The NASA Exoplanet Exploration Program (ExEP) has a curated list of 164 nearest Sun-like stars that are easily visible from Earth. This list represents the first targets for a proposed large space telescope specially designed to find life in the universe, currently known as the Habitable Worlds Observatory (HWO). 

After the HWO is completed, likely sometime in the 2040s, scientists will use the specialized telescope to directly image planets in those star systems. The telescope will search around Sun-like stars for planets in a region known as the habitable zone, where temperatures allow liquid water to exist. Scientists hope to find Earth-like planets with liquid water in the habitable zones of other stars.

While the 164 stars on ExEP’s list have qualities that make them good candidates for study, it remains unknown how many actually have terrestrial planets within their habitable zones. A team of scientists at Arizona State University used simulated data for ExEP’s 164 stars and their hypothetical planetary systems to assess which are most likely to have habitable worlds. Their results would help the HWO prioritize which star systems to observe.

The team started with a subset of 134 stars on ExEP’s list with no known exoplanets, giving researchers the freedom to simulate potential worlds around them. They used a software model called the DYNAmical Multi-planet Injection TEster (DYNAMITE) to predict the possibility that an unseen planet exists around each star by generating 100,000 simulated planetary systems around it. The simulations use mathematical formulas based on observations of real exoplanets to determine the sizes and orbital characteristics of hypothetical gas giants and rocky exoplanets. DYNAMITE then determines whether these planetary systems would be stable and include a terrestrial exoplanet in the star’s habitable zone.

The other 30 stars in the ExEP’s list have known exoplanets, but none of them are rocky planets in habitable zones. To predict whether these stars have unseen terrestrial planets in their habitable zones, the team used the same DYNAMITE process as before with the additional requirement that each star must have an exoplanet matching the one already found.

The space around a Sun-like star can be divided into three regions. The region closest to the star is too hot for liquid water to persist on an exoplanet’s surface, while the region farthest from the star is so cold that any water on an exoplanet would be frozen solid. Between these lies the habitable zone, where temperatures would allow liquid water to exist. However, it’s also possible that a combination of light and gravity could create habitable temperatures on large enough moons orbiting gas giants even beyond a star’s main habitable zone. Illustration by Andrew Bizal.

To increase the chances of finding a terrestrial planet in a habitable zone, the team also investigated potentially habitable moons orbiting gas giants in these systems. For each simulation that included a gas giant, the team inserted between 2 and 23 moons, with masses between 10% of Earth’s Moon and 50% of Earth. Then they simulated the surface temperatures of these moons based on how much starlight they receive and how strongly their host planet pulls on them. They deemed the moons potentially habitable if they were large enough to retain an atmosphere and had a temperature ideal for liquid water, even if they were not technically in their stars’ habitable zones.

For each star, the team calculated a metric based on the probabilities that the star had a terrestrial planet in its habitable zone, that the planet would remain in a stable habitable zone for at least 2 billion years, and that the star had a potentially habitable moon. They called this probability statistic a ranking metric and used it to compare star systems. 

Based on the ranking metric, the team found that the dimmer and cooler stars on their list generally had better chances of hosting a habitable-zone terrestrial planet than the brighter, hotter stars. They also found that stars without known exoplanets ranked more favorably. Around 70% of the stars with known exoplanets had less than a 1% probability of hosting a terrestrial planet in their respective habitable zones. Additionally, fewer than 0.1% of their simulated moons were habitable.

Overall, the researchers concluded that the HWO should prioritize observing cooler, Sun-like stars with no known exoplanets. They claimed that the exoplanets with known characteristics in their sample set were of sizes and in positions that made it unlikely for other exoplanets to be in their systems’ habitable zones. Even with expanded habitability criteria for moons, it would be very unlikely for HWO to find another Earth orbiting a gas-giant exoplanet. They ranked 47 stars in the highest-priority category (A Tier), 51 stars in the middle category (B Tier), and 66 stars in the lowest category (C Tier) to observe with HWO. 

At the end of their study, the team remarked that the proposed HWO represents a potential next step in the search for other Earths, but scientists still need to identify the best targets for this future telescope. The HWO telescope should be able to image rocky exoplanets directly, so thorough research to prepare a list of high-priority targets will help avoid overlooking other Earths.

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Since the 1976 Viking missions, when NASA first successfully placed uncrewed landers on Mars, scientists who study life in the universe, known as astrobiologists, have been gathering evidence to determine whether microorganisms ever lived on the red planet. It has been a challenging determination to make. Until a Mars sample return mission occurs, all necessary analyses must take place on Mars, using instruments designed as much to withstand the stresses of space travel as to collect data, which limits how thoroughly scientists can study Martian rocks. As a result, the search for evidence of microbial life on Mars is inherently incremental. 

Recently, scientists added another piece of exciting, yet inconclusive, evidence to their search. In the summer of 2024, the Mars 2020 Perseverance Rover science team was searching for signs of ancient microbial life in a 3,700-million-year-old lake bed on Mars, known as Jezero Crater. In the process, they detected complex, carbon-containing organic molecules in mudstones within a river channel that once brought water to the now-dry lake bed. 

This is not the first time complex organic molecules have been detected in Martian mudstones. However, there are 2 reasons that astrobiologists are particularly interested in this new discovery. First, these organic molecules are widely dispersed across all 4 river channel mudstones they analyzed. Second, some of the organic molecules they found are associated with minerals that are known to support microbial life on Earth. 

Widely dispersed organic molecules associated with minerals that can support microbial life are exactly what astrobiologists would expect to see if microorganisms once lived in Jezero Crater. However, organic molecules can also form through geological processes, like when gases and minerals react at high temperatures and pressures at deep-sea vents on Earth. Therefore, astrobiologists must carefully assess organic molecules to determine their origin. 

In July of 2026, more than 60 Perseverance science team members took a first step towards determining whether ancient microorganisms could have made the organic molecules found in Jezero Crater. They compared the organic molecule detections in Martian mudstones to similar detections of organic molecules in samples of known origin, including fossilized Earth life, meteorites, and minerals. 

NASA scientists detected the organic molecules in Jezero Crater with a rover instrument called Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC). SHERLOC uses a 248.6 nanometer (nm) wavelength laser to excite molecules in unknown materials. When molecules are excited, they absorb and scatter the laser light, causing characteristic shifts in the wavelength of the scattered light. 

Instruments like this, known as Raman spectrometers, detect the shifted light and display the detections as lines called spectra, which scientists use to identify the unknown molecules. The Raman spectra from the 4 mudstones in Jezero crater showed shifts of about 1600 nm, which is characteristic of complex carbon molecules. These shifts appear as bands in the spectra that are often called G-bands.

Raman spectrometers are common laboratory instruments, but most use lasers with wavelengths of 485 nm or higher, so the spectra they produce can be slightly different from SHERLOC spectra even when the laser excites the same kind of molecule. To get around this issue, the scientists compared the Mars spectra with spectra of complex carbon molecules from fossilized Earth microorganisms, coal, graphite, meteorites, and synthetic samples collected by Raman instruments using the same wavelength laser that SHERLOC uses. They found that the spectra collected on Mars overlap with at least one example from every sample type except graphite. 

These results don’t rule out the possibility that organic molecules in Jezero Crater were produced by ancient microbial life, but they can’t tell us exactly how they formed either. That determination will have to wait until the samples are brought to Earth so they can be studied with laboratory instruments.

The Perseverance team has made a small but critical contribution towards determining whether microbial life ever inhabited our closest planetary neighbor, Mars. Their efforts exemplify how the science of astrobiology and the search for possible extraterrestrial life slowly and carefully move forward. If we keep at it, humans may one day have a definitive answer to one of our most consequential questions: Are we alone?

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Carbon is the 4th most abundant element in the universe. It’s everywhere, from the air you breathe to the carbohydrates in your food to the petroleum in the Earth’s crust. Like most elements, carbon was not created by the Big Bang, but came from 2 different types of stars that fuse lighter elements together. 

Massive stars at least 8 times the Sun’s mass fuse 3 helium atoms into a carbon atom in their cores, through a mechanism called the triple-alpha process. They eventually expel this carbon into space, either through a stream of material called a stellar wind or at the end of their lives during a core-collapse supernova. 

Meanwhile, low-to-intermediate-sized stars between ½ and 8 times the Sun’s mass also induce the triple-alpha process. But unlike massive stars, they produce carbon not in their cores, but in a dense shell of helium that surrounds them during an advanced stage known as the asymptotic giant branch or AGB. AGB stars undergo cycles of swelling and contraction, during which they shed some of their material into space.

While scientists understand these processes, the contribution of each star type to the carbon supply in the universe remains uncertain. To address this, a team of scientists modeled how the chemical elements in the Milky Way Galaxy change over time with different contributions from the 2 carbon-creating processes. They then verified their findings by comparing them with the actual carbon concentration in the Milky Way.

To obtain the benchmark for the Milky Way Galaxy’s carbon content, the team turned to the catalog of stars observed by the Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey. They selected 14,066 stars that were large and bright enough to yield reliable data but, unlike AGB and massive stars, were still at a stage of life when their newly produced carbon was still confined to their cores. The researchers used the carbon in the outer layers of these stars, known as subgiants, as a compositional record of the gas from which they formed, thereby estimating the Galaxy’s carbon content during their formation. 

The scientists started with a computer program developed by another team that models how the Milky Way’s chemistry changes over time. First, the team calculated the potential amount of carbon produced by different types of stars using estimates from earlier studies. Then, they incorporated these results into a model that accounts for how stars form, fuse elements, and release carbon into the Galaxy, using a simulation program called the Versatile Integrator for Chemical Evolution, or VICE. The researchers ran 14 versions of this model, each assuming a different contribution of carbon from AGB stars relative to massive stars.

In their computer model, the Milky Way Galaxy began as a set of 200 concentric rings, each containing its own population of stars, different speeds at which stars form, and varying amounts of gas available to form new stars. The team then stitched these rings together by having stars migrate between them. They allowed these model Galaxies to settle over 10 billion years, then picked a random sample of 14,066 simulated stars to compare their carbon content with carbon in the same number of observed subgiants from APOGEE.

The team’s model most closely reproduced the actual carbon content of the Milky Way’s subgiant stars when AGB stars contribute between 10% and 40% of the Galaxy’s carbon, with the strongest agreement occurring at 15% to 30%. Even though they found that massive stars are the dominant source of carbon, they also found that smaller AGB stars could produce carbon more efficiently than previously thought, though they take longer to become carbon sources than their larger counterparts.

The team concluded that their estimates of how much carbon comes from AGB and massive stars provide a benchmark for future studies, as the connection between the production of elements inside stars and the processes by which those same stars lose mass remains poorly understood. They suggested that future researchers analyze larger sample sizes, examine other galaxies, and use data from future surveys to better understand the chemical history of the universe.

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Astronomers and astrobiologists use a wide range of tools in their search for extraterrestrial life. Probes, satellites, and rovers take photographs and collect data on the geology and chemistry of planets and moons in the solar system. Radio telescopes sweep the sky, looking for signals from intelligent civilizations. And telescopes like the JWST can observe the atmospheres of distant exoplanets, potentially finding ones similar to Earth’s. 

A limitation of these methods is that they assume life on other worlds is fundamentally similar to life on Earth, requiring the same balance of molecules in the atmosphere, producing the same chemicals for their metabolic processes, or developing technologies analogous to those humans make. While researchers use these assumptions as baseline markers to look for, assuming such similarities might lead them astray. Scientists looking for life using too narrow a set of parameters might miss life unlike Earthlings. Or they might mistake coincidentally similar geological, chemical, or astronomical processes for having been made by living things.

So, to complement techniques that assume alien life resembles life on Earth, scientists also consider approaches that detect signs of life while making as few assumptions as possible about what it could look like, known as agnostic biosignatures. One of these life signs with few assumptions is complexity. Though it can be difficult to quantify precisely, living things tend to create more complex systems than nonliving things. A team of researchers recently tested whether living and nonliving worlds could be distinguished solely by examining the complexity of the light-reflection patterns from their surfaces.

For this, the team treated Earth and Mars, similar planets but one with life and one without, as stand-ins for exoplanets around other stars. They collected data on the light reflected off both planets’ surfaces from a space station monitoring Earth, known as the Deep Space Climate Observatory or DSCOVR, and an orbiter around Mars observing its atmosphere, known as the Hope Probe. 

The team identified 3 nearly identical wavelengths of light that these machines observe from their respective planets. From the machine databases, they collected measurements of how much light each planet reflected, called its reflectance, at these 3 wavelengths over 486 local days. By using only individual wavelengths of light rather than full photographs, the team claimed that, if their test worked, researchers could apply it to distant exoplanets that cannot be photographed in detail.

Without looking for any particular chemical or signal, the team compared these measured reflectances using a series of statistical tests to determine the variation and randomness in their reflected light, which they considered representative of the complexity of the planets’ surfaces. The researchers found that of the 27 complexity metrics they tested, Earth scored higher than Mars on 25, Mars scored higher than Earth on 1, and the planets scored the same on 1. So, even though both planets have rocky surfaces with atmospheres, weather, and cyclical changes, the one with life is measurably more intricate and dynamic, and the researchers found this difference detectable even with a small stream of light at great distances.

The researchers claimed their test demonstrated that complexity could be a valid agnostic biosignature, with caveats. One is that Earth is quite distinct from Mars, in that it has oceans and a relatively thick atmosphere. This means that processes like making and dispersing clouds and the sharp contrast between land and water could add complexity to Earth’s measurements without direct ties to living things. 

They suggested 2 possible explanations for how these complicating factors relate to life on Earth. Either life allows Earth to have these factors, or Earth is inherently complex regardless of life, and therefore it can’t be used to assess how life leads to measurable complexity. To say which is the case, one would have to replicate this test with a lifeless copy of Earth. However, the team argued that Earth’s biosphere is unlikely to be wholly unrelated to its water cycle.

To bolster the validity of their methods, the team plans to expand their sample of planets to include Venus, Saturn’s moon Titan, and simulated exoplanets with modeled biochemistry and atmospheres. If the model holds up, their methodology could represent a new category of mission to find alien life, with long searches for variations in reflected light uncovering properties of distant worlds that would otherwise go unnoticed.

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Beyond the solar system, the line between planet and star can get blurry. Astronomers call objects that fall between the size ranges of planets and stars substellar objects. One type of substellar object, called a brown dwarf, forms from collapsing gas clouds like a star, but is not quite massive enough to fuse hydrogen in its core. Some can briefly fuse a heavier form of hydrogen, known as deuterium, in their cores, though never enough to produce starlight. Others are small and cold enough to develop complex atmospheres.

Recently, astronomers discovered a new type of substellar object in a star-forming region of the Milky Way Galaxy called IC 348. Using observational data from the JWST’s Near InfraRed Spectrograph (JWST NIRSpec), they found objects 0.2% to 1.2% the mass of the Sun that float freely like stars but have carbon-containing molecules called hydrocarbons in their atmospheres. Because these objects are smaller than known brown dwarfs and have evidence of hydrocarbons, scientists refer to them as H-type objects.

To investigate what H-type objects are and where they fit within the spectrum of substellar objects, researchers Richard J. Parker and Catarina Alves de Oliveira analyzed survey data on stars and substellar objects in IC 348, then conducted follow-up simulations to uncover how H-type objects formed. The main question they asked was whether H-type objects form from collapsing gas clouds, like brown dwarfs and stars, or develop around host stars before being ejected and drifting freely through space, like planets.

First, they reviewed the observational records of IC 348 obtained with the JWST NIRSpec and the near-IR spectrograph SpeX at the NASA Infrared Telescope Facility. IC 348 has 495 stars and brown dwarfs combined, as well as 9 H-type objects. Estimates for the age of this star-forming region range from either 1 to 3 million years or 5 to 6 million years, depending on how different teams calculated it. From there, Parker and Alves de Oliveira compared where the stars, brown dwarfs, and H-type objects were located within the cluster. They found no unique spatial distribution among the 3 types of objects.

Then, the team simulated the evolution of IC 348 over 10 million years to determine which initial conditions would result in a spatial distribution closest to the survey data. They simulated an initial giant gas cloud and used a mathematical function to distribute its mass among the 495 constituent objects. The researchers then placed a Jupiter-sized planet around each simulated star that was approximately the Sun’s mass to determine whether they could grow and escape their home star and become H-type objects. They also repeated the simulations by placing the Jupiter-sized planets at distances from the Sun similar to those of Earth, Jupiter, and Neptune, and varied the sizes and star densities of the simulated IC 348 regions.

They found that if Jupiter-sized planets orbited their stars at roughly Jupiter’s distance from the Sun, then 5 to 15 of them could escape their stars in a region like IC 348, which would roughly match the number of known H-type objects. However, the escaped planets were distinctly more spread-out than the observed H-type objects, which share a similar spatial distribution with the cluster’s stars and brown dwarfs. Since the simulations did not match the observed distribution, the researchers suggested that these H-type objects are likely a new sub-category of very small brown dwarfs rather than escaped planets.

Although this study favors a brown dwarf origin for H-type objects, the exact details of how they formed remain up for debate. Astronomers still have multiple competing theories about how ordinary brown dwarfs form. Perhaps they are ejected from their gas cloud before fully forming, have their outer layers stripped by radiation from a nearby massive star, or simply form like stars on a slightly smaller scale. Regardless, future researchers will continue to explore this murky area of substellar astronomy.

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In the 1970s, astronomer Vera Rubin analyzed a series of observations of how quickly different galaxies rotated around their centers. She found that these galaxies rotated much faster than they should have, given the amount of material visible through starlight. She concluded that a large fraction of these galaxies’ masses must therefore consist of some kind of unseen material.

Scientists have since observed galaxies to more precisely measure this unseen material, which they now call dark matter. They’ve shown that dark matter moves much more slowly than the speed of light. Unlike most celestial objects, it can’t emit or block any light, meaning it doesn’t interact with the electromagnetic force. Even though it doesn’t interact with a fundamental force in the universe, dark matter constitutes over 80% of the mass of an average galaxy.

In the prevailing model of cosmology, known as ΛCDM (pronounced “Lambda-CDM”), dark matter determined where galaxies formed. The theory is that gravity pulled dark matter together more quickly than regular matter in the primordial universe. This redistribution led to regions of space with greater mass density, which collected chaotic, regular matter into pools that eventually formed galaxies, stars, and planets. 

In the present universe, dark matter and regular matter coexist in galaxies, though generally separated. Regular matter pools in the very center of galaxies, forming the more familiar galactic disks. Dark matter stays towards the edges of galaxies, enveloping the regular matter disks in what are known as dark matter halos. Because these halos make up so much of the mass of the average galaxy, researchers Guangze Sun, Fangzhou Jiang, and Jing Wang conducted a series of simulations to test how dark matter halos affect regular matter in galaxies. Specifically, they examined what characteristics of dark matter halos determine galaxy sizes. 

The force of gravity works such that more massive dark matter halos draw in more regular matter and produce larger galaxies. So these researchers fixed the total mass of the galaxies they simulated at 100 billion times that of the Sun to determine which properties of the dark matter halos would produce the widest galaxies. They selected 4 variables to test: the total density of dark matter in the galactic halo, the density of dark matter in the innermost 1% of the halo, how fast the halo spun, and the ratio of regular matter to dark matter in the entire galaxy. For each variable, they tested 3 to 4 inputs representing low, moderate, and high values against a standard reference value and ran 132 total simulations to generate enough data for statistical analyses.

To simulate the complex physics of dark matter halos, the team used the modeling software GIZMO to model gravity, magnetism, and fluid dynamics. They combined this with a second model, called FIRE-3, which modeled how stars explode at the end of their lives, how much metal these explosions produce, and how gases heat up and cool down over time. The researchers ran the dynamic simulations for 3 billion model years, then used a program they developed based on the existing Pynbody to measure the radii of the simulated galaxies. 

The team found that the faster a dark matter halo spun around a simulated galaxy, the larger the galaxy became, as shown in scenario 1 in the figure below. By contrast, in scenario 2, increasing the halo’s total dark matter concentration caused the central galaxy to shrink. Increasing only the innermost dark matter density in the halo had a smaller effect but generally reduced the galaxy’s size, as shown in scenario 3. And lastly, increasing the ratio of regular matter to dark matter in galaxies resulted in smaller galaxies that formed stars closer to their centers, as shown by a greater concentration of stars in scenario 4. 

This diagram showcases the team’s results across 4 broad scenarios. The left column depicts a default galaxy under the baseline parameters. The right column depicts how the galaxies changed when the team varied a single experimental factor, including 1) increasing how fast the dark matter halo spun, 2) increasing the total density of the dark matter halo, 3) increasing the density of dark matter in the innermost halo, and 4) increasing the ratio of regular matter to dark matter in the entire galaxy. The dark matter haloes are represented as red rings surrounding the galaxies, not to scale. Illustration by Andrew Bizal.

The team’s results suggest a previously unknown connection between the properties of dark matter halos and galactic disks. However, they cautioned that their simulations were all run on galaxies in isolation, unlike in the actual ΛCDM universe, where their interactions are complicated by galaxies tugging on one another or by material moving from one to another. To further test their results for single galaxies, the researchers plan to run follow-up experiments with halos of different sizes, as well as with galaxies with different shapes and features, such as clumps, gaps, and lopsidedness.

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The economist John Maynard Keynes once remarked, “In the long run we are all dead.” From an astronomical standpoint, this is true for the Earth itself. In roughly 5 billion years, the Sun will exhaust the hydrogen at its core and enter the next phase of its life as a Red Giant, where it will swell up large enough to engulf and destroy the Earth.

Currently, the Sun is still in its main-sequence phase, where it fuses hydrogen into helium in its core. Although it remains relatively stable during this phase, it will gradually grow brighter with time. As the Sun shines brighter, the increased heat and radiation will raise Earth’s surface temperature, potentially pushing it past the threshold for life to survive. 

One of Earth’s geological processes that helps regulate its surface temperature is the carbon cycle. Over millions of years, carbon moves from the Earth’s interior into the atmosphere as carbon dioxide or CO₂, where it functions as a greenhouse gas that traps heat. Plants take in this CO₂ through photosynthesis and move carbon through the biosphere. 

Meanwhile, rain and rock weathering draw carbon into the oceans and rocks. By pulling CO₂ out of the atmosphere, the carbon cycle could mitigate Earth’s warming as the Sun brightens over time. Today, human activities are increasing atmospheric CO₂ and warming the planet. However, over billions of years, the carbon cycle could gradually remove enough CO₂ from the atmosphere that eventually too little would remain to support photosynthesis. 

With these challenges to life on Earth in mind, the researchers Jacob Haqq‐Misra and Eric Wolf investigated how long Earth’s biosphere could last in the distant future. The team used computer models to calculate the balance of energy that Earth would receive and emit over time, accounting for the effects of increasing sunlight and changing atmospheric CO₂ concentrations. They followed this up with calculations using the ExoCAM model to determine the temperature and atmospheric conditions of future Earth across different latitudes. They related these conditions to a habitability metric to determine their impact on the biosphere.

To estimate the biosphere’s maximum possible lifespan, the team conducted 29 simulations representing the planet’s conditions over the next 2 billion years. In one set of simulations, the Earth’s carbon cycle contributed little to regulating surface temperature, with the atmosphere’s CO₂ concentration fixed at approximately modern levels of 400 parts per million (ppm) and surface temperatures continuing to increase. In another, the carbon cycle worked to keep Earth’s average surface temperature fixed at the modern average of approximately 59℉ (15℃), no matter how much it had to reduce CO₂ in the atmosphere. 

In the scenario with a fixed CO₂ concentration, the team found that in 1.7 billion years, Earth’s surface reached an average of 122°F (50°C), which is too hot for most land plants. After 1.9 billion years, it reached 149°F (65°), becoming too hot for all land plants to survive as temperatures continued to rise. 

They also found that in the fixed average surface temperature scenario, atmospheric CO₂ dropped below the 150 ppm level that most plants require for photosynthesis within 500 million years. After 1.4 billion years, CO₂ levels fell below 10 ppm, leaving only a few land photosynthesizers and aquatic plants alive. After about 1.8 billion years, atmospheric CO₂ dropped to 1 ppm, leaving only microbes capable of photosynthesis.

The researchers’ findings generally extend the estimated lifetime of Earth’s future biosphere compared with earlier, simpler models that suggested maximum lifespans of 100 million to 1.5 billion years. However, they clarified that other scientists should verify their results by running tests with other models, especially under extreme conditions of high incoming solar radiation and low atmospheric CO₂. 

Lastly, Haqq‐Misra and Wolf noted that their model did not account for major evolutionary changes that could help life adapt to future conditions. Over time, plants could evolve to tolerate higher temperatures or lower atmospheric CO₂ levels. Future humans or another intelligent species might also engage in large-scale geoengineering projects to prevent the end of life on Earth. If life on Earth proves adaptable enough, the biosphere may endure until a Red Giant fills the daytime sky.

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Around 4.6 billion years ago, an immense cloud of gas and dust in space collapsed in on itself. As it collapsed, gravity pulled most of the material toward several small points, one of which would eventually become our Sun. The other points formed other stars in what scientists call an open cluster. 

During this early stage of the Sun’s life, it was surrounded by a swirling disk of leftover material aligned with its equator, known as a protoplanetary disk. Eventually, this disk condensed to form the planets that make up our solar system. This process normally results in a system with a star at its center, and the planets orbiting the plane of the star’s equator. 

However, scientists have found that this scenario doesn’t quite hold for our solar system. The planets in our solar system orbit the Sun in a single plane offset from the Sun’s equator by approximately 6°. The fact that the planets are in the same orbital plane suggests that the protoplanetary disk model of solar system formation is correct, since the odds of the Sun capturing 8 rogue planets in this arrangement are astronomically low. So, what threw the planets in our solar system off-kilter?

The plane in which the solar system’s planets orbit the Sun is offset from the Sun’s equator by approximately 6°. Li simulated how the solar system tilted based on its 4 largest planets, from left to right: Neptune, Saturn, Jupiter, and Uranus. Illustration by Andrew Bizal.

To determine what caused the solar system to tilt, Daohai Li at Queen Mary University of London recently used the computer program NBODY6++ to model different versions of the open cluster from which the Sun came. In these simulations, he varied the number of stars in the cluster, from 500 to 2,000 to 8,000. He also varied the radius of the cluster, from 0.25 to 1 to 4 parsecs (8 to 30 to 100 trillion kilometers or 5 to 20 to 70 trillion miles). Finally, he varied the percentage of stars with a binary companion from 10% to 50% to 90%. Li assigned each star a randomly generated initial mass, starting position, and velocity, and simulated their interactions for 400 million years.

First, Li examined the simulations to identify stars with masses approximately equal to the Sun’s. From this group of solar-massed stars, Li found ones that left the cluster after 400 million years as a single star, after either forming alone or having lost a former companion star. He proposed that these stars were the most similar to the Sun and could thus host a simulated solar system.

Li then simulated the planets and their orbits as they would have formed on these potential host stars. To simplify this process, Li only simulated the 4 giant planets, Jupiter, Saturn, Uranus, and Neptune, which dominate planetary behavior in our solar system. He modeled these solar systems independently for 200 million years to ensure they would remain intact and used those that did for statistical analysis of the solar system’s past.

Overall, Li discovered 2 scenarios that could explain the solar system’s tilt. In the first, the primordial solar system was jostled by many distant neighboring stars in its cluster. This jostling tilts the planets’ orbits, but it does so randomly. Therefore, this scenario would make it unlikely for the planets to remain in the same orbital plane. It would also cause them to orbit the Sun faster and more chaotically than in our solar system.

In the second scenario, the Sun had a temporary companion star that it either formed with or briefly encountered before separating. When this occurred, the simulated planets’ orbits all tilted together. Stars that formed with a binary companion had a 10% chance of tilting their planets’ orbits by 6° or more, while stars that formed alone had a less than 0.1% chance. 

Li concluded that the solar system could have tilted as a result of interactions between the Sun and a temporary companion star within its parent cluster. However, he acknowledged that the model strongly depends on how closely a star passes by the Sun, since the closer a star passes, the more it disturbs the solar system. Despite this uncertainty, it’s possible that somewhere in space is the Sun’s twin or former neighbor that we haven’t seen in eons, and that’s what made our solar system crooked!

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When you think of an asteroid impact, you’re probably imagining something like the cataclysmic event that nearly wiped out the dinosaurs. In an astonishingly rare event, a rock the size of the island of Manhattan could come hurtling from deep space towards Earth and destroy the world.

On the contrary, asteroids are generally leftover debris from the primordial solar system that didn’t become part of a larger body, like a planet or moon, and they’re not particularly rare. Scientists estimate that there are about 1 billion asteroids and comets classified as near-Earth objects, or NEOs. NEOs are generally much smaller than the asteroid that killed the dinosaurs. They range from weakly held-together piles of rubble 200 meters (700 feet) across, to the minuscule 2024 BX1, approximately 44 centimeters (17 inches) across. 

For several decades, astronomical surveys of the sky have looked for these asteroids. NASA’s Center for Near-Earth Object Studies (CNEOS) Fireball and Bolide Database has records of over 1,000 incidents of objects colliding with our planet since the mid-1990s. Among these is a set of 11 objects that scientists detected before they hit Earth, known as imminent impactors. Finding imminent impactors allows scientists unique opportunities to study the smallest building blocks of the solar system, enabling them to learn more about asteroid composition, structure, and history.

A team of scientists recently conducted a study on how the newly built Vera Rubin Observatory could detect more imminent impactors before they strike Earth. They examined the CNEOS Fireball and Bolide Database impact records spanning February 1, 1994, to January 1, 2026, for records of the asteroids’ coordinates, velocities, and the energy they released at the time of collision. 

They found that 343 NEOs met this criterion, including 5 known imminent impactors. Using these recorded data, they reconstructed the objects’ pre-impact characteristics. The team predicted the asteroids’ orbital paths from the recorded coordinates and velocity. Assuming the objects were spherical, they estimated the size from velocity and impact energy. From this information about the asteroids’ state before impact, the team could then simulate whether the Rubin Observatory would have detected them. 

When small asteroids hit Earth, they mostly burn up in the atmosphere, producing a bright flash that is relatively easy to spot compared to when they are in space. The Rubin Observatory can see objects up to 10 billion times dimmer than the brightest star in the night sky, Sirius. To predict if the Rubin Observatory can detect dim NEOs, the team used the solar system simulator SORCHA. SORCHA takes an observatory’s specifications and the physical properties of the NEOs and uses these to determine whether the observatory could detect them. It also estimates the measured sizes and speeds of the NEOs, and predicts when the observatory would have seen them. 

At the Rubin Observatory, an imminent impactor is considered discovered if it’s observed at least 3 separate times over 15 days before hitting Earth. So this team applied this same criterion to their simulation. They found that of their 343 impactors, SORCHA estimated that the Rubin Observatory would have discovered 14 of them before impact. 

They also found that SORCHA predicted 4 additional impactors that could have been observed at least once, potentially allowing scientists to search other telescope data for evidence of their existence in pre-collision images. Based on these results, the team estimated that the Rubin Observatory could find 4% of all objects longer than 1 meter (3 feet) before they hit Earth. This percentage of discovery translates to 1 to 2 imminent impactors detected every year, at least double their current discovery rate.

The ability to more consistently find imminent impactors would allow researchers to study the smallest pieces of the solar system in detail and to draw general conclusions about the properties of objects that could potentially collide with Earth. This would also allow scientists to more accurately calculate the orbits of imminent impactors, determine where more meteorites land to recover them, and keep tabs on the extremely rare but more hazardous large impactors for planetary defense initiatives. So, humanity can learn more about the ancient solar system, and hopefully avoid going the way of the dinosaurs while doing so!

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Earth has many unique features for a planet, such as a magnetic field, a large moon, and plate tectonics. It’s also the only planet we know of that harbors life. These facts form the basis of the Rare Earth hypothesis, which posits that we haven’t found aliens because other planets in the Galaxy probably don’t have all the right conditions for life. 

Another characteristic of Earth is that about 30% of its surface is land and about 70% is ocean. Recently, Columbia University Assistant Professor David Kipping investigated whether the proportion of Earth’s surface covered by dry land versus ocean, or its land fraction, is another reason Earth is habitable not only for simple single-celled organisms, but also for intelligent species like humans. 

To test this hypothesis, Kipping created 4 statistical models of planets with different land fractions that intelligent aliens could potentially evolve on. First, he created an equation to describe the likelihood that a planet in its star’s habitable zone has a particular land fraction, known as a probability distribution. Kipping weighted this probability distribution toward the extreme ends, making it more likely that a planet would be covered by a single huge landmass or a single vast ocean than by a mix of both, as on Earth. 

Kipping then incorporated this land fraction probability distribution into his statistical models to calculate the probability that a random planet will have that land fraction and host intelligent life. The 4 scenarios Kipping tested were: 1) that intelligent life is more likely to emerge on land-dominated planets, 2) that it’s more likely to emerge on ocean-dominated planets, 3) that it’s more likely to emerge on planets with roughly equal amounts of land and ocean, and 4) that its emergence is independent of a planet’s land fraction. 

As a first step in determining the kinds of planets intelligent aliens would tend to emerge on, Kipping used each model to predict the probability that intelligent life would emerge on a planet with the same land fraction as Earth. He then compared these probabilities by calculating the ratios between each value. Because Earth is the only known planet with intelligent life, a model that predicted a greater probability for humanity’s existence on Earth would be more likely to reflect reality.

Kipping considered it strong evidence that a given model was more realistic than another if the ratio between 2 of them was greater than 10, meaning one model was 10 times more likely to predict the existence of Earth and humanity. Kipping found that no comparison of any 2 models passed this threshold. However, the models assuming that intelligent life prefers ocean-dominated planets or planets with a land-ocean balance were 2.5 and 3 times more likely to predict the existence of humanity than the model assuming that intelligent life prefers land-dominated planets. Additionally, the model assuming that intelligent life prefers a land-ocean balance was always more likely to predict humanity than any other model, though marginally. 

Kipping also addressed whether finding more planets with intelligent life would affect which model was deemed most realistic, for example, if scientists discovered conclusive evidence of life on Mars in its distant past. Here, Kipping identified 2 complications. First, it’s uncertain how much of Mars’s surface was once covered by water – some estimate it had a land fraction as high as 81%, while others estimate it was as low as 25%. Second, proving that Mars once had life would not prove it once had intelligent life.

Regardless, Kipping reran the models assuming that ancient Mars had a land fraction comparable to Earth’s. Adding this second data point produced ratios similar to those in the earlier Earth-only calculations, meaning it still didn’t make any single model 10 times more likely to predict the existence of humans and Martians, respectively. 

Kipping then took the 10-times threshold and reversed the calculations to find what conditions would exceed it. In doing so, he calculated that astronomers would need to find 14 other planets with intelligent life and known land fractions to robustly determine whether intelligent life is more likely to occur on desert planets, ocean planets, balanced planets, or without bias.

Kipping concluded that he can’t yet definitively state whether there is something special about Earth’s land fraction when it comes to producing intelligent species. However, Earth’s existence would suggest that intelligent life is unlikely to favor extreme desert planets, so the Milky Way probably isn’t filled with Tatooines and Jakkus. And while his analysis doesn’t debunk the Rare Earth hypothesis, it does undermine the argument that Earth’s ocean size explains why Earth is rare. 

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Most people think of Mars as a big red dustball, but researchers recently found Martian mineral deposits suggesting it was once warm and humid. The team used the Compact Reconnaissance Imaging Spectrometer aboard NASA’s Mars Reconnaissance Orbiter to analyze specific wavelengths of visible and near-infrared light from minerals on Mars’s surface to determine their chemical composition from afar.

Past researchers identified layered silicate minerals, called clays, across the Martian surface. Clays form when water interacts with rock, and record the amounts and chemical compositions of the waters that formed them. As water interacted with Martian surface rocks, it picked up more mobile elements like magnesium and iron and carried them to lower depths in the Martian soils, while less mobile elements like aluminum stayed in place. This process, called leaching, created 2 distinct layers of clays in the Martian rocks. 

Scientists have proposed 2 main hypotheses for how these layered clays formed on Mars. The first is that they formed through underwater leaching in pools or lakes sometime in Mars’ past. The second is that they formed across the Martian surface, where a widespread humid environment provided the moisture needed to leach them. 

To evaluate these hypotheses, a team led by researchers at Purdue University recently estimated the “true” thicknesses of Martian clay layers with a method scientists had previously only used on Earth. Rock layers containing clays can become tilted, making them appear thicker or thinner than they actually are. To address this discrepancy, the team used the High Resolution Imaging Science Experiment (HiRISE) tool on the Mars Reconnaissance Orbiter to create high-resolution elevation maps of the Martian surface. Then they combined these maps with surface composition data from the Compact Reconnaissance Imaging Spectrometer to create 3D composition maps. 

Using the 3D composition maps, the researchers found where each clay layer was exposed at the surface and traced it underground to estimate an angle of tilt. They then used trigonometry to calculate the true thicknesses of each clay layer. They analysed 46 regions across the Martian surface, and found that the combined thickness of both clay layers was around 20 to 680 feet (6 to 200 meters), with an average of about 190 feet (60 meters). That’s a maximum thickness as high as a 60-story building! 

Next, the researchers tested the extent of the clay deposits in a large ancient Martian valley known as the Mawrth Vallis Region. They focused on this region because it had large elevation changes, and scientists in the past had already collected high-resolution chemical composition and elevation data there. 

They explained that if the clay layers were restricted to the lowest parts of the valley where water once existed, and had changing thicknesses and boundaries between layers, this would provide strong evidence in favor of the “underwater leaching” hypothesis. In contrast, if the clay layers were more widespread, with consistent layer boundaries and thicknesses, this would provide strong evidence of a humid surface environment, in favor of the “surface leaching” hypothesis. 

The researchers found that the clay layers extended beyond the lowest parts of the valley and had consistent layer boundaries across more than half a mile (about a kilometer) of elevation change. Thus, they concluded that the clay layers formed by surface leaching in a humid environment. 

These findings conflict with climate models of early Mars, which generally suggest that the Martian surface rarely got above freezing temperatures. To address this discrepancy, the team proposed that these deposits could have formed over a long period of time rather than in a consistently warm and wet environment. If the surface was frozen most of the time, but got above freezing in short bursts, these clay deposits could still have formed, just over a much longer time period. In this case, the Mars climate models and the researchers’ findings would agree.

The researchers acknowledged that their study has some limitations, particularly regarding the sparse sample locations. Though they found strong evidence for a widespread humid environment on early Mars, more in-depth studies of locations like Mawrth Vallis could better constrain the specific surface environmental conditions under which these clays formed and potentially reconcile their data with Martian climate models.

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The universe is around 14 billion years old, but scientists theorize that no stars formed for the first several hundred million years, during an era known as the cosmic dark ages. They refer to the first billion years or so after this, when stars formed, as the cosmic dawn. At that time, the very oldest galaxies first assembled from collections of gas and plasma. 

As these galaxies assembled and more material became available, the number of stars formed each year increased. Around 2 to 3 billion years after the Big Bang, galaxies grew faster than they ever would, producing stars at the highest rate in the universe’s history. This era is called cosmic noon.

Researchers from the Netherlands recently investigated 3 distant galaxies whose light began its journey to Earth during cosmic noon. They selected targets from a set of ancient star-forming galaxies identified in the ALMA – Archival Large Program to Advance Kinematic Analysis or ALMA-ALPAKA project. Of these, they chose to study 3 galaxies labeled ID1, ID3, and ID13.

They combined 2 different types of data to produce a detailed description of these galaxies. First, they collected data from an enormous telescope comprising 66 antennas in Chile, known as the Atacama Large Millimeter/submillimeter Array or ALMA. They used ALMA to detect radio-wave emissions from carbon monoxide and elemental carbon in these galaxies. The researchers stated that studying these chemicals in distant galaxies could reveal how their free-floating gas clouds move. They also used publicly available data from JWST’s Near Infrared Camera, or NIRCam, to determine how much light the galaxies’ stars emitted. By analyzing cosmic noon galaxies in multiple different ways, the team aimed to measure their masses and the relative contributions of regular matter and dark matter.

They used a computer program developed by other astronomers to interpret the JWST data as a series of maps showing the distribution of stars across each galaxy. They used this light-emission data to estimate the total mass of all the stars in these galaxies. Then they developed an original computer program to map the distribution of gas through each galaxy using the ALMA data. The team used these maps to create plots, known as rotation curves, which show how fast particles orbit each galaxy’s center as a function of their distance from it. 

The astronomers used these rotation curves to estimate the amount of dark matter in each galaxy. They explained that this method works because dark matter is totally invisible, but it still exerts a gravitational pull. Its gravitational pull causes visible material like stars and gas closer to the edges of these galaxies to move faster than they would in galaxies without dark matter. 

The team found that these galaxies had between 39 and 80 billion times the mass of our sun, or solar masses, in stars. They had between 4 billion and nearly 16 billion solar masses worth of free-floating gas. And they had from 1 trillion to 31 trillion solar masses of dark matter.

However, when the team compared the light-emission data with the rotation curves, they found a discrepancy. Typically, dark matter resides in a shell or halo surrounding a galaxy, meaning it should mostly affect material near the galaxy’s outer edge. Since astronomers don’t usually have to account for dark matter near a galaxy’s center, they can calculate the total mass of center material based on the amount of gas and stars they see there. But near the centers of these galaxies, the team found that the masses they derived from the light emissions were less than what they calculated from the rotation curves. 

They proposed multiple potential explanations for this discrepancy. First, they suggested that the halo shape might not be a good model for the dark matter distribution in all galaxies, meaning that cosmic noon galaxies could contain dark matter near their centers. Second, they suggested that stars could be packed tightly in the center of these galaxies, blocking each other’s light emissions. Third, they suggested that galaxy ID1 could have a supermassive black hole as big as 1.5% its total stellar mass at its center.

The team concluded that they now have a detailed picture of the mass distribution in these cosmic noon galaxies, but the reason for their center mass discrepancies remains elusive. They suggested that a complex relationship exists between the dark matter halos and the rest of the material within these galaxies. They indicated that future astronomers could adapt their methods to study the distribution of material in other distant galaxies studied by ALMA-ALPAKA and forthcoming galactic surveys.

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The Star Wars series depicted alien heroes fighting against evildoers and their planet-destroying superweapons “a long time ago in a galaxy far, far away.” But what do scientists really know about alien planets in distant galaxies beyond our own? These worlds, known as extragalactic exoplanets, are expected to exist, assuming the Milky Way is no different from other galaxies. However, we have yet to find them since other galaxies are still too far, far away for modern exoplanet-observing techniques.

Recently, a team of astronomers analyzed a stream of over 700,000 stars that the Milky Way likely absorbed from the dissolving Sagittarius dwarf galaxy. These stars are very distant, so the team investigated whether any of them host large, close-orbiting exoplanets called hot Jupiters, which are relatively easy to find.  

They established a set of 3 criteria to narrow down their list of stars. First, each star should appear bright enough when observed by the Transiting Exoplanet Survey Satellite, or TESS, to ensure high-precision results from the team’s data processing software. Second, each star must have more than a 50% likelihood that it originated from the Sagittarius dwarf galaxy, based on motion and position measurements from the Gaia mission. Finally, each star should have a radius of less than twice the Sun’s, as it’s easier to find planets around smaller stars. They used these criteria to limit their candidate list to around 20,000 stars.

After selecting their candidate stars, the team analyzed publicly available TESS catalog data using the software packages eleanor and TESS-Gaia Light Curve, or TGLC. These tools allowed them to plot each star’s brightness over time, in graphs called light curves. Then, the astronomers looked for periodic brightness dips in these light curves as evidence that an exoplanet passed in front of the star. From this, they excluded several thousand additional stars with too much light interference from their surroundings, reducing their final sample size to just over 15,000 stars.

To find hot Jupiters, the team looked for brightness dips at intervals of 14 hours to 10 days, which is the typical orbital period range for hot Jupiters. Then, they used geometry to derive each exoplanet’s radius from the fraction of the starlight it blocked. They excluded candidates with dips corresponding to objects with radii at least twice that of Jupiter’s, as these are likely caused by orbiting companion stars rather than exoplanets.

Among all the stars they surveyed, the team’s strongest candidate to host a hot Jupiter was a star labeled TIC 92223525. They calculated that this star could host an exoplanet with a radius 1.76 times the size of Jupiter’s and an orbital period of 7.2 days. However, when they reviewed this star’s light curve, they found that it was likely contaminated by its neighbor, TIC 92223526. The regular brightness dips from this system of orbiting stars mimicked that of an exoplanet, creating a false positive for TIC 92223525 that was difficult to detect during initial screening. As a result, the team ultimately excluded this candidate, leaving them with no confirmed exoplanets.

The researchers drew several conclusions from their inability to find hot Jupiters in their sample of stars from the Sagittarius dwarf stream. They estimated that if more than 1% of these stars hosted hot Jupiters, it would have been highly unlikely not to detect one in a sample of over 15,000 stars. This places an upper limit of about 1% on the occurrence rate of hot Jupiters. If this estimate is accurate, then even an ideal exoplanet search team would need to examine over 11,000 stars to find an extragalactic hot Jupiter. Accounting for more realistic levels of scientific uncertainty, a future team would likely need to study at least 80,000 stars to find one. 

Although this survey of the Sagittarius dwarf stream yielded null results, the team suggested that future researchers continue searching it and other star streams from different galaxies. Scientists have identified over 20 such streams in the Milky Way. Researchers studying these streams could find the first extragalactic exoplanet or provide evidence that other galaxies produce fewer hot Jupiters than our own. But let’s hope none of them find the first extragalactic Death Star!

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Stars are mostly made of 2 elements: hydrogen and helium. While this has always been the case, those 2 elements and lithium were the only elements in existence when the Big Bang occurred around 14 billion years ago. When the first stars exploded, they released those primordial elements, as well as heavier elements produced by nuclear fusion inside them. 

Astronomers call all elements heavier than hydrogen and helium metals, a term chemists use quite differently. Subsequent generations of stars, including the Sun, formed in clouds of gas and dust enriched with these metals, such as carbon, oxygen, magnesium, and silicon. Scientists estimate that modern stars are 1% to 5% metal by mass.

Astronomers claim there is no solid evidence that stars contain exceptionally high amounts of metals, but some, called chemically peculiar stars, appear to. Astronomers study stars by looking at the patterns of light they emit, called spectra. Each element produces a unique light pattern, so astronomers can compare the light patterns in a star’s spectra to determine how much of each element is present, especially in the outer layers of the star. Researchers theorize that chemically peculiar stars don’t actually have more metals than average stars. Instead, they think that metals from their interiors diffuse to their outer layers more than in most stars.

A team of researchers from the American Association of Variable Star Observers and Masaryk University in Czechia recently observed 85 chemically peculiar stars to understand their behavior and better classify them. For their study, they first used the General Catalog of CP Stars, published in 2009 in Astronomy & Astrophysics, to identify targets across the 4 classes of these stars, labeled CP1 through CP4. CP1 stars have strong spectral patterns for iron and other heavy elements, CP2 stars have strong patterns for silicon, chromium, strontium, and europium, CP3 stars have strong patterns for mercury and manganese, and CP4 stars have either unusually weak or usually strong helium patterns. 

The team compiled a list of 85 stars to observe, then used the BRIght Target Explorer (BRITE) Constellation to monitor changes in their brightness. The BRITE Constellation is a set of 5 satellites equipped with telescopes and cameras for either red or blue light. Using the BRITE Constellation, the team monitored each star for several days. 

They found that 74 of these 85 chemically peculiar stars varied in brightness during their survey. They attributed this to the varied abundance of metals on their surfaces, which would form dark patches that go in and out of view from Earth’s perspective as the stars rotate. The team observed that 6 of these 74 stars appeared to change in brightness over multiple periods. They were surprised by this result because a star’s brightness wouldn’t vary over multiple periods if the changes were due to rotation. They compared their findings to data other scientists had collected from these stars with the Transiting Exoplanet Survey Satellite, or TESS, and found that all 6 stars had been misclassified as chemically peculiar stars.

The other 11 chemically peculiar stars appeared to show no periodic changes in their brightness, suggesting that they’re stationary. The team claimed that some CP1 and CP3 stars don’t rotate, but they identified cases in which CP2 and CP4 stars that ought to rotate appeared to be stationary. They suggested 2 potential reasons for this. One is that these CP2 and CP4 stars are misclassified, requiring more thorough analysis of their spectra to confirm their classifications. The other is that the stars rotate slowly, with rotational periods of 50 days or longer, which would be difficult to distinguish from those of totally stationary stars.

The team concluded that more astronomers should revisit the historical classifications of stars, especially as technology advances and more space-based telescopes become available. This strategy would allow future researchers to draw better data from research archives and catalogs. Additionally, they claimed that their method of pairing long-term monitoring via small satellites with TESS data is well-suited for refining classifications, identifying misclassified objects, and further exploring the structure and mechanics of chemically peculiar stars.

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