Hidden 'funnels' let complex systems slip between stable states

Many systems in nature can settle into several different stable states, with the final state depending on their starting conditions. However, the boundaries separating these states are often far more complicated than they first appear. The science-journalism coverage adds useful context, while the strongest evidential footing still comes from the underlying data, papers or institutional documentation.

That matters because physics only takes a result seriously when the measurement chain remains robust under scrutiny. Experimental particle physics and precision metrology both operate in regimes where the signal sits far below the background noise, and where systematic uncertainties can mimic new physics if not controlled rigorously. The history of the field contains numerous anomalies that generated theoretical excitement before better data showed them to be artifacts, and it also contains genuine discoveries that were initially dismissed as noise. The difference is almost always resolved by independent replication with different instruments and different systematics. This article has been reviewed according to Science X's editorial process and policies. However, the boundaries separating these states are often far more complicated than they first appear.

Through new research published in Physical Review Letters, researchers in Ireland and Germany, led by Serhiy Yanchuk at University College Cork, have shown that these boundaries. From signals in the brain to patterns in Earth's climate, many complex systems in nature can exist in more than one stable state, a property known as "multistability.

This raises important questions about resilience: If a disturbance knocks a system away from its current state, will it return or tip into a different one entirely. In Earth's climate, for example, fast-changing weather is coupled to far slower changes in oceans and ice sheets.

Drawing on this idea, Yanchuk's team developed new models of multistable systems containing both fast and slow processes. Yanchuk's team found these funnels in several different systems, from the simplest possible model with two competing states to complex networks of up to 10 linked oscillators.

The broader interest lies as much in the method as in the headline number, because a durable measurement procedure can travel farther than a single result. When experimental physicists develop a technique that achieves new sensitivity or controls a previously uncharacterized systematic, that methodological contribution persists even if the specific measurement is later revised. This is one reason why precision physics experiments often generate long-term value that is not immediately visible in the original publication.

Ultimately, the findings offer a warning against drawing conclusions about resilience or "tipping points" from simplified models alone, including those used to assess the possible. We rely on readers like you to keep independent science journalism alive.

Because this item comes through Phys. org Physics as science journalism, it should be treated as contextual reporting rather than primary evidence. Good science reporting can identify why a result matters, connect it to the wider literature and make technical work readable, but the decisive evidence remains in the original paper, dataset, mission release or technical record. That distinction is especially important when a story is later repeated by aggregators, because repetition increases visibility, not evidential strength.

The next step is more measurement, tighter systematic control and scrutiny from groups whose experimental setups are genuinely independent. In experimental particle physics and precision metrology, the threshold for a discovery claim is a five-sigma excess surviving multiple analyses; an intriguing signal at lower significance is a reason to run more experiments, not a reason to revise the textbooks. Next-generation experiments currently under construction or commissioning will revisit several of the open questions that give the current result its context.

Source