Do major world events cluster under specific solar and geomagnetic conditions, and if so, can those conditions be tracked before the event occurs?
That is the question Chaerus is built on.
Chaerus begins from a simple premise drawn from nonlinear systems: some systems may resist precise prediction while still showing structure. A system does not have to be random simply because its exact path cannot be forecast. It may move through patterns, thresholds, feedback states, and boundaries that can be observed over time.
Some world events show an association with certain periods of geomagnetic quietude and disturbed solar conditions, varying by event type.
The same signature appears across multiple independently tested event categories. The strength varies by category. The research documents a statistical association. No causal mechanism has been established.
Space weather is rarely considered when people think about world events or human systems. Research outside this project has reported associations between solar and geomagnetic conditions and biological systems, with effects varying by location and season.
Consider how a major weather system develops. No single factor creates it. Sea surface temperatures have been elevated for weeks. Atmospheric pressure gradients have been building. A front stalls in a particular position. Humidity accumulates. None of these factors alone produces the storm. But when they stack within the right window, a relatively small change is enough to trigger a rapid and large-scale transition. A meteorologist cannot say exactly when that transition will occur or what form it will take. But they can observe the accumulated conditions and recognize that the system has entered a state that has preceded major transitions before.
That is not prediction. That is pattern recognition applied to a complex environment under observable stress.
Chaerus tests whether similar threshold dynamics may be observable in social, political, and geophysical systems. A system already under multiple stressors may not require a large trigger to cross a threshold. What matters is the stacking of conditions and the timing. A country under economic strain, drought, political instability, and biological load is not in the same risk state as a stable country facing one of those factors in isolation.
Chaerus is not a prediction engine. Chaerus identifies a recurring field condition associated with major events and makes that condition visible in near real time.
That is risk intelligence, not prediction.
The research below includes two 2026 preprints with plain-language summaries and two foundational studies currently presented without separate public summaries.
Foundational study
A matched-control analysis of 463 major events across 36 geomagnetic features, identifying a systematic event-day quietude signature.
Research areas: Social Physics · Environmental Data Analysis
Cross-category replication
An independent cross-category test of the quietude signature across nine categories of major world events.
Research areas: Social Physics · Environmental Data Analysis
2026 · Preprint
Two space-weather signals were replicated across two independent time windows in a global inventory of referendum events spanning 2010 to 2026. The same signals persisted specifically in referendums but were not observed in treaty or coup comparison sets tested under identical conditions. Effect sizes are small. The subtype concentration and cross-window persistence are what make the finding worth reporting.
2026 · Preprint
Broad-spectrum geomagnetic suppression in the 10 days before major earthquakes (M ≥ 5) was statistically enriched relative to 26,250 matched control windows across seven years. The enrichment held across four structurally distinct control designs, all seven leave-one-year-out configurations, and after duplicate dates were removed. A secondary exploratory finding identified a candidate IMF variability transition on earthquake day itself.
All findings are independent, unfunded, and not affiliated with any institution. Data sources: NASA OMNI-2, NOAA SWPC, NOAA NCEI, SILSO, and the University of Oulu Neutron Monitor.