Technologie

Kann KI helfen, Leben auf dem Mars oder Eiswelten zu finden?

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Außerirdische Eiswelten Exoplaneten

Eine aktuelle Studie unter der Leitung von Kim Warren-Rhodes, Senior Research Scientist am SETI-Institut, die in Nature Astronomy veröffentlicht wurde, bringt uns der Entdeckung außerirdischen Lebens näher, indem sie seltene Lebensformen in extremen Umgebungen kartiert. Die interdisziplinäre Forschung konzentriert sich auf das Leben, das in Salzstöcken, Felsen und Kristallen am Salar de Pajonales verborgen ist, der an der Grenze zwischen der chilenischen Atacama-Wüste und dem Altiplano liegt. Diese Studie könnte dazu beitragen, genaue Orte für die Suche nach Leben auf anderen Planeten zu bestimmen, trotz der begrenzten Möglichkeiten, Proben zu sammeln oder auf Fernerkundungsinstrumente zuzugreifen.

Wäre es nicht einfacher, Leben auf anderen Welten zu entdecken, wenn wir die genauen Orte wüssten, an denen wir suchen müssen? Die Möglichkeiten, Proben zu sammeln oder auf Fernerkundungsinstrumente zuzugreifen, sind jedoch begrenzt. Eine aktuelle Studie, veröffentlicht in Naturastronomie und unter der Leitung von Kim Warren-Rhodes, Senior Research Scientist am SETI Institute, bringt uns der Suche nach außerirdischem Leben einen Schritt näher. Die interdisziplinäre Studie kartiert die seltenen Lebensformen, die in Salzstöcken, Felsen und Kristallen am Salar de Pajonales verborgen sind, der sich an der Grenze zwischen der chilenischen Atacama-Wüste und dem Altiplano befindet.

Warren-Rhodes tat sich mit Michael Phillips vom Johns Hopkins Applied Physics Lab und Freddie Kalaitzis vom Johns Hopkins zusammen[{” attribute=””>University of Oxford to train a machine-learning model that could recognize patterns and rules associated with the distribution of life forms. This model was designed to predict and identify similar distributions in untrained data. By combining statistical ecology with AI/ML, the scientists achieved a remarkable outcome: the ability to locate and detect biosignatures up to 87.5% of the time, compared to just 10% with a random search. This also reduced the search area by as much as 97%.

Biosignature Probability Maps

Biosignature probability maps from CNN models and statistical ecology data. The colors in a) indicate the probability of biosignature detection. In b) a visible image of a gypsum dome geologic feature (left) with biosignature probability maps for various microhabitats (e.g., sand versus alabaster) within it. Credit: M. Phillips, F. Kalaitzis, K. Warren- Rhodes.

“Our framework allows us to combine the power of statistical ecology with machine learning to discover and predict the patterns and rules by which nature survives and distributes itself in the harshest landscapes on Earth,” said Rhodes. “We hope other astrobiology teams adapt our approach to mapping other habitable environments and biosignatures. With these models, we can design tailor-made roadmaps and algorithms to guide rovers to places with the highest probability of harboring past or present life—no matter how hidden or rare.”


Video, das die wichtigsten Konzepte zur Integration von Datensätzen aus dem Orbit zum Boden zeigt. Die ersten Frames zoomen von einer globalen Ansicht zu einem orbitalen Bild des Salar de Pajonales. Der Salar wird dann mit einer Interpretation seiner Zusammensetzungsvariabilität überlagert, die aus ASTER-Multispektraldaten abgeleitet wird. Die nächste Folge von Einzelbildern geht in von Drohnen abgeleitete Bilder des Feldstandorts im Salar de Pajonales über. Beachten Sie interessante Merkmale, die in der Szene identifizierbar werden, beginnend mit polygonalen Gratnetzwerken, dann einzelnen Gipskuppeln und polygonal gemustertem Boden und endend mit einzelnen Selenitklingen. Das Video endet mit einer Ich-Perspektive-Ansicht einer Reihe von Gipskuppeln, die in dem Artikel unter Verwendung von Techniken des maschinellen Lernens untersucht wurden. Bildnachweis: M. Phillips

Letztendlich könnten ähnliche Algorithmen und maschinelle Lernmodelle für viele verschiedene Arten von bewohnbaren Umgebungen und Biosignaturen an Bord von Planetenrobotern automatisiert werden, um Missionsplaner effizient zu Gebieten jeder Größenordnung mit der höchsten Wahrscheinlichkeit zu führen, dass Leben enthalten ist.

Rhodes und das SETI-Institut[{” attribute=””>NASA Astrobiology Institute (NAI) team used the Salar de Pajonales, as a Mars analog. Pajonales is a high altitude (3,541 m), high U/V, hyperarid, dry salt lakebed, considered inhospitable to many life forms but still habitable.

During the NAI project’s field campaigns, the team collected over 7,765 images and 1,154 samples and tested instruments to detect photosynthetic microbes living within the salt domes, rocks, and alabaster crystals. These microbes exude pigments that represent one possible biosignature on NASA’s Ladder of Life Detection.

At Pajonales, drone flight imagery connected simulated orbital (HiRISE) data to ground sampling and 3D topographical mapping to extract spatial patterns. The study’s findings confirm (statistically) that microbial life at the Pajonales terrestrial analog site is not distributed randomly but concentrated in patchy biological hotspots strongly linked to water availability at km to cm scales.

Next, the team trained convolutional neural networks (CNNs) to recognize and predict macro-scale geologic features at Pajonales—some of which, like patterned ground or polygonal networks, are also found on Mars—and micro-scale substrates (or ‘micro-habitats’) most likely to contain biosignatures.

Orbit To Ground Study of Biosignatures in the Terrestrial Mars Analog Study Site Salar De Pajonales

Orbit-to-Ground study of biosignatures in the terrestrial Mars analog study site Salar de Pajonales, Chile. (b) drone view of the site with macroscale geologic features (domes, aeolian cover, ridge networks, and patterned ground) in false color. (c) 3-D rendering of dome macrohabitats from drone imagery. (d) Orange and green bands of pigments of the photosynthetic microbial communities living in Ca-sulfate micro-habitats. These biosignatures are a feature of NASA’s Ladder of Life Detection and are detectable by eye and by instruments such as Raman (e) and Visible Short-Wave Infrared spectroscopy. Credit: N. Cabrol, M. Phillips, K. Warren-Rhodes, J. Bishop, and D. Wettergreen.

Like the Perseverance team on Mars, the researchers tested how to effectively integrate a UAV/drone with ground-based rovers, drills, and instruments (e.g., VISIR on ‘MastCam-Z’ and Raman on ‘SuperCam’ on the Mars 2020 Perseverance rover).

The team’s next research objective at Pajonales is to test the CNNs ability to predict the location and distribution of ancient stromatolite fossils and halite microbiomes with the same machine learning programs to learn whether similar rules and models apply to other similar yet slightly different natural systems. From there, entirely new ecosystems, such as hot springs, permafrost soils, and rocks in the Dry Valleys, will be explored and mapped. As more evidence accrues, hypotheses about the convergence of life’s means of surviving in extreme environments will be iteratively tested, and biosignature probability blueprints for Earth’s key analog ecosystems and biomes will be inventoried.

“While the high-rate of biosignature detection is a central result of this study, no less important is that it successfully integrated datasets at vastly different resolutions from orbit to the ground, and finally tied regional orbital data with microbial habitats,” said Nathalie A. Cabrol, the PI of the SETI Institute NAI team. “With it, our team demonstrated a pathway that enables the transition from the scales and resolutions required to characterize habitability to those that can help us find life. In that strategy, drones were essential, but so was the implementation of microbial ecology field investigations that require extended periods (up to weeks) of in situ (and in place) mapping in small areas, a strategy that was critical to characterize local environmental patterns favorable to life niches.”

This study led by the SETI Institute’s NAI team has paved the way for machine learning to assist scientists in the search for biosignatures in the universe. Their paper “Orbit-to-Ground Framework to Decode and Predict Biosignature Patterns in Terrestrial Analogues” is the culmination of five years of the NASA-funded NAI project, and a cooperative astrobiology research effort with over 50 team members from 17 institutions. In addition to Johns Hopkins Applied Physics Lab and the University of Oxford, the Universidad Católica del Norte, Antofagasta, Chile supported this research.

Reference: “Orbit-to-ground framework to decode and predict biosignature patterns in terrestrial analogues” by Kimberley Warren-Rhodes, Nathalie A. Cabrol, Michael Phillips, Cinthya Tebes-Cayo, Freddie Kalaitzis, Diego Ayma, Cecilia Demergasso, Guillermo Chong-Diaz, Kevin Lee, Nancy Hinman, Kevin L. Rhodes, Linda Ng Boyle, Janice L. Bishop, Michael H. Hofmann, Neil Hutchinson, Camila Javiera, Jeffrey Moersch, Claire Mondro, Nora Nofke, Victor Parro, Connie Rodriguez, Pablo Sobron, Philippe Sarazzin, David Wettergreen, Kris Zacny and the SETI Institute NAI Team, 6 March 2023, Nature Astronomy.
DOI: 10.1038/s41550-022-01882-x

The SETI NAI team project entitled “Changing Planetary Environments and the Fingerprints of Life” was funded by the NASA Astrobiology Program.



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