When you purchase through links in our articles, we may earn a small commission. NASA's rover Perseverance finds best evidence of life on Mars so far A rock sample taken from the red planet has distinct markings and clues of a potential biosignature. NASA's famous Mars rover Perseverance, which landed on the red planet back in 2021, has been quiet for a long time--but now the indestructible robot is making new headlines. According to NASA, the rover may have found a potential biosignature, which could be evidence of life. Perseverance made the decisive discovery back in July 2024, when the Mars rover drove through a dried-up 400-meter-wide riverbed called Neretva Vallis in the Jezero Crater (which is 28 miles across).

Molecular assembly offers a promising path to detect life beyond Earth, while minimizing assumptions based on terrestrial life. As mass spectrometers will be central to upcoming Solar System missions, predicting molecular assembly from their data without needing to elucidate unknown structures will be essential for unbiased life detection. An ideal agnostic biosignature must be interpretable and experimentally measurable. Here, we show that molecular assembly, a recently developed approach to measure objects that have been produced by evolution, satisfies both criteria. First, it is interpretable for life detection, as it reflects the assembly of molecules with their bonds as building blocks, in contrast to approaches that discount construction history. Second, it can be determined without structural elucidation, as it can be physically measured by mass spectrometry, a property that distinguishes it from other approaches that use structure-based information measures for molecular complexity. Whilst molecular assembly is directly measurable using mass spectrometry data, there are limits imposed by mission constraints. To address this, we developed a machine learning model that predicts molecular assembly with high accuracy, reducing error by three-fold compared to baseline models. Simulated data shows that even small instrumental inconsistencies can double model error, emphasizing the need for standardization. These results suggest that standardized mass spectrometry databases could enable accurate molecular assembly prediction, without structural elucidation, providing a proof-of-concept for future astrobiology missions.

The search for evidence of past life on Mars presents a tremendous challenge that requires the usage of very advanced robotic technologies to overcome it. Current digital microscopic imagers and spectrometers used for astrobiological examination suffer from limitations such as insufficient resolution, narrow detection range, and lack of portability. To overcome these challenges, this research study presents modifications to the Phoenix rover to expand its capability for detecting biosignatures on Mars. This paper examines the modifications implemented on the Phoenix rover to enhance its capability to detect a broader spectrum of biosignatures. One of the notable improvements comprises the integration of advanced digital microscopic imagers and spectrometers, enabling high-resolution examination of soil samples. Additionally, the mechanical components of the device have been reinforced to enhance maneuverability and optimize subsurface sampling capabilities. Empirical investigations have demonstrated that Phoenix has the capability to navigate diverse geological environments and procure samples for the purpose of biomolecular analysis. The biomolecular instrumentation and hybrid analytical methods showcased in this study demonstrate considerable potential for future astrobiology missions on Mars. The potential for enhancing the system lies in the possibility of broadening the range of detectable biomarkers and biosignatures.

Abstract-- As natural access points to the subsurface, lava tubes and other caves have become premier targets of planetary missions for astrobiological analyses. Few existing robotic paradigms, however, are able to explore such challenging environments. ReachBot is a robot that enables navigation in planetary caves by using extendable and retractable limbs to locomote. This paper outlines the potential science return and mission operations for a notional mission that deploys ReachBot to a martian lava tube. In this work, the motivating science goals and science traceability matrix are provided to guide payload selection.

The quest to find extraterrestrial life is a critical scientific endeavor with civilization-level implications. Icy moons in our solar system are promising targets for exploration because their liquid oceans make them potential habitats for microscopic life. However, the lack of a precise definition of life poses a fundamental challenge to formulating detection strategies. To increase the chances of unambiguous detection, a suite of complementary instruments must sample multiple independent biosignatures (e.g., composition, motility/behavior, and visible structure). Such an instrument suite could generate 10,000x more raw data than is possible to transmit from distant ocean worlds like Enceladus or Europa. To address this bandwidth limitation, Onboard Science Instrument Autonomy (OSIA) is an emerging discipline of flight systems capable of evaluating, summarizing, and prioritizing observational instrument data to maximize science return. We describe two OSIA implementations developed as part of the Ocean Worlds Life Surveyor (OWLS) prototype instrument suite at the Jet Propulsion Laboratory. The first identifies life-like motion in digital holographic microscopy videos, and the second identifies cellular structure and composition via innate and dye-induced fluorescence. Flight-like requirements and computational constraints were used to lower barriers to infusion, similar to those available on the Mars helicopter, "Ingenuity." We evaluated the OSIA's performance using simulated and laboratory data and conducted a live field test at the hypersaline Mono Lake planetary analog site. Our study demonstrates the potential of OSIA for enabling biosignature detection and provides insights and lessons learned for future mission concepts aimed at exploring the outer solar system.

A new study involving University of Oxford researchers has found that artificial intelligence could accelerate the search for extraterrestrial life by showing the most promising places to look. The findings have been published in Nature Astronomy. In the search for life beyond Earth, researchers have few opportunities to collect samples from Mars or elsewhere. This makes it critical that these missions target locations that have the best chance of harbouring life. In this new study, researchers demonstrated that artificial intelligence (AI) and machine learning methods can support this by identifying hidden patterns within geographical data that could indicate the presence of life.

An artificial-intelligence model trialled in Chile's Atacama Desert could one day detect signs of life on other planets. Artificial intelligence (AI) and machine learning could revolutionize the search for life on other planets. But before these tools can tackle distant locales such as Mars, they need to be tested here on Earth. A team of researchers have successfully trained an AI to map biosignatures -- any feature which provides evidence of past or present life -- in a three-square-kilometre area of Chile's Atacama Desert. The AI substantially reduced the area the team needed to search and boosted the likelihood of finding living organisms in one of the driest places on the planet.

Artificial intelligence (AI) and machine learning could revolutionize the search for life on other planets. But before these tools can tackle distant locales such as Mars, they need to be tested here on Earth. A team of researchers have successfully trained an AI to map biosignatures -- any feature which provides evidence of past or present life -- in a three-square-kilometre area of Chile's Atacama Desert. The AI substantially reduced the area the team needed to search and boosted the likelihood of finding living organisms in one of the driest places on the planet. The results were reported on 6 March in Nature Astronomy1.

With over 144,370,000 square miles of surface terrain, Mars has a lot of places where signs of potential life could hide. Factor in the ultra-valuable time of current and future rovers, and it makes it even more challenging to scour for evidence of potential ancient microbes and organisms in an efficient way. To even the playing field a bit, SETI is turning again to artificial intelligence and machine learning in an effort to calculate the most likely and promising places for rovers--and, perhaps one day, astronauts--to look for clues of life. And as first detailed on Monday in Nature Astronomy, the team's new AI machine learning modeling is already showing potential to speed up humanity's search for alien life. To build their AI, the interdisciplinary project led by SETI Institute Senior Research Scientist Kim Warren-Rhodes trained a program on datasets drawn from a region called Salar de Pajonales.

Back in 1950, Enrico Fermi posed the question now known as the Fermi Paradox: Given the countless galaxies, stars, and planets out there, the odds are that life exists elsewhere--so why haven't we found it? The size of the universe is only one possible answer. Maybe humans have already encountered extraterrestrial (ET) life but didn't recognize it. Maybe it doesn't want to be found. Maybe it doesn't find us interesting.