ALBERT

Description: Laser-induced breakdown spectroscopy (LIBS) uses pulses of laser light to ablate a material from the surface of a sample and produce an expanding plasma. The optical emission from the plasma produces a spectrum which can be used to classify target materials and estimate their composition. The ChemCam instrument on the Mars Science Laboratory (MSL) mission will use LIBS to rapidly analyze targets remotely, allowing more resource- and time-intensive in-situ analyses to be reserved for targets of particular interest. ChemCam will also be used to analyze samples that are not reachable by the rover's in-situ instruments. Due to these tactical and scientific roles, it is important that ChemCam-derived sample compositions are as accurate as possible. We have compared the results of partial least squares (PLS), multilayer perceptron (MLP) artificial neural networks (ANNs), and cascade correlation (CC) ANNs to determine which technique yields better estimates of quantitative element abundances in rock and mineral samples. The number of hidden nodes in the MLP ANNs was optimized using a genetic algorithm. The influence of two data preprocessing techniques were also investigated: genetic algorithm feature selection and averaging the spectra for each training sample prior to training the PLS and ANN algorithms. We used a ChemCam-like laboratory stand-off LIBS system to collect spectra of 30 pressed powder geostandards and a diverse suite of 196 geologic slab samples of known bulk composition. We tested the performance of PLS and ANNs on a subset of these samples, choosing to focus on silicate rocks and minerals with a loss on ignition of less than 2 percent. This resulted in a set of 22 pressed powder geostandards and 80 geologic samples. Four of the geostandards were used as a validation set and 18 were used as the training set for the algorithms. We found that PLS typically resulted in the lowest average absolute error in its predictions, but that the optimized MLP ANN and the CC ANN often gave results comparable to PLS. Averaging the spectra for each training sample and/or using feature selection to choose a small subset of wavelengths to use for predictions gave mixed results, with degraded performance in some cases and similar or slightly improved performance in other cases. However, training time was significantly reduced for both PLS and ANN methods by implementing feature selection, making this a potentially appealing method for initial, rapid-turn-around analyses necessary for Chemcam's tactical role on MSL. Choice of training samples has a strong influence on the accuracy of predictions. We are currently investigating the use of clustering algorithms (e.g. k-means, neural gas, etc.) to identify training sets that are spectrally similar to the unknown samples that are being predicted, and therefore result in improved predictions

Description: Absolute dating of planetary samples is an essential tool to establish the chronology of geological events, including crystallization history, magmatic evolution, and alteration. Traditionally, geochronology has only been accomplishable on samples from dedicated sample return missions or meteorites. The capability for in situ geochronology is highly desired, because it will allow one-way planetary missions to perform dating of large numbers of samples. The success of an in situ geochronology package will not only yield data on absolute ages, but can also complement sample return missions by identifying the most interesting rocks to cache and/or return to Earth. In situ dating instruments have been proposed, but none have yet reached TRL 6 because the required high-resolution isotopic measurements are very challenging. Our team is now addressing this challenge by developing the Potassium (K) - Argon Laser Experiment (KArLE) under the NASA Planetary Instrument Definition and Development Program (PIDDP), building on previous work to develop a K-Ar in situ instrument [1]. KArLE uses a combination of several flight-proven components that enable accurate K-Ar isochron dating of planetary rocks. KArLE will ablate a rock sample, determine the K in the plasma state using laser-induced breakdown spectroscopy (LIBS), measure the liberated Ar using quadrupole mass spectrometry (QMS), and relate the two by the volume of the ablated pit using an optical method such as a vertical scanning interferometer (VSI). Our preliminary work indicates that the KArLE instrument will be capable of determining the age of several kinds of planetary samples to +/-100 Myr, sufficient to address a wide range of geochronology problems in planetary science.

Description: Onboard the Mars Science Laboratory (MSL) Curiosity rover, the ChemCam instrument consists of :(1) a Laser-Induced Breakdown Spectrometer (LIBS) for elemental analysis of the targets [1;2] and (2) a Remote Micro Imager (RMI), for the imaging context of laser analysis [3]. Within the Gale crater, Curiosity traveled from Bradbury Landing through the Rocknest region and into Yellowknife Bay (YB). In the latter, abundant light-toned fracture-fill material were seen [4;5]. ChemCam analysis demonstrate that those fracture fills consist of calcium sulfates [6].