detectorization

Detectorization is a term used (though rarely) to describe the process of reducing or eliminating the role of detectors in a system, typically replacing direct measurements with indirect indicators, models, or non-sensing proxies. It stands in contrast to instrumentation or detector-heavy configurations. Because it is not a widely standardized term, its exact meaning can vary by domain; in many cases it refers to decommissioning or downgrading sensing hardware, or to design choices that rely on software inference rather than physical detection.

Contexts and applications

- In experimental science and instrumentation, detectorization can mean phasing out large detector arrays and relying on existing data, simulations, or alternative measurement modalities.

- In industrial control or smart systems, it can refer to reducing sensor counts to simplify maintenance, lower cost, or protect privacy, while compensating with software analytics, redundancy, or system-level observability.

- In cybersecurity or anomaly detection, detectorization could describe shifting from continuous monitoring by detectors to periodic checks or inference from log data.

Key considerations

- Benefits may include lower cost, reduced maintenance, privacy preservation, and simpler data pipelines.

- Risks include reduced safety margins, data gaps, diminished resilience, and potential regulatory or certification challenges.

- Practitioners typically perform risk assessments, define minimum data requirements, implement fallback strategies, and validate performance through simulations and experiments.

See also: instrumentation, sensor networks, observability, decommissioning, data-driven monitoring.