Non-Abelian gauge fields provide a conceptual framework to describe particles having spins, underlying many phenomena in electrodynamics, condensed-matter physics and particle physics. Lattice models of non-Abelian gauge fields allow us to understand their physical implications in extended systems. The theoretical importance of non-Abelian lattice gauge fields motivates their experimental synthesis and explorations. Photons are fundamental particles for which artificial gauge fields can be synthesized, yet the demonstration of non-Abelian lattice gauge fields for photons has not been achieved. Here we demonstrate SU(2) lattice gauge fields for photons in the synthetic frequency dimensions, a playground to study lattice physics in a scalable and programmable way. In our lattice model, we theoretically observe that homogeneous non-Abelian lattice gauge potentials induce Dirac cones at time-reversal-invariant momenta in the Brillouin zone. We experimentally confirm the presence of non-Abelian lattice gauge fields by two signatures: linear band crossings at the Dirac cones, and the associated direction reversal of eigenstate trajectories. We further demonstrate a non-Abelian scalar lattice gauge potential that lifts the degeneracies of the Dirac cones. Our results highlight the implications of non-Abelian lattice gauge fields in topological physics, and provide a starting point for demonstrations of emerging non-Abelian physics in the photonic synthetic dimensions. Our results may also benefit photonic technologies by providing controls of photon spins and pseudo-spins in topologically non-trivial ways.
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http://dx.doi.org/10.1038/s41586-024-08259-2 | DOI Listing |
Phys Rev Lett
December 2024
Joint Center for Quantum Information and Computer Science, NIST and University of Maryland, College Park, Maryland 20742, USA.
A key objective in nuclear and high-energy physics is to describe nonequilibrium dynamics of matter, e.g., in the early Universe and in particle colliders, starting from the standard model of particle physics.
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December 2024
Physics Division, Argonne National Laboratory, Lemont, Illinois 60439, USA.
The transverse-momentum-dependent distributions (TMDs), which are defined by gauge-invariant 3D parton correlators with staple-shaped lightlike Wilson lines, can be calculated from quark and gluon correlators fixed in the Coulomb gauge on a Euclidean lattice. These quantities can be expressed gauge invariantly as the correlators of Coulomb-gauge-dressed fields, which reduce to the standard TMD correlators under principal-value prescription in the infinite boost limit. In the framework of large-momentum effective theory, a quasi-TMD defined from such correlators in a large-momentum hadron state can be matched to the TMD via a factorization formula, whose exact form is derived using soft collinear effective theory and verified at one-loop order.
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January 2025
School of Economics and Management, Shandong Agricultural University, Taian, China.
Agriculture is a major contributor to global greenhouse gas emissions, highlighting the urgent need for effective carbon reduction strategies. This study presents an innovative integrated model that employs Fermatean Neutrosophic Set in conjunction with the Weighted Influence Nonlinear Gauge System and the Analytic Hierarchy Process combined with the Entropy Weight Method to assess key factors influencing agricultural carbon reduction. Our study delineates the hierarchical importance of factors influencing carbon emissions, with carbon emission reduction policy (τ4) emerging as the paramount factor, attributed a value of 0.
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January 2025
Department of Behavioral and Cognitive Biology, University of Vienna, Djerassiplatz 1, 1030, Vienna, Austria.
Object play has been proposed to provide individuals with information about their environment, facilitating foraging skills and tool use. In species where object play co-occurs with locomotor or social play, it may have additional functional implications, such as facilitating the evaluation of peers or forming social bonds. For instance, ravens judge others' competitiveness via play caching and engage in social play by exchanging objects.
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January 2025
Department of Computer Science, Sri Guru Gobind Singh College of Commerce, University of Delhi, Delhi, India.
Domain-specific vocabulary, which is crucial in fields such as Information Retrieval and Natural Language Processing, requires continuous updates to remain effective. Incremental Learning, unlike conventional methods, updates existing knowledge without retraining from scratch. This paper presents an incremental learning algorithm for updating domain-specific vocabularies.
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