Machine learning (ML), a form of artificial intelligence that recognizes faces, understands language and navigates self-driving cars, can help bring to Earth the clean fusion energy that lights the sun and stars. Researchers at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) are using ML to create a model for rapid control of plasma—the state of matter composed of free electrons and atomic nuclei, or ions—that fuels fusion reactions.
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Electronic properties of materials can be directly influenced via light absorption in under a femtosecond (10-15 seconds), which is regarded as the limit of the maximum achievable speed of electronic circuits. In contrast, the magnetic moment of matter has only been able to be influenced up to now by a light and magnetism-linked process and roundabout way by means of magnetic fields, which is why magnetic switching takes that much longer and at least several hundred femtoseconds.
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Scientists at DESY have achieved a new world record for an experimental type of miniature particle accelerator: For the first time, a terahertz powered accelerator more than doubled the energy of the injected electrons. At the same time, the setup significantly improved the electron beam quality compared to earlier experiments with the technique, as Dongfang Zhang and his colleagues from the Center for Free-Electron Laser Science (CFEL) at DESY report in the journal Optica. "We have achieved the best beam parameters yet for terahertz accelerators," said Zhang.
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Recent advances in the observation of high-energy radiations, including X-rays and gamma-rays, have unveiled many high-energy aspects of the universe. To achieve a complete understanding of these radiations, however, researchers need to find out more about the high-energy particles (i.e. cosmic rays) that produce them. In fact, non-thermal radiations characterized by the power-law spectrum are all backed by the acceleration and propagation of these rays.
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Researchers at Tokyo Institute of Technology have found a simple, yet highly versatile way to generate "chaotic signals" with various features. The technique consists of interconnecting three ring oscillators, effectively making them compete against each other, while controlling their respective strengths and their linkages. The resulting device is rather small and efficient, thus suitable for emerging applications such as realizing wireless networks of sensors.
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Dark matter is an unknown type of matter present in the universe that could be of particle origin. One of the most complete theoretical frameworks that includes a dark matter candidate is supersymmetry. Many supersymmetric models predict the existence of a new stable, invisible particle called the lightest supersymmetric particle (LSP), which has the right properties to be a dark matter particle.
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