Raw data and Python code for the manuscript "Emergent coherent modes in nonlinear magnonic waveguides detected at ultrahigh frequency resolution".  All data files and source code are provided in a zip file. .ipynb file is a python jupyter notebook file which contains all the source codes to generate all the figures.

Abstract: Nonlinearity of dynamic systems plays a key role for neuromorphic computing which is expected to reduce the ever-increasing power consumption of machine learning and artifical intelligence applications. For spin waves (magnons), nonlinearity combined with phase coherence is the basis of phenomena like Bose-Einstein condensation, frequency combs, and pattern recognition in neuromorphic computing. Yet, the broadband electrical detection of these phenomena with high frequency resolution remains a challenge. Here, we demonstrate the generation and detection of phase-coherent nonlinear magnons in an all-electrical GHz probe station based on coplanar waveguides connected to a vector network analyzer which we operate in a frequency-offset mode. Making use of an unprecedented frequency resolution, we resolve the nonlocal emergence of a fine structure of propagating nonlinear magnons, which sensitively depends on both power and a magnetic field. These magnons are shown to maintain coherency with the microwave source while propagating over macroscopic distances. We propose a multi-band four magnon scattering scheme which is in agreement with the field-dependent characteristics of coherent nonlocal signals in the nonlinear excitation regime. Our findings are key to enable the seamless integration of nonlinear magnon processes into high-speed microwave electronics and to advance phase-encoded information processing in magnonic neuronal networks.