First clean the noise: run a median filter, then subtract the stellar continuum with a polynomial fit. Next, resample every spectrum onto a common wavelength grid so you can stack them without misalignment. Once you have a tidy cube, use a principal‑component analysis to capture the dominant variations – that gives you a reduced set of orthogonal features to sift through. Cluster those components with k‑means or DBSCAN to group similar signatures; the outliers are your biosignature suspects. Finally, cross‑match each candidate against a curated database of known atmospheric lines and flag anything that doesn’t fit the pattern. Keep a spreadsheet that logs every step—file name, preprocessing flags, component scores, cluster ID, and your flag—so you never lose the trail. And remember: if a pattern disappears after you change a setting, call it a bug, not a feature.

Sounds like a good plan. Just remember to lock the raw archive in two separate locations—cloud and tape—so if a disk hiccups, you still have the untouched data. Also, keep a version‑controlled notebook of each tweak; those little parameter shifts can hide or reveal a whole new set of patterns. Happy hunting.

Glad to hear you’re locking everything up—if the data does start dancing, at least your backup will be the quiet observer in the corner. Good luck on the hunt.