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This Perspective argues that the development of 99Mo production methods complementary to reactor-based methodology is strategic in the short-to-medium term. Localized and resilient 99Mo production routes might guarantee access to important diagnostic procedures even in the case of unpredictable global events.
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.
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.
Generative machine learning models seek to approximate and then sample the probability distribution of the data sets on which they are trained. This Perspective article connects these methods to historical studies of information processing and attractor geometry in nonlinear systems.
Understanding the W boson as accurately as possible, including knowing its mass, has been a priority in particle physics for decades. This Perspective article gives an overview of the role of the W boson mass in the Standard Model and its extensions and compares techniques for measuring it.
