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NASA NTRS2016

The Search for Life in the Universe

Each recent report of liquid water existing elsewhere in the solar system has reverberated through the international press and excited the imagination of humankind. We have come to realize that where there is liquid water on Earth, virtually no matter what the physical conditions, there is life. Dr. Lynn Rothschild, an evolutionary biologist known for her work on life in extreme environments and a founder of the field of astrobiology, tells us about intriguing new data. The prevalence of potential abodes for life in our solar system and beyond, the survival of microbes in the space environment, modeling of the potential for transfer of life between celestial bodies, and advances in synthetic biology suggest that life could be more common than previously thought. Are we truly "alone"?

Life in the Universe↗
NASA NTRS2015

The Search for Life in the Universe

Each recent report of liquid water existing elsewhere in the solar system has reverberated through the international press and excited the imagination of humankind. We have come to realize that where there is liquid water on Earth, virtually no matter what the physical conditions, there is life. Dr. Lynn Rothschild, an evolutionary biologist known for her work on life in extreme environments and a founder of the field of astrobiology, tells us about intriguing new data. At NASA's Ames Research Center in Moffett Field, CA, she leads a program in synthetic biology and works with researchers in industry, government agencies around the world and in academia, including at Brown and Stanford University. The prevalence of potential abodes for life in our solar system and beyond, the survival of microbes in the space environment, modeling of the potential for transfer of life between celestial bodies, and advances in synthetic biology suggest that life could be more common than previously thought. Are we truly "alone"?

Water↗
NASA NTRS2017

Are We Alone? The Search for Life in the Universe

Each report of liquid water existing elsewhere in the solar system has reverberated through the international press and excited the imagination of humankind. We have come to realize that where there is liquid water on Earth, virtually no matter what the physical conditions, there is life. Dr. Lynn Rothschild, an evolutionary biologist known for her work on life in extreme environments and a founder of the field of astrobiology, tells us about intriguing new data. The prevalence of potential abodes for life in our solar system and beyond, the survival of microbes in the space environment, modeling of the potential for transfer of life between celestial bodies, and advances in synthetic biology suggest that life could be more common than previously thought. Are we truly alone?

solar system↗
NASA NTRS2022

Science Autonomy for Ocean Worlds Astrobiology: A Perspective

Astrobiology missions to ocean worlds in our solar system must overcome both scientific and technological challenges due to extreme temperature and radiation conditions, long communication times, and limited bandwidth. While such tools could not replace ground-based analysis by science and engineering teams, machine learning algorithms could enhance the science return of these missions through development of autonomous science capabilities. Examples of science autonomy include onboard data analysis and subsequent instrument optimization, data prioritization (for transmission), and real-time decision-making based on data analysis. Similar advances could be made to develop streamlined data processing software for rapid ground-based analyses. Here we discuss several ways machine learning and autonomy could be used for astrobiology missions, including landing site selection, prioritization and targeting of samples, classification of “features” (e.g., proposed biosignatures) and novelties (uncharacterized, “new” features, which may be of most interest to agnostic astrobiological investigations), and data transmission.

ocean worlds↗
NASA NTRS2012

SEVO (Space Environment Viability of Organics) Preliminary Results from Orbit

SEVO (Space Environment Viability of Organics) is one of two astrobiology experiments onboard the NASA Organism/Organics Exposure to Orbital Stresses (O/OREOS) cubesat, launched in November 2010. The satellite is still operational with nominal performance and records data on a regular basis. In the SEVO experiment, four astrobiologically relevant organic thin films are exposed to radiation in low-earth orbit, including the unfiltered solar spectrum from approximately 120 - 2600 nm. The thin films are contained in each of four separate micro-environments: an atmosphere containing CO2, a low relative humidity (approximately 2%) atmosphere, an inert atmosphere representative of interstellar/interplanetary space, and a SiO2 mineral surface to measure the effects of surface catalysis. The UV/Vis spectrum of each sample is monitored in situ, with a spectrometer onboard the satellite.

Cook, A.↗
NASA NTRS2011

Earth as an Extrasolar Planet: Earth Model Validation Using EPOXI Earth Observations

The EPOXI Discovery Mission of Opportunity reused the Deep Impact flyby spacecraft to obtain spatially and temporally resolved visible photometric and moderate resolution near-infrared (NIR) spectroscopic observations of Earth. These remote observations provide a rigorous validation of whole disk Earth model simulations used to better under- stand remotely detectable extrasolar planet characteristics. We have used these data to upgrade, correct, and validate the NASA Astrobiology Institute s Virtual Planetary Laboratory three-dimensional line-by-line, multiple-scattering spectral Earth model (Tinetti et al., 2006a,b). This comprehensive model now includes specular reflectance from the ocean and explicitly includes atmospheric effects such as Rayleigh scattering, gas absorption, and temperature structure. We have used this model to generate spatially and temporally resolved synthetic spectra and images of Earth for the dates of EPOXI observation. Model parameters were varied to yield an optimum fit to the data. We found that a minimum spatial resolution of approx.100 pixels on the visible disk, and four categories of water clouds, which were defined using observed cloud positions and optical thicknesses, were needed to yield acceptable fits. The validated model provides a simultaneous fit to the Earth s lightcurve, absolute brightness, and spectral data, with a root-mean-square error of typically less than 3% for the multiwavelength lightcurves, and residuals of approx.10% for the absolute brightness throughout the visible and NIR spectral range. We extend our validation into the mid-infrared by comparing the model to high spectral resolution observations of Earth from the Atmospheric Infrared Sounder, obtaining a fit with residuals of approx.7%, and temperature errors of less than 1K in the atmospheric window. For the purpose of understanding the observable characteristics of the distant Earth at arbitrary viewing geometry and observing cadence, our validated forward model can be used to simulate Earth s time dependent brightness and spectral properties for wavelengths from the far ultraviolet to the far infrared.brightness

Robinson, Tyler D.↗
NASA NTRS2001

Astrobiology Investigations at a Martian Hematite Site

Christensen et al, using data from the Mars Global Surveyor Thermal Emission Spectrometer (TES), have identified gray crystalline hematite in a 350 km by 750 km region near Sinus Meridiani. The deposit corresponds closely to the low-albedo highlands unit 'sm', mapped as a wind-eroded, ancient, subaqueous sedimentary deposit. Christensen et al interpreted the Sinus Meridiani deposit to be 'an in-place, rock-stratigraphic sedimentary unit characterized by smooth, friable layers composed primarily of basaltic sediments with approximately 10 to 15 % crystalline gray hematite.' Christensen et al discussed five possible mechanisms for the formation of this deposit: direct precipitation from standing, oxygenated, Fe-rich water; precipitation from Fe-rich hydrothermal fluids; low-temperature dissolution and precipitation through mobile groundwater leaching; surface weathering and coatings; thermal oxidation of magnetite-rich lavas. Four of these mechanisms involve the interactions of rock with water, and thus have implications in the search for evidence of microbial life.

Allen, Carlton, C.↗
NASA NTRS2016

Biomolecular Analysis Capability for Cellular and Omics Research on the International Space Station

International Space Station (ISS) assembly complete ushered a new era focused on utilization of this state-of-the-art orbiting laboratory to advance science and technology research in a wide array of disciplines, with benefits to Earth and space exploration. ISS enabling capability for research in cellular and molecular biology includes equipment for in situ, on-orbit analysis of biomolecules. Applications of this growing capability range from biomedicine and biotechnology to the emerging field of Omics. For example, Biomolecule Sequencer is a space-based miniature DNA sequencer that provides nucleotide sequence data for entire samples, which may be used for purposes such as microorganism identification and astrobiology. It complements the use of WetLab-2 SmartCycler"TradeMark", which extracts RNA and provides real-time quantitative gene expression data analysis from biospecimens sampled or cultured onboard the ISS, for downlink to ground investigators, with applications ranging from clinical tissue evaluation to multigenerational assessment of organismal alterations. And the Genes in Space-1 investigation, aimed at examining epigenetic changes, employs polymerase chain reaction to detect immune system alterations. In addition, an increasing assortment of tools to visualize the subcellular distribution of tagged macromolecules is becoming available onboard the ISS. For instance, the NASA LMM (Light Microscopy Module) is a flexible light microscopy imaging facility that enables imaging of physical and biological microscopic phenomena in microgravity. Another light microscopy system modified for use in space to image life sciences payloads is initially used by the Heart Cells investigation ("Effects of Microgravity on Stem Cell-Derived Cardiomyocytes for Human Cardiovascular Disease Modeling and Drug Discovery"). Also, the JAXA Microscope system can perform remotely controllable light, phase-contrast, and fluorescent observations. And upcoming confocal microscopy capability will allow for optical sectioning of biological tissues to determine microanatomical localization of biomarkers. Furthermore, NASA's geneLAB effort addresses integration of genomic, epigenomic, transcriptomic, proteomic and metabolomic datasets, by applying an innovative open source science platform for multi-investigator high throughput utilization of the ISS. In sum, the expanding ISS capability for analysis of biomolecules is enabling innovative research in a broad spectrum of areas such as cellular and molecular biology, biotechnology, tissue engineering, biomedicine, and Omics, providing manifold benefits for humanity.

Guinart-Ramirez, Y.↗
NASA NTRSDate not supplied

Statistical Classification of Biosignature Information using Multiple Instrument Observations

The accurate identification of biosignatures (indications of life) from data taken from remote or in situ planetary exploration is one of the most important challenges in astrobiology, the interdisciplinary field examining habitability and the potential for extraterrestrial life. This study employs machine learning algorithms to optimize the identification of biosignatures, with an emphasis on those which are agnostic to a specific biochemical basis. We exploit the wealth of terrestrial data available from biogenic and abiogenic systems to enhance efficient feature prioritization. Our dataset, pulled from public databases and laboratory recorded measurements, includes elemental abundance, isotopic fractionation, and VNIR/Raman spectra The data curation process included standardization for detection limits and ranges. Subsequent feature extraction yielded detailed inputs for machine learning, including combinations of elemental content, isotopic ratios, and parameters of spectral peaks and troughs. Feature significance was evaluated across diverse machine learning methodologies, such as k-nearest neighbors, logistic regression, Random Forest, support vector machines, and Gaussian Naïve Bayes, along with a combined voting classifier. We utilized Receiver Operating Characteristic Area Under the Curve (ROC AUC) across 2,000 50% test-train splits as a robust metric of model performance. Results revealed a promising ROC AUC of 0.853 for the combined voting classifier. Removing elemental abundance data notably reduced model accuracy (13% decrease in AUC), highlighting its critical role in biosignature detection. Several other individual data features exhibited significance within their respective data types, offering additional granularity. This research fortifies the relevance of machine learning to astrobiology, potentially enhancing life detection missions by allowing algorithmic prioritization of high-interest samples for further investigation. Future work will refine data standardization, expand the dataset to include more terrestrial systems, and incorporate convolutional neural networks for spectral feature extraction. The potential for public data sharing is also under exploration, reinforcing our commitment to collective scientific advancement.

Statistical↗
NASA NTRSDate not supplied

Nearest-Neighbor Machine Learning Feature Selection for Interpretation of Microbial Molecular Signatures from Isotope Ratio Mass Spectrometry Data

Mass spectrometry (MS) promises to be a powerful tool for potential biosignature detection during astrobiological missions on ocean worlds in our solar system. Accurate and generalizable machine learning methods could enhance science return on investment by predicting seawater chemistry and classifying isotopic biosignatures, either as a signature consistent with microbial life (biotic) or as a novelty (unclassified/unique). However, machine learning models are likely to be complex and involve interactions between MS features, making biosignatures difficult to interpret. Feature selection methods provide biological and chemical context that help interpret the mechanisms of machine learning models, but these methods also need the ability to detect complex interactions. Previously, we developed a machine learning feature selection algorithm called nearest-neighbor projected distance regression (NPDR) that has the ability to identify important model features that involve complex interactions and automatically reduce correlation and the dimensionality in a high-dimensional variable space. The standard distance metrics used in NPDR – Manhattan and Euclidean – assume the multivariate data are isotropic, which is often violated in real data due to differences in the covariance between variables. Thus, we extend NPDR to include a random forest distance, and other anisotropic distance metrics, for computing nearest neighbors. We also augment the isotope-ratio MS data with time-series features from the raw MS signal to improve biotic classification. We test NPDR on our novel experimental ocean world seawater analog MS data. We measure isotope fractionations of volatile CO 2 that could be measured in exospheres or plumes. Samples include baseline abiotic conditions using a range of possible seawater chemistry consistent with Europa and Enceladus, and biotic samples that include microbes in these seawaters. We use penalized NPDR with random forest proximity to identify interpretable microbial molecular signatures. We compare features with random forest importance, and we train a classifier that discriminates between biotic and abiotic samples with high accuracy. These ML-trained ocean-world analog MS data could be used to assist in identifying biosignatures during future missions.

geochemistry↗
NASA NTRS2000

The Geologic History of Mars: An Astrobiology Perspective

Fourteen SNC meteorites contain information which must be incorporated with recent spaceflight data for developing Mars' geologic history. SNCs have crystallization ages of 4500 to 160 m.y. Tle oldest meteorite ALH84001 contains information on the Noachian period of Mars' history. There are no meteorites from the Hesperian period and the remaining 13 meteorites fall into two age groups within the Amazonian: The nakhlites around 1300 m.y. and the shergottites between 800-160 m.y. Oxygen isotopic analysis of Martian samples shows two distinct O2 reservoirs throughout Martian history indicating late additions of volatiles and a lack of plate tectonics prior to 3.9 Gy. Evidence for percolation of aqueous brines through impact-produced fractures in the rocky surface is contained in the 3.9 Gy-old ALH84001 carbonate deposits. These carbonates precipitated at approx. 100 C. At this time life had already evolved on Earth. Early Mars could have hosted life similar to the bacteria that inhabited early Earth. Potential microorganisms could have been transported into fractures by carbonate-bearing waters and their remains could have become incorporated into the precipitated carbonate. Since Mars had a weak magnetic field at this time, it can be hypothesized that some of the Martian microorganisms may have been similar to terrestrial magnetotactic bacteria. Over geologic time episodic cratering, and tectonic events have occurred on Mars along with the periodic release of subsurface waters which may have produced clays within SNC meteorites. The geochemical data contained within SNC meteorites complements previous observational data and the recent Mars Global Surveyor data to provide a geological and environmental history which spans almost the entire lifespan on Mars. One of the outstanding features of this model is the possible creation of an early (about 4 Gy) volatile reservoir distinct from the outgassed Mars volatiles, and the persistence of this reservoir throughout most, if not all of subsequent Mars' history. Within the framework of this history a potential scenario for a possible record of living organisms is provided by suggestive structures and organic signatures trapped within secondary mineral deposits and alteration features of some SNC meteorites. Tracing the differences in oxygen isotopic compositions within Martian components allows us to gain insight in the history of Mars.

Gibson, Everett K.↗
NASA NTRS2001

Determining Biosignatures by Complexity Analysis in Antarctic Cryptoendolithic Communities

One of the most difficult problems of life detection is that of identifying biosignatures across a wide range of scales using multiple co-registered probes. The technique should be of equal utility across a wide range of search spaces from remote sensors probing volumes of space or planetary surfaces, visual eye or camera searches across the surface of a rock in Antarctica, low resolution microscopic scanning of a rock or a space craft in situ, or high resolution electron microscope and computerized tomography scanning of geobiological samples. We describe here an approach to this problem which derives in large part from past work done in the area of astrophysics - namely the analysis of complexity in galactic signals by data compression methods. This approach is a radically new one for geobiology and astrobiology, and allows us to assess the complexity (and thus potential biogenicity) of an object being examined. This is done by considering the information within pixels of an image (regardless the sensor used to gather the information) as an energetic system capable of description in terms of classical thermodynamics. The image data space is searched by an algorithm that judges complexity via data compression (e.g., the more compressible it is, the less complex, and vice versa) and maximum entropy as originally outlined by Shannon. At present we are implementing methods to utilize images from multiple sensors gathering different kinds of information (e.g., visible gray-scale data, color analyses, UV fluorescence, chemical information, etc). We present here preliminary data from deep UV fluorescence and ESEM (Environmental Scanning Electron Microscope) images from a layered cryptoendolithic community of an Antarctic rock.

Storrie-Lombardi, M. C.↗
NASA NTRS1999

Composition and Origin of Martian Surface Material, Remote Detection of Minerals, and Applications to Astrobiology

Martian surface composition and processes are under study through analysis of spectral, magnetic and chemical data from Mars and analysis of laboratory analog materials. The focus of this study is on potential lander/rover measurements of weathered volcanic tephra and hydrothermal rocks because these samples resulted from processes that may have occurred on Mars. Fine-grained particles from these sources may be responsible for origination of the dust/soil on Mars that is shaping the planet's surface character. Alteration on the surface of Mars likely includes both chemical and physical interactions of soil particles and rock surfaces. Many of the minerals present in hydrothermal samples may be associated with organisms and may be useful as indicators of life or environments supportive of life on Mars. Characterization of the spectroscopic properties in the visible/near-infrared (VIS/NIR) and mid-infrared (IR) regions using reflectance, emittance and Raman, as well as the thermal properties of minerals thought to be present on Mars are being performed in order to identify them remotely. Particular interest is directed toward locating minerals, and hence landing sites, important to Astrobiology.

Bishop, J. L.↗
NASA NTRS2003

Development and Operations of the Astrophysics Data System

SAO TASKS ACCOMPLISHED: Abstract Service: (1) Continued regular updates of abstracts in the databases, both at SAO and at all mirror sites; (2) Established a new naming convention of QB books in preparation for adding physics books from Hollis or Library of Congress; (3) Modified handling of object tag so as not to interfere with XHTML definition; (4) Worked on moving 'what's new' announcements to a majordomo email list so as not to interfere with divisional mail handling; (5) Implemented and tested new first author feature following suggestions from users at the AAS meeting; (6) Added SSRv entries back to volume 1 in preparation for scanning of the journal; (7) Assisted in the re-configuration of the ADS mirror site at the CDS and sent a new set of tapes containing article data to allow re-creation of the ADS article data lost during the move; (8) Created scripts to automatically download Astrobiology.

Murray, Stephen S.↗
NASA NTRS2018

Infrared Spectra and Interstellar Sulfur: New Laboratory Results for H2S and Four Malodorous Thiol Ices

New infrared spectra are presented for H2S and four other sulfur-containing compounds, all thiols, at 10–140 K to aid in the study of interstellar and solar system ices. Infrared spectral changes on warming H2S and each thiol are described with an emphasis on the S–H stretching vibration at 2550–2525 cm(exp -1) (λ=3.92–3.96 μm) as it is in a relatively unobscured part of the infrared spectra of interstellar and planetary ices. Infrared positions and band strengths for each thiol’s S–H and C–H stretching vibrations are reported, along with the S–H band strength of H2S. Two band strengths of near-infrared features of CH3SH are included. Results for these compounds are compared, and some areas of agreement and disagreement with the literature are described.

Astrobiology↗
NASA NTRS2013

Texturecam: A Smart Camera for Microscale, Mesoscale, and Deep Space Applications

The TextureCam project is developing a 'smart camera' that can classify geologic surfaces in planetary images. This would allow autonomous spacecraft to collect data opportunisitcally during intervals between communications with Earth, such as during long traverses. Its surface classifications can identify new targets that were not anticipated in advance. The spacecraft might use this information to target these features with high-resolution instruments such as spectrometers nd narrow-field cameras. Classifications could also inform data 'triage' decisions, identifying high value images for prioritized downlink. Finally, the surface classification can serve as compressed maps of image content. Each of these strategies can improve the science data returned at each command cycle and speed reconnaissance during site survey and astrobiology investigation. Our first year of development has completed the image analysis algorithms and validated them in software tests. Here we survey these initial results and explore several application areas relevant to Mars and beyond.

Hyperspectral imagery↗
NASA NTRSDate not supplied

Squeezing Every Last 'Bit' of Information from Enceladus Mass Spectrometry

Potential opportunities to return to Enceladus in Discovery and Flagship class missions inspire development of next-generation instruments and creative approaches to sample collection, sample analysis, and data analysis and transmission strategies. Mass spectrometers (MS) are ideally suited to future Enceladus missions due to their analytical power in identifying a range of molecular and ionic compositions – including complex organics – and potentially astrobiologically-important features such as isotope ratios, chirality, and enantiomeric excess. However, long communication delays from Enceladus and limited bandwidth limits the data transmission from these higher-data-volume instruments, likely delaying mission-related response to new data. We explore the utility of data science and machine learning (ML) on isotope ratio (IR)MS data collected from laboratory analogs of Enceladus to: 1) process data quickly for rapid ground-based analyses, 2) understand if compositional and biosignature information could be extracted from IRMS data, and 3) evaluate whether onboard ML techniques could improve sample analysis, cadence, and transmission prioritization. Laboratory analogs analyzed isotopes of volatile CO2 that interacted with seawaters of varying composition, and include both abiotic and biotic (microbially-influenced) experiments. Enceladus’s alkaline oceans promote speciation of carbon into multiple forms (e.g., H2CO3 / CO2, HCO3-, and CO32-), each of which could be isotopically fractionated by abiotic or biotic reactions. Large (>2‰) changes in carbon isotopes (δ13C) are observed from some biotic experiments inoculated with complex microbial ecosystems relative to the abiotic seawaters. ML training and classification suggests that microbial samples can be distinguished from abiotic samples, yet that a broad range of microbial experiments are necessary to train ML models to cover a range of complexities including disequilibria, and isotopic and compositional fractionation.

geochemistry↗
NASA NTRSDate not supplied

Algorithmic Classification of Raman Spectra Biosignatures: Improving Life Detection Confidence

“Agnostic” biosignatures – indicators of life (or the absence of life), independent of a particular biochemistry – are increasingly considered a high standard for life detection. The Ladder of Life Detection (2018) called for investigating how combinations of independent and different potential biosignatures affect confidence. To address this gap, statistical classification of elemental abundances, isotopic fractionation, and reflectance spectroscopy (VNIR) has been implemented. Raman spectroscopy, highly desirable due to its wide availability, has the potential to improve this predictive power. This work implemented biosignature classification algorithms on Raman data alone, in preparation for combination with the other data types. Raman spectroscopy data was collected from published databases and papers as part of a manually curated dataset of “indicative” and “non-indicative of life” samples. These currently include 61 non-indicative samples (meteorites, magnetite); 3 indicative living samples (bacteria); 20 indicative non-living samples (chalk, bone); and 12 indicative mixed (with non-indicative material) samples (soil, microbial mats). Laboratory work is ongoing to characterize additional samples, particularly a greater breadth of mixed systems. Spectra were interpolated, filtered with the Savitzsky-Golay filter, and de-noised. For a preliminary examination, agnostic features were manually extracted including mean intensity, number of peaks, and mean peak width. Different peak prominences and filtering polynomials were used to refine features. Classification algorithms were implemented: k-nearest neighbors (KNN), logistic regression (LR), linear support vector machines (SVM), random forest (RF), Gaussian naïve bayes (GNB). Lastly, Monte Carlo simulations on 1,000 50%-train-test-splits were used to validate classification performance and feature significance. The preliminary feature set achieved its highest AUC of 0.52 with LR, with no strongly discriminatory features. Work to improve feature extraction, such as through deep learning with back propagation, is planned. In future work, the Raman data will be combined with the other data types, and potentially new data types such as enantiomeric excess. This project was partially supported through the NASA Ames Project EXcellence (APEX) incubator program.

Astrobiology↗