Brihat Sharma

When planets tug on each other, their transits arrive early or late. That timing signal encodes their masses, even for planets that never transit. This is what I learned trying to invert it with a neural network: the model stays honest where everyone feared it would lie, and the real wall is the data, not the algorithm.

A follow-up experiment. In the last post I argued that the wide, near-resonant posteriors in transit-timing inference are physical, a limit of the data and not the model, so the lever is more observables rather than a fancier network. Here I add transit durations to the forward model and watch the mass-eccentricity degeneracy collapse.

Aggregate success rate tells you an agent failed. It will not tell you that two agents with the same score fail in completely different ways. A short look at why per-axis failure profiles are the more useful number.