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Sunanda Sharma

Publications and source records attributed to Sunanda Sharma.

14 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Diverse Organic-Mineral Associations in Jezero Crater, Mars

The presence and distribution of preserved organic matter on the surface of Mars can provide key information about the Martian carbon cycle and the potential of the planet to host life throughout its history. Several types of organic molecules have been previously detected in Martian meteorites1 and at Gale crater, Mars. Evaluating the diversity and detectability of organic matter elsewhere on Mars is important for understanding the extent and diversity of Martian surface processes and the potential availability of carbon sources1,5,6. Here we report the detection of Raman and fluorescence spectra consistent with several species of aromatic organic molecules in the Máaz and Séítah formations within the Crater Floor sequences of Jezero crater, Mars. We report specific fluorescence-mineral associations consistent with many classes of organic molecules occurring in different spatial patterns within these compositionally distinct formations, potentially indicating different fates of carbon across environments. Our findings suggest there may be a diversity of aromatic molecules prevalent on the Martian surface, and these materials persist despite exposure to surface conditions. These potential organic molecules are largely found within minerals linked to aqueous processes, indicating that these processes may have had a key role in organic synthesis, transport or preservation.

Sunanda Sharma↗
NASA NTRS2023

Overview and Results From the Mars 2020 Perseverance Rover's First Science Campaign on the Jezero Crater Floor

The Mars 2020 Perseverance rover landed in Jezero crater on 18 February 2021. After a 100-sol period of commissioning and the Ingenuity Helicopter technology demonstration, Perseverance began its first science campaign to explore the enigmatic Jezero crater floor, whose igneous or sedimentary origins have been much debated in the scientific community. This paper describes the campaign plan developed to explore the crater floor's Máaz and Séítah formations and summarizes the results of the campaign between sols 100–379. By the end of the campaign, Perseverance had traversed more than 5 km, created seven abrasion patches, and sealed nine samples and a witness tube. Analysis of remote and proximity science observations show that the Máaz and Séítah formations are igneous in origin and composed of five and two geologic members, respectively. The Séítah formation represents the olivine-rich cumulate formed from differentiation of a slowly cooling melt or magma body, and the Máaz formation likely represents a separate series of lava flows emplaced after Séítah. The Máaz and Séítah rocks also preserve evidence of multiple episodes of aqueous alteration in secondary minerals like carbonate, Fe/Mg phyllosilicates, sulfates, and perchlorate, and surficial coatings. Post-emplacement processes tilted the rocks near the Máaz-Séítah contact and substantial erosion modified the crater floor rocks to their present-day expressions. Results from this crater floor campaign, including those obtained upon return of the collected samples, will help to build the geologic history of events that occurred in Jezero crater and provide time constraints on the formation of the Jezero delta.

Mars 2020↗
NASA NTRSDate not supplied

Classifying Agnostic Biosignatures using Raman, VNIR, and Elemental Data

How can we use our current wealth of terrestrial data, encompassing biogenic and abiogenic systems, to determine the distinguishing properties of life? SCOBI (Statistical Classification of Biosignature Information) uses machine learning techniques to algorithmically identify combinations of measurements that are “indicative of life”. A set of ~1000 observations, comprising elemental abundance, isotopic fractionation, VNIR reflectance, and (in progress) Raman spectra, have been assembled from existing literature and databases. The observations cover systems classified as “indicative alive” (e.g., cells, vegetation), “indicative non-alive” (e.g., fossils, teeth), “mixed indicative” (e.g., soil, pond water), or “non-indicative” (e.g., rocks, meteorites). VNIR data was preprocessed by linear interpolation from 400-2100 nm and smoothed with a Savitzky-Golay filter. To limit the amount of Earth-biochemistry-specific (non-agnostic) information included, the first five spectral features extracted were number of peaks, number of troughs, mean reflectance, mean peak width, and broadest peak width. To help further emphasize agnostic biosignatures, Earth-specific features such as chlorophylls have been manually flagged so that feature importance with and without them can be compared. Classifiers including k-nearest neighbors (KNN), Gaussian Naïve Bayes (GNB), logistic regression (LR), random forest (RF), and support vector machine (SVM) were implemented, as was a combination voting classifier. Performance metrics included false positive rates, false negative rates, and AUC with 50-50 test/train splits (Monte Carlo simulations). Key takeaways from this stage, prior to the inclusion of Raman spectra, are (1) the overall success rate of 0.933 AUC was most heavily influenced by the elemental abundance data; and (2) VNIR reflectance had the lowest classification performance with 0.52 AUC (58% of objects correctly classified). The next steps are to complete integration of Raman spectral data and to improve the approach to pre-processing and feature extraction for both types of spectral data, such as automated baseline removal, whole spectrum matching, and dimensionality reduction.

Biosignatures↗
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↗
NASA NTRSDate not supplied

Collecting Samples from the Máaz Formation of Jezero Crater with the Mars 2020 Perseverance Rover

Collection of samples that could be returned to Earth from the floor of Jezero,a Noachian crater characterized by a delta–lake system with high potential for habitability, is a major goal of the Mars 2020 mission. The Mars 2020 Perseverancerover iscurrently exploringthe Máaz and Séítahformationsto the southeastof the delta. Here wefocus on thecrater-retainingMáaz formation, a widespread, rough and fractured terrain with lobatemarginsmapped in orbital images, e.g.[1]. Outcrop morphology and texture, as well as the appearance, composition and mineralogy of abraded rock surfaces observed by Perseverance suggest that theMáazformation consists of asequence of maficigneous units, likely lavas flows. These rocks have experienced variable interaction with aqueous fluids. Type localities of the lower Roubion and the more resistant Rochette members of the Máazformation have been targeted and their abraded surfaces characterized prior to sample collection. In thispresentation we will summarize these sampling activities and potential future sampling of theheavily crateredupperCh’ałmember that is indicative of the Máazformationfrom orbit.

Justin Ibrahim Simon↗
NASA NTRSDate not supplied

Mapping Organic-Mineral Associations in Jezero crater

The search for potential signs of life on Mars, a primary aim of the Mars 2020 mission, is greatly informed by the detection of organic matter1. The presence of organic matter also provides key information about the habitability and biological potential of the planet throughout its history. The Perseverance rover was designed for in situ science with the ability to collect a suite of promising samples for eventual return to Earth. One of its instruments, Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC), is a deep ultraviolet (DUV) Raman and fluorescence spectrometer designed to map the distribution of organic molecules and minerals on rock surfaces at a resolution of 100 μm2. With its unique spectral mapping capabilities, SHERLOC enables a novel understanding of organic-mineral relationships on Mars to better determine their formation, deposition, and preservation mechanisms. The rover’s landing site within Jezero crater combines a high potential for past habitability as the site of an ancient lake basin with a diverse set of minerals, including carbonates and clays, that may preserve organic materials and potential biosignatures. The Jezero crater floor includes three formations (fm); two of these, Máaz and Séítah, were explored as part of the mission’s first campaign. Here, we report the detection of multiple species of aromatic organic molecules using Raman and fluorescence spectroscopy across ten targets in the two formations. This is the first evidence of organic molecules in Martian materials obtained using Raman spectroscopy, and among the first using fluorescence spectroscopy, beyond Earth3. We report specific spatial patterns and classes of organic molecules in these compositionally distinct formations, potentially indicating different fates of carbon in these environments. Our findings indicate that there is a diversity of aromatic molecules prevalent on the Martian surface and these materials persist despite exposure to surface conditions. These organic molecules are largely found within minerals linked to aqueous processes, suggesting that these processes may have had a key role in organic synthesis, transport from their point of origin, or preservation.

Sunanda Sharma↗
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

Statistical Classification of Biosignature Information: Combining Elemental, Molecular, Reflectance, and Raman Data to Increase Life Detection Confidence

Planetary exploration missions seeking past or present signs of life carry not just a single instrument, but a suite. There is a need to study how these multiple data types can be combined to create “composite” biosignatures [1]. Algorithmic methods using existing data on living and non-living systems, though limited by the n = 1 of Earth, can nonetheless be informative. We assembled a database of 1277 measurements spanning 16 representative systems either indicative or non-indicative of life. Five classification (machine learning) methods were used on each individual data type, then on the entire set. This abstract summarizes the results; the data is described in more detail in [2], and methods in [3].

Biosignatures↗
NASA NTRSDate not supplied

Curating a Standardized Dataset for Statistical Biosignature Classification

In recent years, machine learning has been explored as a toolkit for planetary science and operations [Helbert, Azari]. Machine learning has been used to improve our understanding of possible biosignatures and mineral signatures to improve science return on future missions [Warren-Rhodes, Cleaves].

Biosignatures↗
NASA NTRSDate not supplied

Microbial Pigments and Their Degradation Products as Biosignatures

Carotenoids are a class of vibrant biological pigments that have a characteristic chemical structure centered around a polyene core (Lu et al. 2018). Carotenoids and their derivatives are candidate biosignatures because they can persist in the terrestrial geologic record for up to 1.73 billion years (Vinnichenko et al. 2020), have specific structures that are likely the result of complex pathways, mediate the survival of many microorganisms in Mars and Ocean Worlds analog environments, and are detectable with multiple techniques, including Raman spectroscopy. In this project, we aim to investigate the detectability of carotenoid pigments with different spectroscopic methods to inform future instrument selection. We compare the spectra of five unaltered carotenoids, two model compounds, and carotenoid-forming archaeon with visible and deep UV Raman spectroscopy and UVVis absorption spectrophotometry. We then use one model pigment, beta-carotene, to evaluate the likelihood that unique spectral properties of carotenoids, or their refractory byproducts, would be preserved and detectable on a remote planetary surface by exposing it to simulated conditions for Mars. Sample Acquisition. Pigments betacarotene, lutein, zeaxanthin, astaxanthin, and lycopene were purchased from Sigma Aldrich. Halobacterium salinarum NRC-1 was acquired from Carlina Biological and grown in Halobacterium media. Mineral salts including sodium sulfate, sodium carbonate, and halite were used to form matrices in which the beta-carotene was embedded before exposure. Pigment-mineral mixes were at a 1:10 ratio in water. Analytical Techniques. Deep UV Raman data were collected on a custom laboratory mapping spectrometer called MOBIUS (Mineral and Organic Based Investigations using Ultraviolet Spectroscopy), which is an analog to the SHERLOC instrument on the Mars 2020 Perseverance rover (Bhartia et al. 2021). It features a 248.56 nm NeCu pulsed laser, liquid nitrogen-cooled detector, and tunable optical setup. Visible Raman data were collected using a Horiba Jobin Yvon LabRam HR spectrometer with a frequencydoubled Nd:YAG laser (532 nm) and a HeNe laser (633 nm). A VWR 6300 PC UV/Visible Spectrophotometer was used to collect absorption data for carotenoid solutions, model compounds, and solvents in UVpermissible capped cuvettes. Data were collected from 190-1100 nm at 1 nm increments. All spectral data were analyzed using Igor Pro 9 (Wavemetrics). Irradiation. We used a vacuum chamber equipped with a cryostat and a flood electron gun to simulate Martian surface temperatures, low pressures, and ionizing radiation (10keV, 10μA for 6h at 200K for our initial tests). The samples were prepared by drying the pigment-mineral mix onto polished metal tabs, then mounted on the cryostat for processing. Samples were then analyzed directly on the tabs after exposure. Results: In comparing the visible and deep UV Raman spectra of unaltered pigments, we found that they differed drastically. Carotenoids are often studied with visible Raman and typically have peaks at 1525 cm-1 and 1157 cm-1, due to the stretching of the C=C and C-C bonds in the polyene structure. However, in deep UV, the strongest feature is at ~1630 cm-1 and is broad, possibly indicating that multiple peaks are forming this feature. This stark difference is likely due to different preresonant enhancement effects. The UV-Vis results show that there is an absorption band in the deep UV <300 nm, which supports the hypothesis that the 248.6 nm excitation is interrogating another aspect of carotenoids than visible Raman. Our preliminary exposure tests indicated that pigments – even without minerals present - were largely unaltered in the applied conditions, with only a slight broadening in the primary polyene peaks apparent in the visible Raman data. Figure 1. A) Visible vs. deep UV Raman spectra of unaltered beta carotene. B) Schematic of exposure. Conclusions: Our results to date indicate that deep UV and visible Raman spectroscopy, both techniques with planetary mission heritage from Mars 2020 (Wiens et al. 2021, Bhartia et al. 2021), may be used in a complementary manner to observe carotenoids. In addition, we find that beta-carotene is largely resistant to our current exposure conditions, though there may be some amount of amorphization of the material which could cause the broadening of the peaks at 1525 and 1157 cm-1. As a next step, we aim to increase the dosage and duration of exposure to observe degradation of the parent pigment, possibly add UV as a factor via an Ar mini-arc UV lamp and use GC-MS to characterize possible degradation products.

pigments↗