock, mineral, Mn ores). No outliers were removed. We use the Python Hyperspectral Analysis Tool [10] and the associated graphical interface for point spectra analysis [10] to preprocess the data and evaluate multivariate regression models. Ensemble methods were trained using Python scikit-learn [7,9]. Each spectrum is normalized by the sum of the total emission for each detector [5]. A "peak area" (PA) preprocessing technique is used [6], where local minima and maxima of the average spectra of the dataset is determined. The process then bins the emission between each pair of minima and assigns the result to the wavelength of the corresponding maximum. We compared full spectra with peak area spectra for this work. Based on preliminary work, we masked wavelengths ≥750 nm, where there are no Mn emission lines, to remove lines from alkali, minor elements, and oxygen, all of which had some influence on the LASSO model

Quantification of manganese for ChemCam Mars and laboratory spectra using a multivariate model

International audienc

Dataset for the manuscript Manganese-rich sandstones as an indicator of ancient oxic lake water conditions in Gale crater, Mars

<p>This file is a csv file containing the average composition of rocks in the Murray formation in Gale crater, as determined by ChemCam, and metadata, for the manuscript <em>Manganese-rich sandstones as an indicator of ancient oxic lake water conditions in Gale crater, Mars</em> submitted to the Journal of Geophysical Research: Planets. The file contains the major oxide composition data for each observation point (comprised of at least 30 shots where the first 5 are removed due to Mars dust), the root mean squared error (RMSE) for each ChemCam observation point, and the standard deviation of the shot-to-shot data of that observation point (shot_stdev), and the sum of oxides of the observation point. The metadata includes the ‘Plot Label’ to indicate the category of the target (Dark Sandstones, Light Sandstones, Concretion, Other Diagenetic, Murray) as indicated in Figure 1 of the manuscript. The data was also used to produce Table 1. The filename, sol, spacecraft clock, target name, target distance from ChemCam, laser power, spectral totals, elevation, longitude, latitude, easting, northing, and notes metadata columns are provided for each observation point. The observations points are in order of ‘Plot Label’ and then by Spacecraft Clock.</p&gt

Initial Major Element Quantification Using SuperCam Laser Induced Breakdown Spectroscopy

International audienceSuperCam uses Laser Induced Breakdown Spectroscopy (LIBS) to collect atomic emission spectra from targets up to ~7 meters from the Perseverance rover. Due to the complexity of LIBS physics and the diversity of geologic materials, we use an empirical approach to major element (SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O) quantification, based on a suite of 1198 SuperCam laboratory spectra of 334 standards, including the rover calibration targets. SuperCam LIBS spectra are pre-processed by subtracting "dark" (passive/non-LIBS) spectra, denoising, continuum removal, instrument response correction, conversion to radiance, and wavelength calibration. For quantification, the spectra are masked to remove noisy sections of the spectrum and normalized by dividing signal in each spectrometer by the total signal from that spectrometer. We also found that the additional preprocessing steps of peak binning and/or per-channel standardization improved the results in some cases. These data are used to train multivariate regression models, with parameters optimized using cross-validation to avoid overfitting. We considered a variety of regression algorithms including Partial Least Squares (PLS), Least Absolute Selection and Shrinkage Operator (LASSO), Ridge, Elastic Net, Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting Regression (GBR), Local Elastic Net, and blended sub-models. Models were selected based on test-set performance, accuracy of predictions of the onboard calibration targets, comparison of Mars and laboratory spectra, and the geochemical plausibility of Mars results. In some cases we found that the average of the predictions of several algorithms gave better results than any single method. Accuracy of predictions is estimated as the root mean squared error of prediction (RMSEP) for the test set. As additional spectra are collected from Mars, we continue to validate and improve upon this initial SuperCam elemental quantification. Areas of investigation include calibration transfer, probabilistic regression methods, and regression models for additional elements.Figure 1: Test set predictions vs actual compositions for each major element. Perfect predictions would fall on the line. RMSEP measures the accuracy of the model in wt.%

Initial Major Element Quantification Using SuperCam Laser Induced Breakdown Spectroscopy

International audienceSuperCam uses Laser Induced Breakdown Spectroscopy (LIBS) to collect atomic emission spectra from targets up to ~7 meters from the Perseverance rover. Due to the complexity of LIBS physics and the diversity of geologic materials, we use an empirical approach to major element (SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O) quantification, based on a suite of 1198 SuperCam laboratory spectra of 334 standards, including the rover calibration targets. SuperCam LIBS spectra are pre-processed by subtracting "dark" (passive/non-LIBS) spectra, denoising, continuum removal, instrument response correction, conversion to radiance, and wavelength calibration. For quantification, the spectra are masked to remove noisy sections of the spectrum and normalized by dividing signal in each spectrometer by the total signal from that spectrometer. We also found that the additional preprocessing steps of peak binning and/or per-channel standardization improved the results in some cases. These data are used to train multivariate regression models, with parameters optimized using cross-validation to avoid overfitting. We considered a variety of regression algorithms including Partial Least Squares (PLS), Least Absolute Selection and Shrinkage Operator (LASSO), Ridge, Elastic Net, Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting Regression (GBR), Local Elastic Net, and blended sub-models. Models were selected based on test-set performance, accuracy of predictions of the onboard calibration targets, comparison of Mars and laboratory spectra, and the geochemical plausibility of Mars results. In some cases we found that the average of the predictions of several algorithms gave better results than any single method. Accuracy of predictions is estimated as the root mean squared error of prediction (RMSEP) for the test set. As additional spectra are collected from Mars, we continue to validate and improve upon this initial SuperCam elemental quantification. Areas of investigation include calibration transfer, probabilistic regression methods, and regression models for additional elements.Figure 1: Test set predictions vs actual compositions for each major element. Perfect predictions would fall on the line. RMSEP measures the accuracy of the model in wt.%