arXiv subjects

Chenran Xu

Publications and source records attributed to Chenran Xu.

12 recordsLinked to original sources

arXiv2026

Five-Dimensional Beam Sigma Matrix Determination in Transport Lines with Differentiable Simulation

Precise measurement of the beam sigma matrix is essential for matching the optics in transport lines and ensuring reliable accelerator operation. In this work, we present a method for measuring and reconstructing the non-temporal five-dimensional beam sigma matrix using quadrupole scans performed in a dispersive transport region. The proposed approach enables characterization of the beam moments using only quadrupoles and beam transverse profile diagnostics, without requiring longitudinal diagnostics or a dedicated beamline section. To achieve robust and computationally efficient reconstruction, we formulate the problem within a differentiable simulation framework, allowing direct gradient-based optimization of the initial beam covariance matrix. We demonstrate the method experimentally in the Booster-to-Storage-ring (BTS) transport line at the Advanced Photon Source (APS), where it produces consistent reconstructions of the beam sigma matrix from measurements. We further show that the framework is flexible with respect to the number and placement of diagnostic screens, making it applicable to a broad range of existing transport-line configurations. These results establish the proposed 5D beam sigma matrix reconstruction method as a practical and broadly deployable approach for fast, efficient beam characterization during accelerator operation.

physics.acc-ph↗

arXiv2026

The Memory Scaling of Reverse-Mode Differentiation in Particle Accelerator Simulations with Space Charge

The recent development of differentiable simulation codes for particle accelerators has enabled gradient-based workflows that promise finer control and more realistic modeling of accelerator facilities. However, when using reverse-mode automatic differentiation, the memory usage continuously increases during the simulation, and can potentially exceed the available hardware memory -- especially when costly space charge computation is included. To study the memory requirements for differentiable simulations, we have implemented space charge in Cheetah, a PyTorch-based beam tracking code that supports reverse-mode differentiation. We find that the memory usage for reverse-mode differentiation grows linearly with the number of macroparticles and cells, and that it is proportional to the number of space charge kicks involved in the simulation. This general scaling can be used to evaluate whether a given differentiable simulation is feasible given hardware memory constraints.

physics.acc-ph↗

arXiv2026

Quantum Squeezing Enhanced Photothermal Microscopy

Label-free optical microscopy through absorption or scattering spectroscopy provides fundamental insights across biology and materials science, yet its sensitivity remains fundamentally limited by photon shot noise. While recent demonstrations of quantum nonlinear microscopy show sub-shot-limited sensitivity, they are intrinsically limited by availability of high peak-power squeezed light sources. Here, we introduce squeezing-enhanced photothermal (SEPT) microscopy, a quantum imaging technique that leverages twin-beam quantum correlations to detect absorption induced signals with unprecedented sensitivity. SEPT achieves 3.5 dB noise suppression beyond the standard quantum limit, enabling a 2.5-fold increase in imaging throughput or 31% reduction in pump power, while providing an unmatched versatility through the intrinsic compatibility between continuous-wave squeezing and photothermal modulation. We showcase SEPT applications by providing high-precision characterization of nanoparticles and revealing subcellular structures, such as cytochrome c, that remain undetectable under shot-noise-limited imaging. By combining label-free contrast, quantum-enhanced sensitivity, and compatibility with existing microscopy platforms, SEPT establishes a new paradigm for molecular absorption imaging with far-reaching implications in cellular biology, nanoscience, and materials characterization.

physics.optics↗

arXiv2025

Gate-tunable hot electron extraction in a two-dimensional semiconductor heterojunction

Hot carrier solar cells (HCSCs), harvesting excess energy of the hot carriers generated by above-band-gap photoexcitation, is crucial for pushing the solar cell efficiency beyond the Shockley Queisser limit, which is challenging to realize mainly due to fast hot-carrier cooling. By performing transient reflectance spectroscopy in a MoSe2/hBN/WS2 junction, we demonstrate the gate-tunable harvest of hot electrons from MoSe2 to WS2. By spectrally distinguishing hot-electron extraction from lattice temperature increase, we find that electrostatically doped electrons in MoSe2 can boost hot-electron extraction density (n_ET) by factor up to several tens. Such enhancement arises from interaction between hot excitons and doped electrons, which converts the excess energy of hot excitons to excitations of the Fermi sea and hence generates hot electrons. Moreover, n_ET can be further enhanced by reducing the conduction band offset with external electric field. Our results provide in-depth insights into design of HCSCs with electrostatic strategies.

cond-mat.mes-hall↗

arXiv2024

Microsecond-Latency Feedback at a Particle Accelerator by Online Reinforcement Learning on Hardware

The commissioning and operation of future large-scale scientific experiments will challenge current tuning and control methods. Reinforcement learning (RL) algorithms are a promising solution thanks to their capability of autonomously tackling a control problem based on a task parameterized by a reward function. The conventionally utilized machine learning (ML) libraries are not intended for microsecond latency applications, as they mostly optimize for throughput performance. On the other hand, most of the programmable logic implementations are meant for computation acceleration, not being intended to work in a real-time environment. To overcome these limitations of current implementations, RL needs to be deployed on-the-edge, i.e. on to the device gathering the training data. In this paper we present the design and deployment of an experience accumulator system in a particle accelerator. In this system deep-RL algorithms run using hardware acceleration and act within a few microseconds, enabling the use of RL for control of ultra-fast phenomena. The training is performed offline to reduce the number of operations carried out on the acceleration hardware. The proposed architecture was tested in real experimental conditions at the Karlsruhe research accelerator (KARA), serving also as a synchrotron light source, where the system was used to control induced horizontal betatron oscillations in real-time. The results showed a performance comparable to the commercial feedback system available at the accelerator, proving the viability and potential of this approach. Due to the self-learning and reconfiguration capability of this implementation, its seamless application to other control problems is possible. Applications range from particle accelerators to large-scale research and industrial facilities.

physics.acc-ph↗

arXiv2024

Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations

Machine learning has emerged as a powerful solution to the modern challenges in accelerator physics. However, the limited availability of beam time, the computational cost of simulations, and the high-dimensionality of optimisation problems pose significant challenges in generating the required data for training state-of-the-art machine learning models. In this work, we introduce Cheetah, a PyTorch-based high-speed differentiable linear-beam dynamics code. Cheetah enables the fast collection of large data sets by reducing computation times by multiple orders of magnitude and facilitates efficient gradient-based optimisation for accelerator tuning and system identification. This positions Cheetah as a user-friendly, readily extensible tool that integrates seamlessly with widely adopted machine learning tools. We showcase the utility of Cheetah through five examples, including reinforcement learning training, gradient-based beamline tuning, gradient-based system identification, physics-informed Bayesian optimisation priors, and modular neural network surrogate modelling of space charge effects. The use of such a high-speed differentiable simulation code will simplify the development of machine learning-based methods for particle accelerators and fast-track their integration into everyday operations of accelerator facilities.

physics.acc-ph↗

arXiv2023

Bayesian Optimization Algorithms for Accelerator Physics

Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques towards solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design.

physics.acc-ph↗

arXiv2023

Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning

Online tuning of real-world plants is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods, such as Reinforcement Learning-trained Optimisation (RLO) and Bayesian optimisation (BO), hold great promise for achieving outstanding plant performance and reducing tuning times. Which algorithm to choose in different scenarios, however, remains an open question. Here we present a comparative study using a routine task in a real particle accelerator as an example, showing that RLO generally outperforms BO, but is not always the best choice. Based on the study's results, we provide a clear set of criteria to guide the choice of algorithm for a given tuning task. These can ease the adoption of learning-based autonomous tuning solutions to the operation of complex real-world plants, ultimately improving the availability and pushing the limits of operability of these facilities, thereby enabling scientific and engineering advancements.

cs.LG↗

arXiv2022

Quantum Induced Coherence Light Detection and Ranging

Quantum illumination has been proposed and demonstrated to improve the signal-to-noise ratio (SNR) in light detection and ranging (LiDAR). When relying on coincidence detection, such a quantum LiDAR is limited by the response time of the detector and suffers from jamming noise. Inspired by the Zou-Wang-Mandel experiment, we design, construct and validate a quantum induced coherence (QuIC) LiDAR which is inherently immune to ambient and jamming noises. In traditional LiDAR the direct detection of the reflected probe photons suffers from deteriorating SNR for increasing background noise. In QuIC LiDAR we circumvent this obstacle by only detecting the entangled reference photons, whose single-photon interference fringes are used to obtain the distance of the object, while the reflected probe photons are used to erase path information of the reference photons. In consequence, the noise accompanying the reflected probe light has no effect on the detected signal. We demonstrate such noise resilience with both LED and laser light to mimic the background noise and jamming attack. The proposed method paves a new way of battling noise in precise quantum electromagnetic sensing and ranging.

quant-ph↗

arXiv2022

Bayesian Optimization of the Beam Injection Process into a Storage Ring

We have evaluated the data-efficient Bayesian optimization method for the specific task of injection tuning in a circular accelerator. In this paper, we describe the implementation of this method at the Karlsruhe Research Accelerator with up to nine tuning parameters, including the determination of the associated hyperparameters. We show that the Bayesian optimization method outperforms manual tuning and the commonly used Nelder-Mead optimization algorithm both in simulation and experiment. The algorithm was also successfully used to ease the commissioning phase after the installation of new injection magnets and is regularly used during accelerator operations. We demonstrate that the introduction of context variables that include intra-bunch scattering effects, such as the Touschek effect, further improves the control and robustness of the injection process.

physics.acc-ph↗

arXiv2022

Measuring Zak phase in room-temperature atoms

Cold atoms provide a flexible platform for synthesizing and characterizing topolog-ical matter, where geometric phases play a central role. However, cold atoms are intrinsically prone to thermal noise, which can overwhelm the topological response and hamper promised applications. On the other hand, geometric phases also de-termine the energy spectra of particles subjected to a static force, based on the po-larization relation between Wannier-Stark ladders and geometric Zak phases. By exploiting this relation, we develop a method to extract geometric phases from en-ergy spectra of room-temperature superradiance lattices, which are momentum-space lattices of timed Dicke states. In such momentum-space lattices the thermal motion of atoms, instead of being a source of noise, provides effective forces which lead to spectroscopic signatures of the Zak phases. We measure Zak phases direct-ly from the anti-crossings between Wannier-Stark ladders in the Doppler-broadened absorption spectra of superradiance lattices. Our approach paves the way of measuring topological invariants and developing their applications in room-temperature atoms.

cond-mat.quant-gas↗

arXiv2018

Strong Coupling of Collection of Emitters on Hyperbolic Meta-Material

Recently considerable effort is devoted to the realization of the strong coupling regime of the radiation matter interaction in the context of emitter at a meta surface. The strong interaction is well realized in cavity quantum electrodynamics which also shows that the strong coupling is much easier to realize using a collection of emitters. Keeping this in mind we study if emitters on a hyperbolic meta materials can yield strong coupling regime. We show that the strong coupling can be realized for densities of emitters exceeding a critical value. A way to detect the strong coupling between emitters and a hyperbolic metamaterials is to use the Kretschman-Raether configuration. The strong coupling appears as the splitting of the re ectivity dip. In the weak coupling regime the dip position shifts. The shift and splitting can be used to sense active molecules at surfaces.

quant-ph↗