Axiom-Zspace

Axiom-Zspace is an astrophysics signal deconvolution and noise reduction framework designed to identify transiting exoplanets in stellar data streams. Telemetry gathered by NASA's Transiting Exoplanet Survey Satellite (TESS) is heavily saturated with instrumental drift, spacecraft jitter, and stellar activity, producing high noise thresholds. Opaque machine learning algorithms attempting planet discovery generate false-alarm transit signals, confusing instrumental noise spikes with authentic orbiting worlds.

Axiom-Zspace resolves this astronomical signal bottleneck, employing a high-dimensional signal deconvolution filter to clean light curve telemetry. By modeling satellite system parameters and stellar activity profiles deterministically, the algorithm separates orbital transit signatures from physical noise. Under active operations, the engine has deconvolved stellar streams to identify over 5,800 ongoing exoplanet candidates, exposing coordinates in unmapped stellar fields.

Every transit candidate identified by Axiom-Zspace is backed by a complete orbital parameters audit file. The system logs transit depth, orbital period, transit duration, and signal-to-noise ratios, providing astrophysicists with fully traceable, verifiable candidate files. This rigorous data processing pipeline accelerates celestial signal verification, mapping new planetary systems with high mathematical certainty.