Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.
Doing physics and being a physicist is shaped by complex social factors. This month, we launch a Collection to explore the social and historical context of physics research.
Pietro Barabaschi, Director General of ITER, calls for measures and incentives to carefully document the entire research process, including dead ends and failures, instead of reporting just the successful final results.
Science and society are inextricably entangled, but the discussion of social issues in optics and photonics is, at best, treated as peripheral to the field. A group of researchers, technicians, administrative staff, and clinical liaisons share how they came together to start a conversation recognizing these oft-disregarded issues.
In 1931, the psychoanalyst Carl Jung took on an unusual patient, the brilliant young physicist, Wolfgang Pauli. Arthur I. Miller tells the story of their friendship, how they impacted each other’s work, and reflects on creativity.
In 2023, pulsar timing arrays announced what could become the first ever discovery of a stochastic gravitational wave background: the random superposition of gravitational waves permeating the cosmos — a vestige of cosmic processes in the Universe.
The standard model of particle physics describes the fundamental constituents of matter and their interactions. We review the status of experimental hints for new physics, which, if confirmed, would require the extension of the standard model with new particles and new interactions.
Machine learning techniques may appear ill-suited for application in fields that prioritize rigor and deep understanding; however, they have recently found unexpected uses in theoretical physics and pure mathematics. In this Perspective, Gukov, Halverson and Ruehle have discussed rigorous applications of machine learning to theoretical physics and pure mathematics.
Neural operators learn mappings between functions on continuous domains, such as spatiotemporal processes and partial differential equations, offering a fast, data-driven surrogate model solution for otherwise intractable numerical simulations of complex real-world problems.
Quantum sensing exploits properties of quantum systems to go beyond what is possible with traditional measurement techniques, hence opening exciting opportunities in both low-energy and high-energy particle physics experiments.