ESA's Gaia DR3 catalogue holds close to 1.8 billion observed objects. A small number are genuine candidates for follow-up; the rest is noise and known sources. Set one threshold wrong and you drown in false positives; set it too tight and you throw away the discoveries. This is the hardest version of the signal-in-noise problem that exists.
Instead of trusting a single model, HGE generated a large field of competing, falsifiable hypotheses and tested every one against the full dataset. Candidates that failed, fell. What survived was quantified and ranked.
220,656 candidates surfaced and ranked reproducibly, each one traceable back to the raw catalogue, independently evaluable in an ESA context. The same question returns the same answer, every run.
Finding the one real cause of a process deviation among millions of tag values is the same class of problem at smaller scale. If the method holds at 1.8 billion objects, your historian is not going to frighten it.
Start with a baseline study.
A fixed-fee, fixed-duration engagement with a defined deliverable. Bring one concrete problem in your operations or your data, and leave with a result you can act on.
Request a baseline study →