Cessation Signature Search: A Statistical Framework for Detecting Technosignature Loss in Multi-Epoch Survey Archives

I propose a methodological reorientation of technosignature search programmes. The standard SETI paradigm monitors for the emergence of anomalous signals against background. I argue that the complementary question—has the composite background itself changed in ways inconsistent with known astrophysical processes?—is not the explicit target of any systematic programme: existing multi-epoch surveys are generally optimised for resolved-source variability rather than temporal stability of the unresolved composite residual—the aggregate flux remaining after subtraction of catalogued point sources and best-available diffuse emission models. Exploring it constitutes an independent search channel whose sensitivity properties are distinct from resolved-source emergence detection. I term this approach the Cessation Signature Search (CeSS). For the purposes of public communication, the same programme is also designated Negative SETI, or N-SETI. CeSS’s core proposition is that technosignatures may be more detectable as perturbations in statistical fields than as discrete signals—and that the perturbations caused by their cessation have not been searched for. Its primary object is the detection of statistically significant changes in the aggregate signal background, especially in the unresolved composite residual, while treating discrete source disappearance as the more tractable end of the same detection spectrum. Relevant archival data already exists to support initial CeSS pilot studies: decades of radio and infrared survey data whose composite statistical properties have never been characterised for temporal stability. Signal loss or transformation may have various causes, each implying a different class of historical pattern in the signal record, though some are only partially discriminating and may overlap observationally. I describe the observational and statistical framework, define the detection spectrum from composite deviation to discrete dropout, identify natural sterilising events as a control channel, and state the falsifiability conditions. Portions of this work were generated with the assistance of artificial intelligence systems, including models from GPT, Claude, and Gemini; full details are provided in the AI Use and Disclosure Statement.