Abstract One of the most interesting questions about the climate and hydrology of early Mars is whether oceans existed and, if so, when. Various geologic features have been interpreted as ancient shorelines, but these features do not follow gravitational equipotentials. Prior work has shown that the elevation of the Arabia level, hypothesized to represent a large, early ocean, better conforms to an equipotential when correcting for global topographic change after its formation. Although the shoreline coordinates underlying these studies are debated, exploring the consequences of these topographic corrections allows additional observable consequences to be identified. Here we show that the topographic corrections cause Jezero crater, the landing site of the Perseverance rover, to be submerged under the proposed Arabia ocean. This precludes the ocean’s existence during known fluvio-lacustrine activity at Jezero and suggests the ocean did not exist during the main era of valley network formation in the Noachian/Early Hesperian. We identify a period of ∼10 8 yr years before fluvial activity at Jezero when the ocean could have existed and discuss potential observable consequences.
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Report of the Science Community Workshop on the proposed First Sample Depot for the Mars Sample Return Campaign
The Mars 2020/Mars Sample Return (MSR) Sample Depot Science Community Workshop was held on September 28 and 30, 2022, to assess the Scientifically‐Return Worthy (SRW) value of the full collection of samples acquired by the rover Perseverance at Jezero Crater, and of a proposed subset of samples to be left as a First Depot at a location within Jezero Crater called Three Forks. The primary outcome of the workshop was that the community is in consensus on the following statement: The proposed set of ten sample tubes that includes seven rock samples, one regolith sample, one atmospheric sample, and one witness tube constitutes a SRW collection that: (1) represents the diversity of the explored region around the landing site, (2) covers partially or fully, in a balanced way, all of the International MSR Objectives and Samples Team scientific objectives that are applicable to Jezero Crater, and (3) the analyses of samples in this First Depot on Earth would be of fundamental importance, providing a substantial improvement in our understanding of Mars. At the conclusion of the meeting, there was overall community support for forming the First Depot as described at the workshop and placing it at the Three Forks site. The community also recognized that the diversity of the Rover Cache (the sample collection that remains on the rover after placing the First Depot) will significantly improve with the samples that are planned to be obtained in the future by the Perseverance rover and that the Rover Cache is the primary target for MSR to return to Earth.
more » « less- NSF-PAR ID:
- 10416374
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Meteoritics & Planetary Science
- Volume:
- 58
- Issue:
- 6
- ISSN:
- 1086-9379
- Page Range / eLocation ID:
- p. 885-896
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Background The Spacecraft Assembly Facility (SAF) at the NASA’s Jet Propulsion Laboratory is the primary cleanroom facility used in the construction of some of the planetary protection (PP)-sensitive missions developed by NASA, including the Mars 2020 Perseverance Rover that launched in July 2020. SAF floor samples (
n =98) were collected, over a 6-month period in 2016 prior to the construction of the Mars rover subsystems, to better understand the temporal and spatial distribution of bacterial populations (total, viable, cultivable, and spore) in this unique cleanroom.Results Cleanroom samples were examined for total (living and dead) and viable (living only) microbial populations using molecular approaches and cultured isolates employing the traditional NASA standard spore assay (NSA), which predominantly isolated spores. The 130 NSA isolates were represented by 16 bacterial genera, of which 97% were identified as spore-formers via Sanger sequencing. The most spatially abundant isolate was
Bacillus subtilis , and the most temporally abundant spore-former wasVirgibacillus panthothenticus . The 16S rRNA gene-targeted amplicon sequencing detected 51 additional genera not found in the NSA method. The amplicon sequencing of the samples treated with propidium monoazide (PMA), which would differentiate between viable and dead organisms, revealed a total of 54 genera: 46 viable non-spore forming genera and 8 viable spore forming genera in these samples. The microbial diversity generated by the amplicon sequencing corresponded to ~86% non-spore-formers and ~14% spore-formers. The most common spatially distributed genera wereSphinigobium ,Geobacillus , andBacillus whereas temporally distributed common genera wereAcinetobacter ,Geobacilllus , andBacillus . Single-cell genomics detected 6 genera in the sample analyzed, with the most prominent beingAcinetobacter. Conclusion This study clearly established that detecting spores via NSA does not provide a complete assessment for the cleanliness of spacecraft-associated environments since it failed to detect several PP-relevant genera that were only recovered via molecular methods. This highlights the importance of a methodological paradigm shift to appropriately monitor bioburden in cleanrooms for not only the aeronautical industry but also for pharmaceutical, medical industries, etc., and the need to employ molecular sequencing to complement traditional culture-based assays.
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Abstract NASA's Mars 2020 and ESA's ExoMars will collect Raman measurements in dusty field conditions obscuring underlying rocks. This presents a challenge for remote Raman measurements at distances where mechanical or ablative sample cleaning is not straightforward. Historically, probing broad lithostratigraphic suites has been thwarted by the need for pristine targets and high‐quality spectra. We provide a means of identifying Raman spectra of common rock‐forming silicate, carbonate, and sulfate minerals under low signal‐to‐noise‐ratios, Mars‐like conditions using a convolutional neural network (CNN). The CNN was trained on the Machine Learning Raman Open Data set data set with 500,000+ Raman spectra of hand samples/powder mixtures (5,000+ spectra/mineral class). Diversity in sample microtopography, orientation, and crystallinity simulated varying laser focuses and spectral quality, and no traditional spectral preprocessing such as cosmic ray or baseline removal was employed. The CNN identified low‐intensity Raman scatterers (micas and amphiboles), mixed minerals, and distinguished between mineral endmembers with +99% success. We present among the first known implementations of “big data” machine learning using varied, high‐volume Raman spectral datasets. The pattern recognition abilities of CNNs can facilitate scientist Raman spectral interpretation on Earth and autonomous rover decision‐making on planets like Mars; increasing scientific yield, correcting human classification errors, reducing the need for thorough target dust removal during evaluative measurements, and streamlining the data communications pipeline—saving time and resources. This study examines an end‐to‐end development process for creating a deep learning algorithm sensitive to varieties of Raman spectra and provides guidelines for CNN model development at the interface of Raman spectroscopy, deep learning, and planetary science.
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null (Ed.)Mission-critical exploration of uncertain environments requires reliable and robust mechanisms for achieving information gain. Typical measures of information gain such as Shannon entropy and KL divergence are unable to distinguish between different bimodal probability distributions or introduce bias toward one mode of a bimodal probability distribution. The use of a standard deviation (SD) metric reduces bias while retaining the ability to distinguish between higher and lower risk distributions. Areas of high SD can be safely explored through observation with an autonomous Mars Helicopter allowing safer and faster path plans for ground-based rovers. First, this study presents a single-agent information-theoretic utility-based path planning method for a highly correlated uncertain environment. Then, an information-theoretic two-stage multiagent rapidly exploring random tree framework is presented, which guides Mars helicopter through regions of high SD to reduce uncertainty for the rover. In a Monte Carlo simulation, we compare our information-theoretic framework with a rover-only approach and a naive approach, in which the helicopter scouts ahead of the rover along its planned path. Finally, the model is demonstrated in a case study on the Jezero region of Mars. Results show that the information-theoretic helicopter improves the travel time for the rover on average when compared with the rover alone or with the helicopter scouting ahead along the rover’s initially planned route.more » « less
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PmagPy Online: Jupyter Notebooks, the PmagPy Software Package and the Magnetics Information Consortium (MagIC) Database Lisa Tauxe$^1$, Rupert Minnett$^2$, Nick Jarboe$^1$, Catherine Constable$^1$, Anthony Koppers$^2$, Lori Jonestrask$^1$, Nick Swanson-Hysell$^3$ $^1$Scripps Institution of Oceanography, United States of America; $^2$ Oregon State University; $^3$ University of California, Berkely; ltauxe@ucsd.edu The Magnetics Information Consortium (MagIC), hosted at http://earthref.org/MagIC is a database that serves as a Findable, Accessible, Interoperable, Reusable (FAIR) archive for paleomagnetic and rock magnetic data. It has a flexible, comprehensive data model that can accomodate most kinds of paleomagnetic data. The PmagPy software package is a cross-platform and open-source set of tools written in Python for the analysis of paleomagnetic data that serves as one interface to MagIC, accommodating various levels of user expertise. It is available through github.com/PmagPy. Because PmagPy requires installation of Python, several non-standard Python modules, and the PmagPy software package, there is a speed bump for many practitioners on beginning to use the software. In order to make the software and MagIC more accessible to the broad spectrum of scientists interested in paleo and rock magnetism, we have prepared a set of Jupyter notebooks, hosted on jupyterhub.earthref.org which serve a set of purposes. 1) There is a complete course in Python for Earth Scientists, 2) a set of notebooks that introduce PmagPy (pulling the software package from the github repository) and illustrate how it can be used to create data products and figures for typical papers, and 3) show how to prepare data from the laboratory to upload into the MagIC database. The latter will satisfy expectations from NSF for data archiving and for example the AGU publication data archiving requirements. Getting started To use the PmagPy notebooks online, go to website at https://jupyterhub.earthref.org/. Create an Earthref account using your ORCID and log on. [This allows you to keep files in a private work space.] Open the PmagPy Online - Setup notebook and execute the two cells. Then click on File = > Open and click on the PmagPy_Online folder. Open the PmagPy_online notebook and work through the examples. There are other notebooks that are useful for the working paleomagnetist. Alternatively, you can install Python and the PmagPy software package on your computer (see https://earthref.org/PmagPy/cookbook for instructions). Follow the instructions for "Full PmagPy install and update" through section 1.4 (Quickstart with PmagPy notebooks). This notebook is in the collection of PmagPy notebooks. Overview of MagIC The Magnetics Information Consortium (MagIC), hosted at http://earthref.org/MagIC is a database that serves as a Findable, Accessible, Interoperable, Reusable (FAIR) archive for paleomagnetic and rock magnetic data. Its datamodel is fully described here: https://www2.earthref.org/MagIC/data-models/3.0. Each contribution is associated with a publication via the DOI. There are nine data tables: contribution: metadata of the associated publication. locations: metadata for locations, which are groups of sites (e.g., stratigraphic section, region, etc.) sites: metadata and derived data at the site level (units with a common expectation) samples: metadata and derived data at the sample level. specimens: metadata and derived data at the specimen level. criteria: criteria by which data are deemed acceptable ages: ages and metadata for sites/samples/specimens images: associated images and plots. Overview of PmagPy The functionality of PmagPy is demonstrated within notebooks in the PmagPy repository: PmagPy_online.ipynb: serves as an introdution to PmagPy and MagIC (this conference). It highlights the link between PmagPy and the Findable Accessible Interoperable Reusabe (FAIR) database maintained by the Magnetics Information Consortium (MagIC) at https://earthref.org/MagIC. Other notebooks of interest are: PmagPy_calculations.ipynb: demonstrates many of the PmagPy calculation functions such as those that rotate directions, return statistical parameters, and simulate data from specified distributions. PmagPy_plots_analysis.ipynb: demonstrates PmagPy functions that can be used to visual data as well as those that conduct statistical tests that have associated visualizations. PmagPy_MagIC.ipynb: demonstrates how PmagPy can be used to read and write data to and from the MagIC database format including conversion from many individual lab measurement file formats. Please see also our YouTube channel with more presentations from the 2020 MagIC workshop here: https://www.youtube.com/playlist?list=PLirL2unikKCgUkHQ3m8nT29tMCJNBj4kjmore » « less
