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Astrobiology. 2015 Nov;15(11):961-76. doi: 10.1089/ast.2015.1349.

Automating X-ray Fluorescence Analysis for Rapid Astrobiology Surveys.

Author information

1
1 Jet Propulsion Laboratory, California Institute of Technology , Pasadena, California.
2
2 Imaging Spectroscopy, Jet Propulsion Laboratory, California Institute of Technology .
3
3 Planetary Chemistry and Astrobiology, Jet Propulsion Laboratory, California Institute of Technology .
4
4 Machine Learning and Instrument Autonomy, Jet Propulsion Laboratory, California Institute of Technology .
5
5 Planetary Chemistry and Astrobiology, Jet Propulsion Laboratory, California Institute of Technology .
6
6 Space Science Institute , Boulder, Colorado.
7
7 Applied Physics Laboratory, University of Washington , Seattle, Washington.
8
8 Planetary Ices, Jet Propulsion Laboratory, California Institute of Technology .
9
9 Department of Geosciences, Stony Brook University , Stony Brook, New York.
10
10 Geophysics and Planetary Geosciences, Jet Propulsion Laboratory, California Institute of Technology .
11
11 Instrument System Engineering, Jet Propulsion Laboratory, California Institute of Technology .

Abstract

A new generation of planetary rover instruments, such as PIXL (Planetary Instrument for X-ray Lithochemistry) and SHERLOC (Scanning Habitable Environments with Raman Luminescence for Organics and Chemicals) selected for the Mars 2020 mission rover payload, aim to map mineralogical and elemental composition in situ at microscopic scales. These instruments will produce large spectral cubes with thousands of channels acquired over thousands of spatial locations, a large potential science yield limited mainly by the time required to acquire a measurement after placement. A secondary bottleneck also faces mission planners after downlink; analysts must interpret the complex data products quickly to inform tactical planning for the next command cycle. This study demonstrates operational approaches to overcome these bottlenecks by specialized early-stage science data processing. Onboard, simple real-time systems can perform a basic compositional assessment, recognizing specific features of interest and optimizing sensor integration time to characterize anomalies. On the ground, statistically motivated visualization can make raw uncalibrated data products more interpretable for tactical decision making. Techniques such as manifold dimensionality reduction can help operators comprehend large databases at a glance, identifying trends and anomalies in data. These onboard and ground-side analyses can complement a quantitative interpretation. We evaluate system performance for the case study of PIXL, an X-ray fluorescence spectrometer. Experiments on three representative samples demonstrate improved methods for onboard and ground-side automation and illustrate new astrobiological science capabilities unavailable in previous planetary instruments.

KEY WORDS:

Dimensionality reduction-Planetary science-Visualization.

PMID:
26575217
DOI:
10.1089/ast.2015.1349
[Indexed for MEDLINE]

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