ExoDiscover

A classifier for NASA exoplanet transit signals, trained on Kepler and tested on TESS.

This is a rebuilt version of a NASA Space Apps 2025 hackathon submission (A World Away: Hunting for Exoplanets with AI). The original reported a strong accuracy that was measuring the wrong thing: the Kepler table ships the vetting pipeline's own verdict as a column. This version quarantines those columns, holds out whole host stars rather than rows, and reports what the model does on a different mission's data. See docs/LEAKAGE.md for the investigation.

77% accuracy zero-shot on TESS      (majority-class baseline 51%)
ROC-AUC 0.838 · Brier 0.177 · n = 2,562 resolved TESS objects

The app

16,932 catalogued objects at their real right ascension, declination and distance, with Earth at the origin. Drag to look, scroll to travel, click a planet.

Both missions are shown. Kepler observed a single 22°×16° window, so its 9,444 objects form a dense beam in one direction. TESS surveyed the whole sky, so its 7,488 objects lie in every direction, the nearest 21 light years away.

Clicking a planet shows the model's prediction, the archive's disposition, and the SHAP terms behind the score. Both missions are scored by the same 11-feature model, the only features the two catalogues share, so the probabilities are comparable.

Run it

Everything runs on localhost. The trained model and its metrics are committed, so no catalogue download or training is required.

Prerequisites: Python 3.11 or 3.12, Node 20+.

make install     # pip install -e ".[dev,api]", then npm install in web/

Then, in two terminals:

make serve       # FastAPI on :8000, interactive docs at /docs
make web         # React UI on :5173

Open http://localhost:5173.

Without make (Windows, or no GNU make installed):

pip install -e ".[dev,api]"
cd web && npm install && cd ..
uvicorn api.main:app --reload --port 8000     # terminal 1
cd web && npm run dev                         # terminal 2

Or run the whole stack with docker compose up --build.

Regenerating the artifacts

Only needed to reproduce them rather than use the committed ones:

exo ingest              # NASA archive to data/raw/
exo train --trials 40   # ~25 min, CPU only (exo train --fast, ~6 min)
exo skymap              # joins positions and distances for the scene

Checks

make test        # pytest (offline, against committed fixtures) + vitest
make lint        # ruff, mypy, eslint

Layout

ml/exodiscover/    ingest, leakage firewall, physics features, training, evaluation
api/               FastAPI: typed prediction, batch CSV, SHAP, metrics, sky map
web/               React and WebGL, reading from the API
docs/              LEAKAGE.md, MODEL_CARD.md, metrics/
tests/             128 Python tests and 28 web tests, offline against fixtures

docs/LEAKAGE.md covers the leakage investigation. docs/MODEL_CARD.md records intended use and limitations.

Python 3.11, scikit-learn, CatBoost/XGBoost/LightGBM, Optuna, SHAP, FastAPI, React, TypeScript, Vite, Tailwind, pytest, vitest, ruff, mypy, GitHub Actions. CPU only.