AI and Astrobiology: Machine Learning in the Search for Life Beyond Earth

In 2023, astronomers using the James Webb Space Telescope (JWST) reported methane and carbon dioxide in the atmosphere of K2-18 b, a sub-Neptune about 124 light-years away, along with a hint of dimethyl sulfide (DMS), a gas that marine microbes release on Earth. Three years of reanalysis have not confirmed the hint. The episode captures where astrobiology stands: sensitivity is no longer the main obstacle, interpretation is. This article explains how AI and machine learning are used in biosignature detection, what they have actually delivered, and where they cannot yet help.

Key takeaways

• A biosignature is a feature that biology can explain after every plausible non-biological cause has been eliminated. That second clause is where most claims fail.
• Machine learning already does real work in the field: it picks laser targets on Mars, speeds up the analysis of exoplanet spectra, and in laboratory tests has separated biological from abiotic samples with about 90 percent accuracy.
• No AI system has detected life, and none of the techniques below can by itself turn an ambiguous signal into a confirmed one.
• The main risks are training data drawn from a single biosphere, synthetic spectra that differ from real ones, and models whose reasoning is hard to inspect.

What a biosignature is, and why none is clean

Life takes in energy, transforms chemicals, and leaves waste. Those activities can leave traces that instruments can read from a distance or in a sample: gases in an atmosphere, patterns in isotopes, organic molecules in rock, or microscopic structures. Astrobiologists group these into broad classes, including isotope patterns, organic matter, minerals, microscopic and macroscopic structures, changes over time, surface reflectance, and atmospheric gases.

The trouble is that each one has a non-biological impostor. Oxygen can build up without life when starlight splits water vapor, or when a planet loses much of its water. Methane is a possible biosignature, especially when it sits in chemical disequilibrium with other gases, but on Mars the methane detections are disputed, and 2019 measurements set an upper limit far below earlier claims. In 2020, a team reported phosphine in the clouds of Venus; later analyses suggested the detection was false or the concentration far lower, and the question was still being reworked in 2024.

Then there is the case that haunts the field. In the 1990s, microscopic structures and magnetite crystals in the Martian meteorite ALH84001 were proposed as fossilized bacteria. Later studies found the structures were probably too small to be fossil cells, and the lesson drawn was that shape alone is not enough evidence.

Why probabilities matter more than detections

A 2022 paper by Searra Foote, Pritvik Sinha, Cole Mathis, and Sara Imari Walker put the problem in Bayesian terms. They argued that high confidence in a life-detection claim requires either a strong prior reason to expect life in that environment, or a signature that cannot produce false positives. Many current candidates, they noted, are not definitive, which is why they urged researchers to tie life-detection work to origin-of-life experiments.

An analogy helps. A smoke detector that goes off tells you something is burning, but not whether it is a house fire or burnt toast. The detector is working perfectly. The inference is the hard part. Astrobiology's version of toast is geochemistry, and the job of analysis is to separate the two.

Where AI actually enters

The word "AI" covers very different tools, and the claims for each should be weighed separately. The table sorts the main uses by what has been shown.

Task	Example	What has been demonstrated	Main limit
Autonomous target selection on Mars	AEGIS for ChemCam and SuperCam	On Curiosity, picked the desired target material more than 93% of the time vs about 24% without intelligent targeting	Chooses what to look at, not what it means
Adaptive sampling	PIXL on Perseverance	Onboard analysis extends measurements at points of interest	Rules are thresholds set by scientists
Speeding up spectrum analysis	Neural networks and random forests for atmospheric retrieval	Speed-ups of about 2x to 10x or more in some tests	Still checked against slower statistical methods
Agnostic chemistry classifiers	Pyrolysis-GC-MS plus machine learning	About 90% accuracy separating biotic from abiotic samples	Trained on a limited set of Earth samples
Triage of planets and spectra	Models for the Habitable Worlds Observatory	Predict biosignature fluxes from simulated spectra	Trained on synthetic data
Onboard autonomy for ocean worlds	Ocean Worlds Life Surveyor prototype	Detect life-like motion and cell structure in test data	Prototype, tested in simulations and a field test

None of these rows is a life detector. Each is a way of getting through data faster, choosing better targets, or screening out obvious false leads.

Mars: AI that picks targets, not verdicts

NASA's Perseverance rover landed in Jezero Crater on February 18, 2021. The crater was chosen partly because it held an ancient lake, and its river delta may preserve evidence of past microbial life. The rover carries instruments aimed at exactly that question. SHERLOC, an ultraviolet Raman spectrometer, maps fine-scale mineralogy and detects organic compounds. PIXL, an X-ray fluorescence spectrometer, determines the fine-scale elemental composition of surface materials. SuperCam, an upgraded descendant of Curiosity's ChemCam with two lasers and four spectrometers, analyzes rocks from a distance.

Where the autonomy sits

SuperCam works together with a targeting system called AEGIS (Autonomous Exploration for Gathering Increased Science). A 2017 paper in Science Robotics by Raymond Francis and colleagues described how it had been used on Curiosity since May 2016. The distance between Earth and Mars means a command takes a long time to arrive, which slows any instrument that needs precise aiming. AEGIS finds geological targets in navigation camera images, picks those that best match criteria set by scientists, and measures them right away. It also corrects pointing errors of a few milliradians so that very small features can be hit on the first try.

The results were measurable. Over the last 2.5 km of driving into unexplored terrain, AEGIS selected the desired material more than 93 percent of the time, compared with about 24 percent expected without intelligent targeting. Every observation produced a successful geochemical measurement.

An important correction belongs here. AEGIS chooses where SuperCam's laser fires. It does not select which rock cores go into the sample tubes, and nothing published suggests it does. Those decisions are made by scientists and engineers on Earth.

Adaptive sampling with PIXL

A second case is PIXL. A 2024 paper by Peter Lawson and colleagues describes how the instrument uses onboard analysis to run longer measurements at points of interest. The sampling rules come from ternary composition diagrams and machine-learning thresholds. The authors describe this as the first autonomous decision-making by a spacecraft on another planet based on real-time compositional analysis. That is their claim, and the technical point is modest and useful: it spends scarce instrument time where the chemistry looks interesting.

The rock that did not settle anything

The test case is a rock called Cheyava Falls. In July 2024, Perseverance found "leopard spots" in it, features that may indicate past microbial life but that need further research. SHERLOC detected carbon-rich macromolecules there, yet it could not determine whether they were biological. A sample from the rock, named Sapphire Canyon, is among the cores NASA describes as containing potential biosignatures that require further study.

As of July 2025, 33 of the rover's 43 sample tubes were filled, including igneous, sedimentary, impactite, serpentinite, and carbonate rocks, two regolith samples, and one atmosphere sample, plus three witness tubes. Whether those tubes will return to Earth is unclear. NASA paused Mars Sample Return on November 13, 2023, after a critical review of cost and complexity, and in April 2024 judged an $11 billion plan infeasible. For an astrobiology question, that matters: the strongest test of a Martian biosignature is to analyze the sample with laboratory instruments too large to fly. For the broader story of rovers and what each has found, see Mars rovers from Sojourner to Perseverance, and for how the autonomy side works, see how AI helps Mars rovers navigate.

Chemistry that does not need a label: agnostic biosignatures

Most biosignature work looks for specific molecules, such as amino acids. The risk is a form of Earth bias: a search that only recognizes terrestrial chemistry may miss, or misjudge, anything else. A newer approach tries to find the statistical signature of life without naming particular compounds.

In a 2023 paper in Proceedings of the National Academy of Sciences, H. James Cleaves II, Robert Hazen, and colleagues analyzed chemically diverse samples using pyrolysis gas chromatography coupled with mass spectrometry. The samples included living cells, geologically processed fossil organic material, carbon-rich meteorites, and laboratory-made organic mixtures. They trained machine-learning models on the data and obtained a model that could tell whether a sample came from life, for both modern and ancient geologically processed material, with about 90 percent accuracy. The method does not require identifying individual compounds. Instead, the relationships among chromatographic and mass peaks carry the information.

A comparison is a wine expert who can tell a region and vintage from the pattern of flavors without reading a label or naming any one chemical. The skill is real, and it still depends on the bottles the expert has tasted.

A related 2025 study by Lindsay Rutter, Leroy Cronin, and colleagues examined molecular assembly, a measure of how complex a molecule's construction history is, as an interpretable biosignature that mass spectrometry can measure without structural elucidation. Their machine-learning model predicted it with roughly three times lower error than baseline methods. They also reported a sobering caveat: small instrumental inconsistencies can double the error, so standardized databases are needed.

Both studies point to the same limit. A model trained on Earth samples and a handful of meteorites tells us how well it separates the samples it has seen. Whether it would handle a truly alien chemistry is a separate question that no one can test until there is alien chemistry to test it on.

Ocean worlds, Europa Clipper, and the data bottleneck

Jupiter's moon Europa and Saturn's moon Enceladus are considered strong candidates in the search for habitable environments because of liquid water beneath their ice. NASA's Europa Clipper launched on October 14, 2024, flew by Mars on March 1, 2025, and has an Earth gravity assist scheduled for December 3, 2026. It is due to enter Jupiter orbit in April 2030 and will make about 49 close flybys of Europa.

Its nine science instruments include a mass spectrometer for the atmosphere and ejecta (MASPEX), a dust analyzer (SUDA), an ultraviolet spectrograph (Europa-UVS), ice-penetrating radar (REASON), thermal and near-infrared mapping instruments, cameras, a magnetometer, and a plasma instrument. A tenth, ICEMAG, was canceled because of cost overruns and replaced by a different magnetometer. Clipper is a habitability mission. It is built to explore Europa's liquid water, chemistry, and energy, and to help choose a landing site for a possible future lander. It is not designed to detect life.

Why autonomy matters for the next step

If a future lander or orbiter does go looking for life, the bottleneck will be data. A 2023 paper by Mark Wronkiewicz and colleagues at NASA's Jet Propulsion Laboratory points out that an instrument suite could produce far more data than can be transmitted to Earth, so some analysis must happen onboard. They built two autonomy tools for a prototype called the Ocean Worlds Life Surveyor. One detects life-like motion in holographic microscopy video, and the other identifies cell structure and composition from fluorescence. The tools were designed with flight-like computing limits, similar to those of the Ingenuity Mars helicopter, and were tested on simulated and lab data and in a field test at Mono Lake.

A 2025 paper from another JPL-led group described an experimental "AI Space Cortex" for ocean-world missions, motivated by long communication delays, limited power, and radiation-limited lifetimes. These are prototypes, and nobody has flown them. But they show what the work looks like: an AI that decides which tiny fraction of the data is worth sending home.

Related background lives in the search for life on Europa and Enceladus and in Enceladus's hydrothermal vents.

Exoplanet atmospheres: the K2-18 b test case

JWST can measure how starlight filters through a planet's atmosphere as the planet crosses its star, a technique called transmission spectroscopy. Molecules absorb specific wavelengths, so the dips in the spectrum reveal what is there. The planet K2-18 b, about 2.6 times Earth's radius and 8.6 times its mass, orbits a red dwarf every 33 days and receives roughly Earth-like energy from its star. It is the prototype of a proposed class called hycean planets, which would have liquid-water oceans under a hydrogen-rich atmosphere, though whether K2-18 b has an ocean at all is uncertain.

Its history since 2023 shows how hard interpretation is. The table lists the main steps.

Date	Team	What was reported
September 2023	Madhusudhan and colleagues	Methane at 5 sigma and carbon dioxide at 3 sigma, each at about 1% by volume; ammonia not detected; "potential signs" of DMS
January 2025	Schmidt and colleagues	Re-analysis with 60 data treatments and over 250 retrievals: methane at about 4 sigma, but no statistically significant or reliable evidence for CO2 or DMS
April 2025	Madhusudhan and colleagues	Mid-infrared data: DMS and/or dimethyl disulfide (DMDS) at 3 sigma, flat spectrum rejected at 3.4 sigma, abundance of at least about 10 parts per million by volume
May 2025	Pica-Ciamarra and colleagues	Search of 661 molecules: only DMS consistently gave a modest preference across datasets, and in the near-infrared the result depended on detector offsets
July 2025	Hu and colleagues	Four new transits robustly detect CH4 and CO2; DMS marginal, below 3 sigma and below about 2 sigma without strong haze
March 2026	Tsai and colleagues	Results "support the interpretation of a sub-Neptune scenario without invoking DMS"

The sigma value expresses how unlikely a result would be by chance if no real feature were there. A 3 sigma result is suggestive, not conclusive, and the standard for claiming a discovery in physics is usually higher. The table also shows that teams disagree even on settled-sounding detections: the 2025 reanalysis found no reliable CO2, while the Hu team reported CO2 robustly in new data.

The DMS problem

A claim often repeated in popular accounts is that DMS is made on Earth only by marine phytoplankton. That is too strong. Most DMS does come from dimethylsulfoniopropionate (DMSP), a compound produced by some marine algae and broken down by phytoplankton and bacteria. But bacteria also make DMS from other sulfur compounds, and truffles, some plants, and cooked vegetables release it. A 2024 study reported evidence for abiotic DMS in cometary matter, and the European Space Agency has detected DMS in the gas and dust of comet 67P/Churyumov-Gerasimenko. Photochemistry can produce DMS and related molecules without life, and the reported abundances on K2-18 b may or may not be too high for that route, depending on the model.

A signal that is hard to explain without life, in other words, is not the same as a signal that only life can make. The distinction is exactly what the Bayesian argument above describes.

What machine learning does for spectra

Interpreting a spectrum means running a retrieval: a statistical search for the atmospheric composition and temperature that best reproduces the data. Traditional retrievals sample huge numbers of model spectra, which is slow. Machine learning has been applied to this since the late 2010s.

• A 2018 team built INARA, a Bayesian deep-learning framework trained on 3 million synthetic rocky-planet spectra, and argued that machine learning could cut retrieval times by orders of magnitude if enough training data were available. They did not report a measured speed-up.
• A 2020 study by Matthew Nixon and Nikku Madhusudhan found a random forest approach could come closer to a full Bayesian retrieval for the planet HD 209458 b at high computational efficiency, but that machine learning remained computationally prohibitive for high-dimensional problems.
• A 2024 method called FlopPITy reported speed-ups from about 2 to 10 times or more, depending on the model and data.
• A 2019 study using unsupervised clustering to set informed starting points made a nested sampling retrieval up to about twice as fast with negligible loss of accuracy.

Other work asks which planets deserve limited telescope time. A 2024 study by David Duque-Castaño and colleagues trained models on about 10 million synthetic spectra resembling TRAPPIST-1 e. The models classified most test cases correctly down to a signal-to-noise ratio of 4, and the authors suggested they could flag likely bioindicator candidates for JWST using four to ten transits. A 2026 paper by Mark Moussa and colleagues proposed machine-learning models that predict biosignature species from reflected-light spectra to help triage targets for the Habitable Worlds Observatory. In 2023, Roy Forestano and colleagues showed anomaly detection could flag spectra with unusual chemistry, including unknown biosignatures, on synthetic data.

Machine learning has a record of finding things in astronomy data. In December 2017, NASA and Google announced the discovery of Kepler-90 i, the eighth planet around its star, using a machine-learning method. For the broader picture, see how AI is changing exoplanet detection, how astronomers detect water on exoplanets, and the exoplanet atmosphere revolution.

Why AI can mislead the search for life

The tools above come with failure modes that deserve their own list.

1. One biosphere. Models learn from life on Earth. If extraterrestrial biology works differently, a classifier may either miss it or label it abiotic.
2. Synthetic training data. Retrieval and classification models are often trained on simulated spectra. Real data contain detector offsets, stellar contamination, and noise that simulations do not capture. The Pica-Ciamarra result that a DMS preference depended on detector offsets is an example of how much a small instrumental choice can matter.
3. Hidden analysis choices. The Schmidt reanalysis ran 60 data treatments and more than 250 retrievals for a single planet and found conclusions shifting. A machine-learning pipeline is one more choice in that stack, and it can be harder to audit.
4. Interpretability. A classifier that says "biotic" is not an argument. The molecular assembly work is valued partly because it is interpretable.
5. Confidence calibration. A model that is accurate on average can still be badly overconfident on the one unusual sample that matters.

The field knows this. A 2025 workshop report from NASA Ames and the SETI Institute on foundation models for astrobiology, which are large models pre-trained on broad scientific data, outlined near-term opportunities in biosignature detection, mission operations, and research support, rather than claiming any detection power.

What a convincing AI-assisted detection would need

A defensible claim of life would have to pass several tests no algorithm can provide alone:

• More than one independent kind of evidence, such as a gas plus a surface feature or a molecule plus an isotope pattern.
• Replication with a second instrument or a second team using different methods.
• A quantified abiotic baseline for the specific environment, not generic geochemistry.
• Where possible, a physical sample analyzed in a laboratory, which is why the stalled Mars Sample Return program matters.

What people get wrong

Myth: AI has found signs of life. It has not. The most notable claim, DMS on K2-18 b, came from conventional statistical analysis of JWST data, and independent teams have disputed it.

Myth: Perseverance's AI chooses its samples. AEGIS selects laser targets for SuperCam. Sample selection is a human decision, informed by data from all the instruments.

Myth: A high accuracy number means a trustworthy life detector. An accuracy of 90 percent on laboratory samples is promising. It says nothing about performance on a Martian rock or an alien atmosphere that the model has never seen.

Myth: Dimethyl sulfide is a smoking gun. DMS has biological sources on Earth, other than just marine plankton, and it has also been detected in comets, where an abiotic origin has been proposed.

Frequently asked questions

Q: What is a biosignature? A: A biosignature is a feature that biological processes can explain after all plausible non-biological causes have been eliminated. Examples include atmospheric gases, isotope patterns, organic molecules in rock, and microscopic structures. Every proposed biosignature has a non-biological impostor, which is why claims need multiple lines of evidence.

Q: Can JWST detect signs of life on other planets? A: JWST can detect molecules such as methane, carbon dioxide, and water in the atmospheres of some exoplanets during transits. Whether any combination counts as evidence of life depends on ruling out non-biological explanations. The disputed DMS signal on K2-18 b, reported in 2023 and 2025 and challenged by independent reanalyses, shows how contested that step is.

Q: How does AI help with Mars exploration? A: A targeting system called AEGIS selects laser targets for the rover's SuperCam instrument. On Curiosity it picked the desired material more than 93 percent of the time versus about 24 percent without it. PIXL on Perseverance uses onboard analysis to extend measurements at points of interest.

Q: Has machine learning been used to tell biological from non-biological samples? A: Yes, in laboratory studies. A 2023 PNAS paper trained models on pyrolysis gas chromatography and mass spectrometry data from living cells, fossil organic material, meteorites, and synthetic mixtures, and reached about 90 percent accuracy without identifying individual compounds. It has not been tested on extraterrestrial samples.

Q: Is Europa Clipper looking for life? A: No. NASA's Europa Clipper, launched on October 14, 2024 and due at Jupiter in April 2030, is designed to investigate Europa's habitability, its liquid water, chemistry, and energy sources. It will also help choose a landing site for a possible later mission, which could carry life-detection instruments.

Q: What makes dimethyl sulfide a potential biosignature, and why is it disputed? A: On Earth, most DMS comes from dimethylsulfoniopropionate (DMSP), a compound made by some marine algae and broken down by phytoplankton and bacteria. It also has other biological sources and has been detected in comets, with a 2024 study arguing for abiotic origins there. On K2-18 b, teams disagree about whether DMS is detected at all.

The takeaway

The most useful thing AI does in astrobiology is also the least dramatic: it decides where to point a laser, which spectrum to run first, and which sliver of data to send home. Those are real gains, and they will matter more as missions fly farther from Earth. The deeper problem has not moved. Any signal can be explained by chemistry that never involved life, and a model trained on one biosphere cannot, by itself, settle whether an unfamiliar pattern is biology. The next credible detection will probably look less like an algorithm's verdict and more like a pile of independent observations, a sample in a laboratory, and a careful argument, with AI helping at each step but not replacing any of them.

Sources

• Cleaves et al., "A robust, agnostic molecular biosignature based on machine learning," PNAS (2023), via Europe PMC
• Francis et al., "AEGIS autonomous targeting for ChemCam on Mars Science Laboratory," Science Robotics (2017)
• Madhusudhan et al., "Carbon-bearing Molecules in a Possible Hycean Atmosphere" (2023)
• Madhusudhan et al., "New Constraints on DMS and DMDS in the Atmosphere of K2-18 b from JWST MIRI" (2025)
• Schmidt et al., "A Comprehensive Reanalysis of K2-18 b's JWST NIRISS+NIRSpec Transmission Spectrum" (2025)
• Hu et al., "A water-rich interior in the temperate sub-Neptune K2-18 b revealed by JWST" (2025)
• Tsai et al., "Three outstanding physical questions for K2-18 b and other temperate sub-Neptunes" (2026)
• Foote et al., "False positives and the challenge of testing the alien hypothesis" (2022)
• Lawson et al., "Adaptive sampling with PIXL on the Mars Perseverance rover" (2024)
• Wronkiewicz et al., "Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor" (2023)
• Rutter et al., "Exploring molecular assembly as a biosignature using mass spectrometry and machine learning" (2025)
• Nixon and Madhusudhan, "Assessment of Supervised Machine Learning for Atmospheric Retrieval of Exoplanets" (2020)
• Ardévol Martínez et al., "FlopPITy" (2024)
• Duque-Castaño et al., "Machine-assisted classification of potential biosignatures in earth-like exoplanets" (2024)
• Moussa et al., "Life, Machine Learning, and the Search for Habitability" (2026)
• Wikipedia, Perseverance (rover)
• Wikipedia, Europa Clipper
• Wikipedia, K2-18b
• Wikipedia, Dimethyl sulfide
• Wikipedia, Biosignature

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Read next:

• The Search for Life on Other Planets: How We're Looking and What We've Found
• Why Red Dwarf Stars Matter for Life Searches
• Astrobiology: The Science of Life Beyond Earth
• How Do We Detect Exoplanet Atmospheres, and What Are Biosignatures?
• The Mars Viking Life Detection Experiments
• Curiosity Rover: Methane Mystery on Mars