On January 22, 2026, NASA announced that its updated ExoMiner++ AI system had identified roughly 7,000 targets as exoplanet candidates during an initial pass through data from the Transiting Exoplanet Survey Satellite (TESS). The result has circulated online as a breakthrough discovery, but the headline requires a critical qualification: these are candidates, not confirmed planets. They are promising transit signals that resemble planetary transits and require additional observations before they can be confirmed as actual worlds.
The January 2026 Announcement
On January 22, 2026, a team at NASA’s Ames Research Center published results showing that ExoMiner++ had evaluated TESS observations and flagged approximately 7,000 targets as exoplanet candidates. The finding was reported in a peer-reviewed paper titled “ExoMiner++: Enhanced Transit Classification and a New Vetting Catalog for 2-minute TESS Data” and made public through NASA’s science portal.
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This result represents a significant acceleration in the vetting process for exoplanet signals. However, to understand what “7,000 candidates” actually means, you need to know what happens inside NASA’s exoplanet search pipeline and why machine learning has become essential.
What Exactly Is ExoMiner++?
ExoMiner++ is a deep-learning system—an artificial neural network trained to recognize patterns—designed to distinguish real planetary transit signals from false positives and noise. It is the updated version of ExoMiner, an earlier AI system that NASA developed to evaluate Kepler mission data. In 2021, the original ExoMiner validated 370 new exoplanets from Kepler observations by identifying promising signals and filtering out mimics.
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The upgraded ExoMiner++ was trained on data from both the retired Kepler mission and the active TESS mission, giving it experience with a broader range of stellar types and observing conditions. NASA has made the software freely available as open-source code, allowing other researchers to use and improve it.
How NASA Finds Exoplanets: The Transit Method
To understand why AI screening matters, you first need to know how TESS detects planets in the first place.
TESS (Transiting Exoplanet Survey Satellite) observes the brightness of distant stars over weeks or months. When a planet orbits between its star and Earth, it blocks a tiny fraction of the star’s light—typically less than 1 percent—causing a small, periodic dip in brightness. Astronomers call this a transit.
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Now the problem: not every dip in brightness is a planet.
Why Candidates Are Not Confirmed Planets
A planet candidate is a signal that resembles a planetary transit but has not yet passed all the checks needed to rule out other explanations. The TESS pipeline and machine-learning systems like ExoMiner++ flag promising candidates, but many can be false positives or mimics caused by:
- Eclipsing binary stars: Two stars orbiting each other can produce brightness dips identical to planetary transits.
- Stellar variability: Sunspots and stellar activity can mimic transit signals.
- Instrumental artifacts: Glitches in the telescope or data processing can create false dips.
- Blended stars: A nearby background star could be producing the signal, not the target star.
- Other astrophysical events: Pulsating stars, rotating stars with cool spots, or other phenomena can resemble transits.
A validated planet is a candidate supported by statistical or observational evidence strong enough to establish its planetary nature—often through multiple transits, additional wavelength observations, or independent analysis. A confirmed planet is one whose existence has been established through accepted follow-up evidence, such as radial-velocity measurements (detecting the star’s tiny motion caused by the planet’s gravity) or high-resolution imaging to rule out blending effects.
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What ExoMiner++ Actually Analyzes
An important technical distinction: ExoMiner++ does not currently search raw TESS images from scratch and independently discover transit signals. Instead, it evaluates a list of possible transit signals that have already been identified by existing automated pipelines. The model applies patterns learned from thousands of previously vetted examples to classify each signal as more or less likely to represent a real planet.
NASA’s documentation states that the team is working toward enabling ExoMiner++ to identify signals directly from raw data without relying on pre-screened candidate lists. That capability would be a more complete end-to-end system, but it is still under development.
This distinction matters: the “single sweep” language in headlines suggests the AI independently discovered 7,000 planets from raw observations. The more accurate picture is that ExoMiner++ screened a list of possible transit signals and classified 7,000 as candidate planets.
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Why AI Acceleration Matters
TESS generates enormous datasets. The TESS pipeline identifies hundreds of thousands of possible transit-like signals. Manually vetting each one would require years of expert astronomer time. ExoMiner++ can process signals consistently and at scale, flagging the most promising candidates for human review and follow-up observation. This accelerates the overall discovery process without replacing human judgment.
A machine-learning system excels at finding statistical patterns in large datasets. By training on examples of real planets and false positives, ExoMiner++ learns which signal characteristics are more commonly associated with genuine planets. This frees telescope time and astronomer effort to focus on the strongest candidates.
What the 7,000 Candidates Could Be
The 7,000 flagged by ExoMiner++ will likely include a wide range of planetary types and orbital configurations:
- Gas giants: Large planets that orbit very close to their stars (hot Jupiters), or more distant giant planets.
- Sub-Neptunes and mini-Neptunes: Planets between Earth and Neptune in size, common around other stars but absent in our solar system.
- Potentially rocky planets: Smaller, Earth-like objects, some of which might orbit in their star’s habitable zone (the distance at which liquid water could exist on a planet’s surface).
- Planets around hot stars: Objects receiving intense stellar radiation, likely too hot for life as we know it.
- Planets around cool, faint stars: Easier to detect by transit because the star is dimmer and a planet blocks a larger percentage of light.
The announcement provides no evidence that the 7,000 candidates are preferentially habitable, Earth-sized, or likely to harbor life. Many will be gas giants, impossibly hot, or otherwise unsuitable for known life. Assessing the potential habitability of any candidate requires additional measurements of the planet’s size, mass, atmospheric composition, and the host star’s activity—none of which the initial AI screening provides.
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What Happens After AI Screening?
Once ExoMiner++ flags a candidate, the work has only begun. Follow-up observations might include:
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- Reobservation: Confirming that multiple transits occur at the expected times and with consistent properties.
- Radial-velocity measurements: Using ground-based telescopes to detect the star’s tiny motion caused by the planet’s gravitational pull, which helps estimate the planet’s mass.
- High-resolution imaging: Checking whether a nearby or background star could explain the signal (ruling out “blended” false positives).
- Spectroscopy: Analyzing the star’s light to measure its properties and determine whether the transit signal is genuine.
- Continued TESS observations: Gathering additional transits as TESS observes the same regions of sky in successive missions.
This follow-up work is the real bottleneck. Space-based and ground-based telescopes have limited observing time. Not every candidate can be immediately prioritized. Some candidates will never receive follow-up because they are too faint, require too much observation time, or have lower scientific interest. Others will be confirmed as planets, validated as likely planets through statistical analysis, or ruled out as false positives.
The Broader Candidate Backlog
The 7,000 ExoMiner++ targets enter a much larger candidate pool. In September 2025, NASA announced that it had confirmed its 6,000th exoplanet. At that same time, more than 8,000 additional candidates were awaiting confirmation—meaning nearly as many promising but unconfirmed signals existed as confirmed planets.
As of July 27, 2026, the NASA Exoplanet Archive (the official repository of planet discoveries) listed 8,113 TESS project candidates. This number is a live tally that changes as candidates are added, removed, reclassified, or confirmed. The ExoMiner++ 7,000-candidate run contributes to this larger candidate pool, though not every ExoMiner++ target may be a new entry to the database.
The implication is clear: NASA has a substantial backlog of promising signals waiting for confirmation. ExoMiner++ helps prioritize this list, but it does not solve the fundamental follow-up bottleneck.
Why Open-Source Matters
NASA released ExoMiner++ as open-source software, freely available through its GitHub repository. This decision has several benefits:
- Reproducibility: Other researchers can run the model on TESS data or other datasets and verify results.
- Improvement: Astronomers worldwide can identify limitations, propose refinements, and contribute improvements to the system.
- Transparency: The code is public and auditable, reducing the risk of hidden biases or errors.
- Democratization: Researchers without NASA computational resources can access and use the tool.
This openness reinforces that the 7,000-candidate identification is not proprietary NASA knowledge but a verifiable scientific result that can be scrutinized and built upon by the broader astronomy community.
Comparing ExoMiner and ExoMiner++
The original ExoMiner (reported in 2021) validated 370 exoplanets from Kepler data by applying machine-learning classification to previously identified candidate signals. It demonstrated that deep-learning systems could effectively distinguish planets from false positives at scale.
ExoMiner++ represents a natural progression:
- Training on both Kepler and TESS data, providing the model with a larger and more diverse set of examples.
- Focusing on TESS, whose ongoing observations are generating enormous numbers of new candidate signals.
- Processing approximately 7,000 targets in an initial run—a much larger number than the 370 planets validated by the original system.
- Continued development toward end-to-end raw-data signal identification, rather than relying on pre-screened candidate lists.
The update is not a revolutionary departure but an evolution reflecting the scale and ambition of modern survey telescopes like TESS.
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Key Technical Limitations
Machine-learning systems learn from their training data. If the Kepler and TESS examples used to train ExoMiner++ underrepresent certain types of stars, orbital periods, planet sizes, or observing conditions, the model’s performance may vary across different kinds of signals. This is a normal limitation of the approach and does not invalidate the result, but it is worth noting.
The 7,000 figure does not automatically equal 7,000 unique planetary systems. A single star might host multiple planets, each producing separate transit signals. A planet observed in more than one TESS sector could be flagged multiple times. The exact number of distinct planetary systems represented in the 7,000 is not specified in the available announcement.
Signal quality varies. The brightness, orbital period, transit depth, and signal-to-noise ratio of the 7,000 candidates are not uniform. Some will be obvious candidates suitable for prompt follow-up; others will be marginal and require significantly more observational effort to confirm or rule out.
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The Bottom Line: Candidates, Not Confirmed Worlds
NASA’s ExoMiner++ AI identified 7,000 promising exoplanet candidates in TESS data, representing a significant acceleration in signal vetting and prioritization. The breakthrough is the scale and speed at which the system can classify transit-like signals—not the immediate confirmation of 7,000 new planets.
These 7,000 targets are the beginning of a multi-year follow-up process. Some will be confirmed as planets, others as validated through statistical evidence, and many will eventually be ruled out as false positives. The real scientific value emerges as astronomers prioritize these candidates for observation and confirmation, gradually converting the most promising signals into verified planets and expanding our census of worlds around other stars.
Frequently Asked Questions
Are these 7,000 objects confirmed planets?
No. They are exoplanet candidates—promising signals that resemble planetary transits but require follow-up observations to confirm. The candidates could also be mimics such as eclipsing binary stars, stellar variability, or instrumental artifacts.
What is ExoMiner++?
ExoMiner++ is an artificial neural network developed by NASA’s Ames Research Center to classify possible transit signals as more or less likely to represent real planets. It is an updated version of the original ExoMiner system that validated 370 Kepler exoplanets in 2021. The new version was trained on data from both Kepler and TESS.
How does TESS find exoplanets?
TESS observes the brightness of distant stars. When a planet passes in front of its star, it blocks a small fraction of the star’s light, causing a tiny, periodic dip in brightness. This is called a transit. Automated pipelines identify these possible transit signals, and ExoMiner++ screens them to prioritize the most promising candidates.
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Did ExoMiner++ search all of TESS’s raw data on its own?
No. ExoMiner++ currently evaluates a list of possible transit signals that have already been identified by existing automated pipelines. NASA is working toward enabling it to identify signals directly from raw data, but that capability is still under development.
When was this announced?
NASA announced the ExoMiner++ result on January 22, 2026. The work was published in the Astronomical Journal in a paper titled ‘ExoMiner++: Enhanced Transit Classification and a New Vetting Catalog for 2-minute TESS Data.’
How many exoplanet candidates are currently awaiting confirmation?
As of July 27, 2026, the NASA Exoplanet Archive listed 8,113 TESS project candidates. This is a live database that changes as candidates are added, removed, or confirmed. In September 2025, NASA reported more than 8,000 candidates awaiting confirmation when the confirmed exoplanet count reached 6,000.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsWhat happens to these candidates after ExoMiner++ flags them?
Candidates enter a follow-up process. Astronomers may observe them with ground-based telescopes to measure the star’s motion (radial velocity), check for blending artifacts, analyze spectral properties, or gather additional transit observations. This work can take months or years and often requires shared telescope time among competing research teams.
Could any of these candidates be habitable planets?
Possibly, but the 7,000-candidate figure alone provides no evidence. Assessing habitability requires knowing the planet’s size, mass, orbital period, host-star temperature, and atmospheric properties—none of which the initial AI screening provides. Many candidates will be gas giants, extremely hot planets, or unsuitable for life.
Can I download and use ExoMiner++?
Yes. NASA has released ExoMiner++ as open-source software freely available through its GitHub repository (github.com/nasa/exominer). Anyone can download the code, inspect it, and apply it to exoplanet data.
Is ExoMiner++ a completely new AI system?
No. It is an updated version of the original ExoMiner, which was reported in 2021 and validated 370 Kepler exoplanets. ExoMiner++ incorporates training from both Kepler and TESS data and is designed to handle the large volume of TESS observations.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchWhy does confirming exoplanets take so long?
Following up on a candidate is time-consuming and requires scarce telescope resources. Astronomers must rule out false positives (eclipsing binaries, stellar variability, blended stars), perform radial-velocity measurements to estimate planetary mass, and gather additional transit observations. Many candidates never receive immediate follow-up due to limited observing time.
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