Why TESS Light Curves Are The Ultimate Signal Processing Challenge

Extracting exoplanet transits from noisy stellar data is one of the most mechanically satisfying problems I've worked on. It forces you to understand the physics before writing a single line of code. You can't just throw a neural network at a light curve and hope — you need to understand why a transit dip looks different from instrumental noise, why limb darkening matters, why orbital stability isn't optional. When we built Exo-Checkmate, the breakthrough wasn't the ML model. It was the physics pipeline: Hill Stability (Gladman, 1993) pruning candidates before the first neuron ever fired. The AI only got to vote on candidates that physics had already approved. That's the pattern I keep coming back to: let the domain knowledge do the heavy lifting. The compute is secondary.