arXiv subjects

Zhengzheng Liu

Publications and source records attributed to Zhengzheng Liu.

3 recordsLinked to original sources

arXiv2026

Enhancement of vacuum-ultraviolet dispersive-wave emission using gas-filled tapered hollow-core fibers

The recent breakthroughs in laser-driving 229Th nuclear transition have created an urgent demand for coherent vacuum-ultraviolet (VUV) sources delivering high spectral brightness at the critical 148.38 nm isomer energy. However, generating sufficient photon flux to overcome the low nuclear excitation probability remains a challenge for compact setups. While resonant dispersive wave emission in gas-filled hollow-core fibers offers a promising route, standard capillaries face a fundamental trade-off: maximizing input coupling requires large core diameters, whereas efficient nonlinear VUV conversion demands the high intensities using small cores. Here, we resolve this conflict using a gas-filled tapered capillary fiber. This architecture utilizes a longitudinally decreasing core diameter to combine a large input aperture with adiabatic field concentration, thereby continuously enhancing the nonlinear interaction. Experimentally, we demonstrate a widely tunable source (135-240 nm) that achieves a twofold efficiency enhancement specifically at the 148.38 nm wavelength compared to uniform geometries. By providing a scalable route to high-flux VUV generation, this work establishes a critical tabletop tool for advancing solid-state nuclear clocks and time-resolved spectroscopy.

physics.optics↗

arXiv2024

Octave-wide broadening of ultraviolet dispersive wave driven by soliton-splitting dynamics

Coherent dispersive wave emission, as an important phenomenon of soliton dynamics, manifests itself in multiple platforms of nonlinear optics from fibre waveguides to integrated photonics. Limited by its resonance nature, efficient generation of coherent dispersive wave with ultra-broad bandwidth has, however, proved difficult to realize. Here, we unveil a new regime of soliton dynamics in which the dispersive wave emission process strongly couples with the splitting dynamics of the driving pulse. High-order dispersion and self-steepening effects, accumulated over soliton self-compression, break the system symmetry, giving rise to high-efficiency generation of coherent dispersive wave in the ultraviolet region. Simultaneously, asymmetric soliton splitting results in the appearance of a temporally-delayed ultrashort pulse with high intensity, overlapping and copropagating with the dispersive wave pulse. Intense cross-phase modulations lead to octave-wide broadening of the dispersive wave spectrum, covering 200 to 400 nm wavelengths. The highly-coherent, octave-wide ultraviolet spectrum, generated from the simple capillary fibre set-up, is in great demand for time-resolved spectroscopy, ultrafast electron microscopy and frequency metrology applications, and the critical role of the secondary pulse in this process reveals some new opportunities for all-optical control of versatile soliton dynamics.

physics.optics↗

arXiv2023

L2T-DLN: Learning to Teach with Dynamic Loss Network

With the concept of teaching being introduced to the machine learning community, a teacher model start using dynamic loss functions to teach the training of a student model. The dynamic intends to set adaptive loss functions to different phases of student model learning. In existing works, the teacher model 1) merely determines the loss function based on the present states of the student model, i.e., disregards the experience of the teacher; 2) only utilizes the states of the student model, e.g., training iteration number and loss/accuracy from training/validation sets, while ignoring the states of the loss function. In this paper, we first formulate the loss adjustment as a temporal task by designing a teacher model with memory units, and, therefore, enables the student learning to be guided by the experience of the teacher model. Then, with a dynamic loss network, we can additionally use the states of the loss to assist the teacher learning in enhancing the interactions between the teacher and the student model. Extensive experiments demonstrate our approach can enhance student learning and improve the performance of various deep models on real-world tasks, including classification, objective detection, and semantic segmentation scenarios.

cs.AI↗