How Citizen Scientists Are Discovering Exoplanets in 2026

Right now, ordinary people are discovering planets around distant stars using nothing more than a laptop and an internet connection. S.O.L.A.R.I.S. (Stellar Object Light Analysis & Retrieval Imaging System) is a citizen science project that harnesses volunteer computing power to search NASA TESS satellite data for new exoplanets — and it is finding them at a remarkable rate.

What Is Citizen Science Exoplanet Discovery?

Citizen science exoplanet discovery is the practice of using volunteer-contributed computing power to search for planets orbiting other stars. Instead of relying solely on professional observatories and university supercomputers, projects like S.O.L.A.R.I.S. distribute the computational work across thousands of personal computers around the world.

The raw data comes from NASA's Transiting Exoplanet Survey Satellite (TESS), which continuously monitors the brightness of hundreds of thousands of stars. This data is publicly available through the Mikulski Archive for Space Telescopes (MAST). S.O.L.A.R.I.S. downloads TESS light curves and distributes them to volunteers for analysis.

What Is the Transit Method for Finding Exoplanets?

The transit method is the primary technique used to detect exoplanets from space. When a planet passes between its host star and our telescopes, it blocks a small fraction of the star's light. This creates a characteristic dip in the star's brightness that repeats with each orbit.

A Jupiter-sized planet can block about 1% of a Sun-like star's light. An Earth-sized planet blocks only about 0.01%. Detecting these tiny, repeating signals in noisy data is computationally intensive, which is exactly why distributed volunteer computing is so valuable.

Why M-Dwarf Stars?

S.O.L.A.R.I.S. focuses its search on M-dwarf stars (red dwarfs) for two key reasons:

• Larger transit signals — M-dwarfs are smaller than the Sun, so a planet blocks a proportionally larger fraction of light, making transits easier to detect.
• Closer habitable zones — Because M-dwarfs are cooler and dimmer, the habitable zone (where liquid water could exist) is much closer to the star. This means habitable-zone planets orbit faster and transit more frequently, giving us more data points to work with.

M-dwarfs are also the most common type of star in our galaxy, making up roughly 70% of all stars. This means there are an enormous number of potential habitable worlds waiting to be found.

How Does S.O.L.A.R.I.S. Detect Planets?

The detection pipeline uses a two-stage approach that combines speed with precision:

Stage 1: BLS Transit Detection

Box Least Squares (BLS) is an algorithm specifically designed to find periodic box-shaped dips in time-series data. It works by systematically testing thousands of possible orbital periods and transit durations, looking for the combination that best matches a repeating dip pattern in the light curve. When the BLS signal-to-noise ratio exceeds a threshold, a candidate is flagged for deeper analysis.

Stage 2: MCMC Orbital Fitting

Once a candidate is detected, Markov Chain Monte Carlo (MCMC) fitting is used to precisely determine the planet's orbital parameters. MCMC is a statistical method that explores the space of possible solutions — planet size, orbital period, inclination, and more — to find the values that best explain the observed light curve. This produces not just best-fit values but also uncertainty estimates, which is critical for assessing the reliability of a detection.

What Happens When a Planet Is Discovered?

When a volunteer's computer detects a strong transit signal and the MCMC fitting confirms a viable candidate, the results are sent back to the S.O.L.A.R.I.S. server for validation. The process from detection to confirmed candidate involves several steps:

• Automated vetting — False positives from eclipsing binaries, instrumental noise, and stellar variability are filtered out.
• Parameter validation — Orbital parameters are checked for physical plausibility (reasonable period, radius, and equilibrium temperature).
• Habitable zone assessment — The planet's equilibrium temperature and orbital distance are evaluated against the host star's habitable zone boundaries.
• Earth Similarity Index (ESI) — A composite score comparing the planet's properties to Earth's, providing a single metric for how "Earth-like" the candidate is.
• Public catalog — Confirmed candidates are added to the S.O.L.A.R.I.S. discoveries page with full parameters and light curve data.

The most notable discovery so far is SOLARIS-002, which has an Earth Similarity Index of 98.3% — making it one of the most Earth-like exoplanet candidates ever identified through citizen science.

How Can I Help Discover Exoplanets?

Joining S.O.L.A.R.I.S. is free and takes about two minutes. Here is exactly what to do:

1. Download the volunteer software — Go to solarisdiscovery.com and download the S.O.L.A.R.I.S. Volunteer package for your platform. It is available for macOS, Windows, and Linux, and the entire package is under 1MB.
2. Unzip and run — Extract the downloaded zip file and run the volunteer executable. No installation required — it runs as a standalone program.
3. Let it work in the background — The software automatically connects to the S.O.L.A.R.I.S. server, receives TESS light curve data, runs BLS transit detection and MCMC fitting, and reports results. It uses minimal system resources.
4. Track the discoveries — Visit the discoveries page to see every confirmed planet candidate, including habitable zone worlds and their Earth Similarity Index scores.