Researchers have developed a new mathematical formalism to visualize how electromagnetic waves collect information from objects they interact with as they travel. This property is widely exploited in various applications that rely on wave deflection, scattering, or reflection.
Physicist Sara Imari Walker is using principles of physics to redefine the concept of life. She introduces Assembly Theory, which measures molecular complexity to distinguish living from non-living systems. This approach could help detect unfamiliar life forms on other planets and better understand life on Earth.
A new model explains the small Higgs mass and the strong-CP problem by invoking a multiverse scenario. It predicts distinctive signals at hadronic EDM, fuzzy dark matter, and axion experiments.
The article discusses a phenomenon known as the "Dynamic Quantum Cheshire Cat Effect", which is a type of quantum effect that allows physical properties to be separated from the objects to which they belong. The authors show that this effect can be generalized to dynamical settings, where the property that is separated from the particle can propagate in space and lead to a flux of conserved quantity.
This article presents a white paper summarizing current knowledge on quantum gravity phenomenology and its multi-messenger signals. It provides an overview of the field, discusses experimental and observational signatures, and identifies key questions and challenges.
Astrophysicists have detected subtle hints that the mysterious 'dark energy' that drives the universe's expansion may be slightly weakening over time.
Researchers led by Nigel Goldenfeld and Björn Hof offer new insights into the transition from laminar to turbulent water flow, solving a 150-year-old mystery. The interdisciplinary team applied statistical mechanics to reveal that the phenomenon behaves like directed percolation.
The paper proposes the "law of increasing functional information," a new law of nature that could help explain the evolution of complex systems across multiple scales in the universe, from atoms and molecules to stars and brains.
These systems are characterized by three attributes: they form from numerous components, processes generate numerous configurations, and configurations are preferentially selected based on function.
The law suggests that functional information of a system will increase over time when subjected to selection for function(s). The authors argue this law could help predict the behavior of these systems and provide a unified framework for understanding their evolution.
They suggest it could be a missing piece in our understanding of the universe.
These systems are characterized by three attributes: they form from numerous components, processes generate numerous configurations, and configurations are preferentially selected based on function.
The law suggests that functional information of a system will increase over time when subjected to selection for function(s). The authors argue this law could help predict the behavior of these systems and provide a unified framework for understanding their evolution.
They suggest it could be a missing piece in our understanding of the universe.
This article discusses the implementation of a procedure for defining time using entangled systems according to the Page and Wootters approach. The authors study how quantum dynamics transform into classical-like behavior and analyze the relations that must hold between quantities of the system and the clock for the overall picture to represent physical dynamics.
The article discusses how machine learning is being used to calculate the macroscopic world that would emerge from string theory, a theory that posits the existence of tiny, invisible extra dimensions. These calculations have been difficult due to the enormous number of possibilities, but recent advances in artificial intelligence have made it possible to approximate the shapes of the Calabi-Yau manifolds, the objects that resemble loofahs and host quantum fields in string theory. The calculations have been able to reproduce the number of particles in the standard model, but not their specific masses or interactions. The long-term goal is to use these calculations to predict new physical phenomena beyond the standard model. The article also mentions that some physicists are skeptical of the usefulness of string theory and the role that machine learning will play in it.
