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Hybrid CNN-Transformer model for automated exoplanet transit detection on NASA Kepler light curves. Features dual scale CNN branches, Transformer based sequence modelling, Grad CAM + SHAP + attention explainability, and Monte Carlo Dropout uncertainty quantification. 5 fold cross validated with baseline comparisons.
Interactive site documenting my TIFR NIUS 2025 astrophysics research — CCD image calibration pipeline (bias frames, aperture photometry, image subtraction) and exoplanet transit detection using TESS light-curve data, PLD systematics correction, and Box Least Squares period finding.
AI pipeline that detects and classifies exoplanet transits in noisy TESS light curves - BLS/TLS search, 15 astrophysical vetting tests, a calibrated transit / eclipsing-binary / blend / other ML ensemble, and Bayesian fitting of period, depth & duration with uncertainties, plus one-page vetting sheets and a 3-page report. (BAH 2026 PS7)
Educational Python toolkit: builds an analytic trapezoid transit model and overlays it on real TESS/Kepler photometry folded on a planet's published ephemeris.
An end-to-end AI-driven pipeline for automated exoplanet detection, using a hybrid framework of 1D CNNs and Box Least Squares (BLS) to filter, classify, and physically model transit signals from noisy, crowded-field TESS light curves.
Autonomous TESS exoplanet-candidate discovery + adversarial AI tribunal: classical transit search, six AI skeptics, provenanced dossiers on a static site.