ExoMars Raman laser spectrometer for Exomars

Rull, Fernando; Sansano, A.; Dı́az, Eva; Canora, C. P.; Moral, Andoni; Tato, C.; Colombo, M.; Belenguer, T.; Fernández, M.; Manfredi, José Antonio Rodríguez; Canchal, R.; Dávila, B.; Jiménez, A.; Gallego, P.; Ibarmia, S.; Prieto, J. A. R.; Santiago, A.; Plá, Juan Carlos; Ramos, G.; Díaz, Carlos; González, C.

doi:10.1117/12.896787

Cited by 30 publications

(29 citation statements)

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“…The 2010 campaign was comprised of 33 people and 8 instruments (Table 1): SAM (Mahaffy, 2008), CheMin , PanCam (Amundsen et al, 2010), MOMA (Goetz et al, 2011), Raman/LIBS (Rull et al, 2011), SPDS Rock Crusher, CLUPI (Josset et al, 2011), and VAPoR (ten Kate et al, 2010). This year's campaign focused on two sites, described below.…”

Section: Brief Overview Of the Amase 2010 Campaignmentioning

confidence: 99%

Isotopic and geochemical investigation of two distinct Mars analog environments using evolved gas techniques in Svalbard, Norway

Stern¹,

McAdam²,

Kate³

et al. 2013

Icarus

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Section: Brief Overview Of the Amase 2010 Campaignmentioning

confidence: 99%

Isotopic and geochemical investigation of two distinct Mars analog environments using evolved gas techniques in Svalbard, Norway

Stern¹,

McAdam²,

Kate³

et al. 2013

Icarus

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“…The ExoMars RLS will analyse crushed soil samples retrieved from the martian near subsurface by the rover's drill. RLS uses green excitation light at a wavelength of 532 nm, and the spectrometer unit offers a spectral range of 200-3,800 cm −1 (covering both mineralogical and biological signals), at a spectral resolution of 6-8 cm −1 [13].…”

Section: )mentioning

confidence: 99%

Destruction of Raman biosignatures by ionising radiation and the implications for life detection on Mars

et al. 2012

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Raman spectroscopy has proven to be a very effective approach for the detection of microorganisms colonising hostile environments on Earth. The ExoMars rover, due for launch in 2018, will carry a Raman laser spectrometer to analyse samples of the martian subsurface collected by the probe's 2-m drill in a search for similar biosignatures. The martian surface is unprotected from the flux of cosmic rays, an ionising radiation field that will degrade organic molecules and so diminish and distort the detectable Raman signature of potential martian microbial life. This study employs Raman spectroscopy to analyse samples of two model organisms, the cyanobacterium Synechocystis sp. PCC 6803 and the extremely radiation resistant polyextremophile Deinococcus radiodurans, that have been exposed to increasing doses of ionising radiation. The three most prominent peaks in the Raman spectra are from cellular carotenoids: deinoxanthin in D. radiodurans and β-carotene in Synechocystis. The degradative effect of ionising radiation is clearly seen, with significant diminishment of carotenoid spectral peak heights after 15 kGy and complete erasure of Raman biosignatures by 150 kGy of ionising radiation. The Raman signal of carotenoid in D. radiodurans diminishes more rapidly than that of Synechocystis, believed to be due to deinoxanthin acting as a superior scavenger of radiolytically produced reactive oxygen species, and so being destroyed more quickly than the less efficient antioxidant β-carotene. This study highlights the necessity for further experimental work on the manner and rate of degradation of Raman biosignatures by ionising radiation, as this is of prime importance for the successful detection of microbial life in the martian near subsurface.

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“…[8] A Raman laser spectrometer (RLS) is part of the science instrument payload of the European Space Agency 2018 ExoMars mission; the RLS instrument will target mineralogical and astrobiological investigations on the surface and subsurface of Mars. [9,10] Raman will also be used on the upcoming NASA Mars 2020 mission as part of the SuperCam and SHERLOC instruments. [11] What all these applications have in common is their dependence on software and mineralogical databases for phase identification and quantification of relative abundances of mineral components.…”

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confidence: 99%

Machine learning tools formineral recognition and classification from Raman spectroscopy

Carey

Boucher

Mahadevan

et al. 2015

J Raman Spectroscopy

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Tools for mineral identification based on Raman spectroscopy fall into two groups: those that are largely based on fits to diagnostic peaks associated with specific phases, and those that use the entire spectral range for multivariate analyses. In this project, we apply machine learning techniques to improve mineral identification using the latter group. We test the effects of common spectrum preprocessing steps, such as intensity normalization, smoothing, and squashing, and found that the last is superior. Next, we demonstrate that full-spectrum matching algorithms exhibit excellent performance in classification tasks, without requiring time-intensive dimensionality reduction or model training. This class of algorithms supports both vector and trajectory input formats, exploiting all available spectral information. By combining these insights, we find that optimal mineral spectrum matching performance can be achieved using careful preprocessing and a weighted-neighbors classifier based on a vector similarity metric. IntroductionUse of Raman spectroscopy in the geosciences is growing rapidly, as evidenced by burgeoning publications in the fields of geology, cultural anthropology, environmental science, and, in particular, planetary science. Raman spectrometers have been proposed for exploration of a diverse range of extraterrestrial targets including asteroids, [1] Europa, [2,3] Mars, [4][5][6] the Moon, [7] and Venus. [8] A Raman laser spectrometer (RLS) is part of the science instrument payload of the European Space Agency 2018 ExoMars mission; the RLS instrument will target mineralogical and astrobiological investigations on the surface and subsurface of Mars. [9,10] Raman will also be used on the upcoming NASA Mars 2020 mission as part of the SuperCam and SHERLOC instruments. [11] What all these applications have in common is their dependence on software and mineralogical databases for phase identification and quantification of relative abundances of mineral components. Because of the structural diversity and chemical complexity of naturally occurring minerals, optimal applications for these purposes require an infusion of work into development of appropriate software and mineral databases.In practice, users commonly depend on a combination of matching software distributed by spectrometer manufacturers and one-on-one comparisons with database spectra for their identifications, but these types of identification have two critical limitations. First, identifications are only as good as the databases used to match them. No database can be entirely comprehensive, so it cannot be unequivocally claimed that any spectrum is a 'perfect match' to exactly one other phase.The RRUFF database was founded in 2006 at Arizona State University by Robert Downs to remedy this situation by providing coverage of all known mineral species. [12] It has quickly become the preeminent resource available for Raman spectra of minerals. RRUFF currently contains over 20 378 spectra acquired at several different laser wavelengths from orien...

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ExoMars Raman laser spectrometer for Exomars

Cited by 30 publications

References 0 publications

Isotopic and geochemical investigation of two distinct Mars analog environments using evolved gas techniques in Svalbard, Norway

Isotopic and geochemical investigation of two distinct Mars analog environments using evolved gas techniques in Svalbard, Norway

Destruction of Raman biosignatures by ionising radiation and the implications for life detection on Mars

Machine learning tools formineral recognition and classification from Raman spectroscopy

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