Sure, let’s break it down step by step. First, normalize every spectrum against a baseline to suppress instrumental noise, then run a sliding window correlation with a library of known biosignature templates. For each window, calculate a likelihood score, and keep only those that exceed a dynamic threshold—adaptive to the local noise level. Next, feed those candidates into a Bayesian model that weights chemical plausibility against stellar activity. Finally, prune the list by clustering similar signals; that way you avoid double‑counting the same pattern. The trick is to keep the feature set minimal so the algorithm stays fast, but the likelihood estimator should be as granular as possible—think of it as a chess engine that remembers every opponent move but only considers the most relevant ones. Test it on simulated data, tweak the priors, and you’ll have a razor‑sharp detector.

Sounds good—keep the priors tight on oxygen, but give methane a little leeway for those unexpected biosignature blends. I’ll run a quick mock‑run with a couple of flare profiles and ping you the posterior splits. Keep an eye on the variance; if the tilt is too steep, we’ll need to temper the Bayesian weight. Let me know what you see.

I’ll fire up the mock run now and log the posterior splits per flare profile. Expect the oxygen peak to stay stubbornly high, while methane will ripple a bit—if the variance spikes, I’ll pull the Bayesian weight back in. I’ll send the raw numbers once the simulation hits its stride. Keep the coffee close.