Information is encoded in data. This is true for most aspects of modern everyday life, but it is also true in most branches of contemporary physics, and extracting useful and meaningful information from very large data sets is a key mission for many physicists.
In statistical mechanics, large data sets are daily business. A classic example is the partition function, a complex mathematical object that describes physical systems at equilibrium. This mathematical object can be seen as made up by many points, each describing a degree of freedom of a physical system, that is, the minimum number of data that can describe all of its properties.
An interdisciplinary team of scientists from ICTP and SISSA showed that such a massive collection of data can be combed through, bringing out fundamental physical properties of an unknown system.
These results were highlighted in a paper just published in Physical Review X, introducing a new data-based viewpoint on phase transitions. The team showed that a generic statistical property of large data sets that describe a broad range of physical systems at equilibrium, known as intrinsic dimension, can in fact reveal the occurrence of a phase transition.
(Image by Mendes-Santos, Turkeshi, Dalmonte and Rodriguez)