arXiv subjects

Jinpu Lin

Publications and source records attributed to Jinpu Lin.

4 recordsLinked to original sources

arXiv2022

Data-driven Science and Machine Learning Methods in Laser-Plasma Physics

Laser-plasma physics has developed rapidly over the past few decades as high-power lasers have become both increasingly powerful and more widely available. Early experimental and numerical research in this field was restricted to single-shot experiments with limited parameter exploration. However, recent technological improvements make it possible to gather an increasing amount of data, both in experiments and simulations. This has sparked interest in using advanced techniques from mathematics, statistics and computer science to deal with, and benefit from, big data. At the same time, sophisticated modeling techniques also provide new ways for researchers to effectively deal with situations in which still only sparse amounts of data are available. This paper aims to present an overview of relevant machine learning methods with focus on applicability to laser-plasma physics, including its important sub-fields of laser-plasma acceleration and inertial confinement fusion.

cs.LG↗

arXiv2022

Electron Acceleration and Radiation Generation from Relativistic Laser-Plasma Interactions at High Repetition-Rate

This dissertation explores the interaction between high-intensity lasers and plasmas to accelerate electrons and produce radiation via experimental and computational efforts. The laser pulses used in this dissertation have ultrashort duration (< 100 fs), near-infrared to mid-infrared wavelength (0.8 $\mu$m, 2 $\mu$m, or 3.9 $\mu$m), millijoules of energy, and high repetition rates (480 Hz or 20 Hz). The plasma sources applied are from solid-density targets (overdense) or gaseous targets (underdense). With the high-repetition-rate capability, statistical methods are employed to optimize certain aspect of the experiments and to interpret the physics.

physics.plasm-ph↗

arXiv2022

Applications of object detection networks at high-power laser systems and experiments

The recent advent of deep artificial neural networks has resulted in a dramatic increase in performance for object classification and detection. While pre-trained with everyday objects, we find that a state-of-the-art object detection architecture can very efficiently be fine-tuned to work on a variety of object detection tasks in a high-power laser laboratory. In this manuscript, three exemplary applications are presented. We show that the plasma waves in a laser-plasma accelerator can be detected and located on the optical shadowgrams. The plasma wavelength and plasma density are estimated accordingly. Furthermore, we present the detection of all the peaks in an electron energy spectrum of the accelerated electron beam, and the beam charge of each peak is estimated accordingly. Lastly, we demonstrate the detection of optical damage in a high-power laser system. The reliability of the object detector is demonstrated over one thousand laser shots in each application. Our study shows that deep object detection networks are suitable to assist online and offline experiment analysis, even with small training sets. We believe that the presented methodology is adaptable yet robust, and we encourage further applications in high-power laser facilities regarding the control and diagnostic tools, especially for those involving image data.

physics.plasm-ph↗

arXiv2020

Beyond optimization -- supervised learning applications in relativistic laser-plasma experiments

We explore the applications of machine learning techniques in relativistic laser-plasma experiments beyond optimization purposes. We predict the beam charge of electrons produced in a laser wakefield accelerator given the laser wavefront change caused by a deformable mirror. Machine learning enables feature analysis beyond merely searching for an optimal beam charge, showing that specific aberrations in the laser wavefront are favored in generating higher beam charges. Supervised learning models allow characterizing the measured data quality as well as recognizing irreproducible data and potential outliers. We also include virtual measurement errors in the experimental data to examine the model robustness under these conditions. This work demonstrates how machine learning methods can benefit data analysis and physics interpretation in a highly nonlinear problem of relativistic laser-plasma interaction.

physics.plasm-ph↗