An automated exoplanet transit data pipeline built in Python. Locally filters raw Kepler/TESS telescope telemetry using adaptive flattening, discovers exact orbital periods via BLS sweeps, and derives precision physical geometries, signal SNR, and morphology models.
PLANAR: bias-aware unsupervised morphology discovery for protoplanetary disk observations (EXXA pipeline).
An end-to-end pipeline for automated exoplanet detection from TESS photometric light curves, combining Box Least Squares periodogram search with a Dual-Branch 1D Convolutional Neural Network (TransitCNN) to classify transit candidates as planets, eclipsing binaries, or false positives.
VESPER- Validation Engine for Stellar Photometric Evidence and Recovery. Evidence-first exoplanet detection pipeline for computationally efficient transit discovery using TESS and Kepler light curves.
We pointed a laptop at NASA's TESS data and found 197 exoplanet transit candidates. Rust-powered BLS detection, 10-50x faster than Python.
Learning guide for exoplanet detection using machine learning on TESS data
Federation of TinyML for Space Science - Democratizing exoplanet discovery. Process NASA data, detect transits, and discover new worlds.
🌟 AI-powered exoplanet detection system using NASA data for Space Apps 2025 hackathon. Features BLS/TLS algorithms, machine learning classification, and real-time analysis pipeline. | 使用 NASA 資料的 AI 系外行星偵測系統，專為 2025 太空應用程式挑戰賽開發。
ML pipeline for detecting and characterizing exoplanets from Kepler/TESS light curves
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