I have recently entered 12th grade, and I've been obsessed with exoplanets for a while now. Not in a casual way — I mean the kind of obsession where you start wondering if you could just... build something that finds them. So I did. What even is an exoplanet classifier? When a planet passes in front of a star, it blocks a tiny fraction of the star's light. Kepler spent 4 years staring at 150,000 stars looking for exactly that — those tiny dips. The result is thousands of light curves, each one a time series of a star's brightness over time. Some dips are planets. A lot aren't — instrument noise, binary stars, other stuff. NASA labels them as confirmed, false positive, or candidate. I wanted to see if a neural network could learn the difference. What I built A 1D CNN that takes a phase-folded light curve — 400 data points representing one orbit's worth of brightness — and outputs a probability: real planet or not. Ended up hitting 0.96 ROC-AUC on the test set, which honestly surprised me.…