Production exoplanet detection pipeline — 8-branch CNN + BLS search.
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.
🌟 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。
Learning guide for exoplanet detection using machine learning on TESS data
Reproducible exoplanet injection-recovery benchmark comparing Box Least Squares with wavelet-scattering transit detection across synthetic light curves, red noise and flares, preserving negative results and method limitations.
Federation of TinyML for Space Science - Democratizing exoplanet discovery. Process NASA data, detect transits, and discover new worlds.
ML pipeline for detecting and characterizing exoplanets from Kepler/TESS 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.
Detection of transiting exoplanets in raw Kepler SAP-flux photometry — coarse-to-fine BLS search + calibrated ML vetting. Built for the IIT Tirupati Computational Astronomy Hackathon (Winning Solution, Rank 1, 2026).
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.
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