Stellar Object Light Analysis & Retrieval Imaging System
Join thousands of volunteers using spare computing power to search for life beyond Earth
Get the lightweight volunteer package — under 1 MB, no install required
Double-click the launcher — it sets up everything automatically, no terminal needed
Your computer performs photometric time-series analysis on NASA TESS stellar light curves
Candidates are scored and validated through a multi-stage pipeline
From first light to thousands of stars — the story of S.O.L.A.R.I.S. so far
Our most Earth-like candidates — ranked by similarity to our home planet
Contribute your computing power to the hunt for habitable exoplanets
S.O.L.A.R.I.S. Volunteer requires a desktop computer (Mac, Windows, or Linux) with Python 3.9+. Visit this page on your computer to download and join the search!
Navigate through an interactive Three.js visualization of every star we've searched and every planet we've discovered — color-coded by habitability, filterable by biosignatures, and rendered in real-time 3D.
OPEN 3D DASHBOARD EXPLORE STAR MAPS.O.L.A.R.I.S. (Stellar Object Light Analysis & Retrieval Imaging System) is a distributed citizen-science project dedicated to discovering habitable exoplanets through photometric time-series analysis. By harnessing volunteer computing power from participants worldwide, S.O.L.A.R.I.S. performs transit detection via the BLS algorithm on publicly available light curve data from NASA's Transiting Exoplanet Survey Satellite (TESS) to search for planets orbiting other stars.
The project employs a multi-stage candidate validation pipeline: when an exoplanet crosses in front of its host star, it causes a measurable dip in brightness. S.O.L.A.R.I.S. applies Box Least Squares (BLS) algorithms to identify periodic transit signals, then uses phase-folding techniques to stack multiple transits and improve detection sensitivity. Orbital parameter estimation through MCMC sampling determines period, planetary radius, and semi-major axis with robust uncertainty quantification. The pipeline focuses on M-dwarf stars, where habitable zone planets produce stronger transit signals relative to signal-to-noise ratio (SNR) thresholds and are easier to detect.
Since its launch, S.O.L.A.R.I.S. has searched over 36,000 stars and identified 54+ transit candidates, including worlds with Earth Similarity Index scores as high as 98.3%. All candidates are statistical detections requiring professional follow-up observations for confirmation. The project is completely free to join and runs on macOS, Windows, and Linux. All discoveries are published on the public dashboard and interactive 3D star map.
Whether you are an astronomer, a student, or simply curious about the cosmos, S.O.L.A.R.I.S. offers a way to contribute to real exoplanet science from your own computer. Download the volunteer package and start searching for habitable worlds today.
S.O.L.A.R.I.S. (Stellar Object Light Analysis & Retrieval Imaging System) is a citizen-science project that uses volunteer computing power to perform photometric time-series analysis on NASA TESS satellite data. It operates a transit detection pipeline employing BLS algorithms and MCMC orbital fitting to identify exoplanet candidates orbiting distant stars.
Download the free volunteer package (under 1 MB) for your operating system. Unzip it and double-click the launcher. It automatically installs Python dependencies and begins searching for exoplanets. No account is required to participate.
As of March 2026, S.O.L.A.R.I.S. has identified 54+ transit candidates across 36,000+ stars searched, including candidates with Earth Similarity Index scores as high as 98.3%. These are statistical detections based on photometric data and require professional follow-up observations (radial velocity confirmation, high-resolution imaging) before they can be classified as confirmed exoplanets. View all results on the discoveries page or explore the 3D star map.
Yes, completely free. There are no fees, subscriptions, or hidden costs. S.O.L.A.R.I.S. is an open citizen-science project. The volunteer software, data, and results are all freely available.
You need Python 3.9 or later, at least 4 GB of RAM, and an internet connection. S.O.L.A.R.I.S. runs on macOS, Windows, and Linux. The volunteer package automatically installs all required Python dependencies on first launch.
S.O.L.A.R.I.S. uses the transit method with a multi-stage candidate validation pipeline. The Box Least Squares (BLS) algorithm scans TESS photometric time-series data for periodic brightness dips. Detected signals are phase-folded to stack multiple transits and improve signal-to-noise ratio (SNR). Candidates passing SNR thresholds undergo Markov Chain Monte Carlo (MCMC) fitting to estimate orbital period, planetary radius, and semi-major axis with full posterior distributions. The pipeline then applies false alarm probability tests and transit morphology checks before classifying validated signals as exoplanet candidates. Learn more in How It Works or view the full methodology.
Each star typically takes 1 to 5 minutes to analyze, depending on the quality of the light curve data and your hardware. The software processes stars automatically in the background, so you can leave it running while you use your computer normally.
Yes. The volunteer dashboard opens in your browser and shows real-time results for every star analyzed, including light curve plots, transit fits, and habitability scores. All confirmed discoveries are published on the public dashboard and 3D star map.
Kovács, G., Zucker, S., & Mazeh, T. (2002). "A box-fitting algorithm in the search for periodic transits." Astronomy & Astrophysics, 391, 369–377. arXiv:astro-ph/0206099
Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. (2013). "emcee: The MCMC Hammer." Publications of the Astronomical Society of the Pacific, 125, 306. arXiv:1202.3665
Kopparapu, R. K. et al. (2013). "Habitable Zones around Main-sequence Stars: New Estimates." The Astrophysical Journal, 765, 131. arXiv:1301.6674
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Jenkins, J. M. et al. (2016). "The TESS science processing operations center." Proc. SPIE 9913, Software and Cyberinfrastructure for Astronomy IV. DOI:10.1117/12.2233418