Reading the physics hiding in data
Information is encoded in data. This is true for most aspects of modern 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 being made up of 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 the Abdus Salam International Centre for Theoretical Physics (ICTP) and the Scuola Internazionale Superiore di Studi Avanzati (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.
The authors of the paper, coordinated by Marcello Dalmonte, a researcher in ICTP's Condensed Matter and Statistical Physics Section and SISSA collaborator, come from different backgrounds. Tiago Mendes, a former postdoctoral fellow at ICTP and now at the Max Planck Institute for the Physics of Complex Systems, in Dresden, Germany, works mainly in numerical methods applied to statistical mechanics. Alex Rodriguez is a chemist, previously working at SISSA and now at ICTP, who works in the implementation of complex system algorithms and the development of machine learning methods. Xhek Turkeshi, a Ph.D. student at SISSA, works mostly in statistical physics.
The researchers focussed on a generic statistical property of the data sets, called the intrinsic dimension. The simplest way to describe this property is as the minimum number of variables needed to represent a given data set, without any loss of information. "Take, for example, all the people around the world," explains Rodriguez. "That is a data set by itself. Now, if you want to specify the position of the people around the world, in theory, you would need the coordinates of all their positions in space, that is, three data for each person. But since we can approximate the Earth as a bidimensional surface, we will only need two parameters, that is, the latitude and the longitude. This is what intrinsic dimension is: if the data set was humanity then the intrinsic dimension would be 2, not 3."
In the more theoretical context of statistical systems, the paper shows that this property of intrinsic dimension can reveal collective properties of partition functions at thermal phase transitions. This means that, regardless of what system is under consideration, the data can show if and when that system is undergoing a phase transition. The team has developed a theoretical framework to explain why generic data exhibit such a 'universal' behavior, common to a broad range of different phase transitions, from melting ice to ferromagnets.
"The work introduces a new viewpoint on phase transitions by showing how the intrinsic dimension reveals correspondent structural transitions in data space," say the scientists, "when ice melts, its data structure does as well."
What is really new in this work is that raw data mirror the physical behavior of the systems under consideration, and that is important for physicists, as it allows them to analyze a system without knowing the physics underlying it. Looking at the data is enough in order to see if there is a transition happening in the system or not, without even knowing what kind of transition it is. "We could say that this method is completely agnostic," says Mendes. "You don't need to know a priori all the parameters of the system; you just work with raw data and see what comes out of them."
After the interesting results obtained in this research, the team plans to continue working together in the same direction, broadening their field of analysis. They are already working on a second paper, focussing on the so-called 'quantum phase transitions', that is, quantum systems where phase transitions happen at a temperature equal to zero and are induced by external parameters, like the magnetic field.
In terms of applications of these findings, the possibilities are many—from experiments with computer simulations of quantum systems to more fundamental branches of physics, such as quantum chromodynamics, that could also have an impact on nuclear physics. "An interesting possibility of application is in the use of statistical physics techniques to understand machine learning," says Rodriguez. "In this kind of research, that goes from quantum computing to the study of neural networks for example, phase transitions are very often involved and we could try to use our method to tackle all these kinds of different problems."
More information: T. Mendes-Santos et al, Unsupervised Learning Universal Critical Behavior via the Intrinsic Dimension, Physical Review X (2021). DOI: 10.1103/PhysRevX.11.011040
Journal information: Physical Review X
Provided by International School of Advanced Studies (SISSA)