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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 paper, 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 1000 laser shots in each application. Our study shows that deep object detection networks are suitable to assist online and offline experimental analysis, even with small training sets. We believe that the presented methodology is adaptable yet robust, and we encourage further applications in Hz-level or kHz-level high-power laser facilities regarding the control and diagnostic tools, especially for those involving image data.

We investigate MeV-level attosecond electron bunches from ultrashort-pulse laser-solid interactions through similarities between experimental and simulated electron energy spectra. We show measurements of the bunch duration and temporal structure from particle-in-cell simulations. The experimental observation of such bunches favors specular reflection direction when focusing the laser pulse onto a subwavelength boundary of thick overdense plasmas at grazing incidence. Particle-in-cell simulation further reveals that the attosecond duration is a result of ultra-thin ($$\sim $$ ∼ tenth of a micron) gaps of zero electromagnetic energy density in the modulated reflected radiation, while the bunching (locally peaked electron concentration) comes from the highly-directional electron angular distribution acquired by the electrons in a grazing incidence setup. To isolate a single electron bunch, we perform simulations using 1-cycle laser pulses and analyze the effect of carrier-envelop phase with particle tracking. The duration of the electron bunch can be further decreased by increasing the laser intensity and the focal spot size, while its direction can be changed by tuning the preplasma density gradient.

We demonstrate that is it possible to optimize the yield of microwave radiation from plasmas generated by laser filamentation in atmosphere through manipulation of the laser wavefront. A genetic algorithm controls a deformable mirror that reconfigures the wavefront using the microwave waveform amplitude as feedback. Optimization runs performed as a function of air pressure show that the genetic algorithm can double the microwave field strength relative to when the mirror surface is flat. An increase in the volume and brightness of the plasma fluorescence accompanies the increase in microwave radiation, implying an improvement in the laser beam intensity profile through the filamentation region due to the optimized wavefront.

We explore the applications of a variety of machine learning techniques in relativistic laser-plasma experiments beyond optimization purposes. With the trained supervised learning models, the beam charge of electrons produced in a laser wakefield accelerator is predicted given the laser wavefront change caused by a deformable mirror. Feature importance analysis using the trained models shows that specific aberrations in the laser wavefront are favored in generating higher beam charges, which reveals more information than the genetic algorithms and the statistical correlation do. The predictive models enable operations beyond merely searching for an optimal beam charge. The quality of the measured data is characterized, and anomaly detection is demonstrated. The model robustness against measurement errors is examined by applying a range of virtual measurement error bars to the experimental data. This work demonstrates a route to machine learning applications in a highly nonlinear problem of relativistic laser-plasma interaction for in-depth data analysis to assist physics interpretation.

Improved focusing of laser beams using high numerical aperture systems is an efficient route to increasing intensities in the ultrafast regime to relativistic levels. We experimentally demonstrate a new method of optimizing the focus of a high-power laser, using second harmonic generation at full intensity in a low-pressure gas to provide a figure of merit for optimizing the shape of a deformable mirror via a genetic algorithm. Nonlinear and thermal aberrations are corrected, and aberrations introduced by filters are avoided. The method is applied to focus a millijoule, short pulse (30 fs), high repetition rate laser system to relativistic intensity in both near IR and mid IR regimes.

Filament-induced breakdown spectroscopy (FIBS) is an analytical method that holds significant promise for remote sensing. In FIBS, it is desirable to maximize the intensity and improve the reproducibility of a selected spectroscopic feature. We demonstrate the use of wavefront control in conjunction with a genetic algorithm in FIBS of metallic copper and show that this approach can increase the efficacy of filament-induced breakdown signal production. Through wavefront optimization, we enhance the intensity of a chosen characteristic spectroscopic feature of copper by a factor of approximately 3 when performing filamentation over a 1-meter distance. The relative standard deviation of signal intensity is reduced from ∼ 17% to ∼ 12% after optimization. We find that modification of the wavefront by introduction of astigmatism and coma maximizes the signal intensity, and these aberrations appear in two distinct trials of the genetic algorithm. We compare these findings to previous work on multiple filament control and discuss the possible mechanisms that lead to signal enhancement associated with both the beam amplitude and phase profile. A broader use of wavefront control with feedback may improve the performance of FIBS in remote sensing applications.

This paper presents the electric field distribution and current emission in a vacuum nano-diode. The electric field is calculated exactly inside a finite vacuum cathode-anode (A-K) gap with a single trapezoid protrusion on one of the electrode surface. It is found there is strong field enhancement on both electrodes near the protrusion, when the ratio of the A-K gap distance to the protrusion height d/h < 2. We calculate the effective field enhancement factor for the field emission current, by integrating the local Fowler-Nordheim current density along the electrode surfaces. Both the electric field enhancement and the current rectification of the miniaturized geometrical diode are studied.

There has been growing interest both in studying high intensity ultrafast laser plasma interactions with adaptive control systems as well as using long wavelength driver beams. We demonstrate the coherent control of the dynamics of laser-wakefield acceleration driven by ultrashort (∼ 100 fs) mid-infrared (∼ 3.9 μm) laser pulses. The critical density at this wavelength is 7.3 × 1019 cm−3, which is achievable with an ordinary gas target system. Interactions between mid-infrared laser pulses and such near-critical-density plasma may be beneficial due to much higher absorption of laser energy. In addition, the normalized vector potential of the laser field a0 increases with longer laser wavelength, lowering the required peak laser intensity to drive non-linear laser-wakefield acceleration. Here, MeV level, collimated electron beams with non-thermal, peaked energy spectra are generated. Optimization of electron beam qualities are realized through adaptive control of the laser wavefront. A genetic algorithm controlling a deformable mirror improves the electron total charge, energy spectra, beam pointing and stability at various plasma density profiles. Particle-in-cell simulations reveal that the optimal wavefront causes an earlier injection on the density up-ramp and thus higher energy gain as well as less filamentation during the interaction, which leads to the improvement in electron beam collimation and energy spectra.

While nearly all investigations of high order harmonic generation with relativistically intense laser pulses have taken place at 800 or 1053 nm, very few experimental studies have been done at other wavelengths. In this study, we investigate the scaling of relativistic high harmonic generation towards longer wavelengths at intensities of a 0 ∼ 1. Longer driver wavelengths enable enhanced diagnostics of the harmonic emission, as multiple orders lie in the optical regime. We measure the conversion efficiency by collecting the entire harmonic emission as well as the divergence through direct imaging. We compare the emission with 2D particle-in-cell simulations to determine the experimental target conditions. This new regime of high order harmonic generation also enables relativistic scaling as well as improved discrimination of harmonic generation mechanisms.

Studies of high-power ultrashort laser pulse interaction with matter are not only of fundamental scientific interest, but are also highly relevant to applications in the domain of remote sensing. Here, we investigate the effect of laser wavelength on coupling of femtosecond laser filaments to solid targets. Three central wavelengths have been used to produce filaments: 0.4, 0.8, and 2.0 µm. We find that, unlike the case of conventional tight focusing, use of shorter wavelengths does not necessarily produce more efficient ablation. This is explained by increased multi-photon absorption arising in near-UV filamentation. Investigations of filament-induced plasma dynamics and its thermodynamic parameters provide the foundation for unveiling the interplay between wavelength-dependent filament ablation mechanisms. In this way, strategies to increase the sensitivity of material detection via this technique may be better understood, thereby improving the analytical performance in this class of applications.

We demonstrate that optical wavefront control can enhance the intensity of signal in filament-induced breakdown spectroscopy, and that this enhancement can be associated with the application of astigmatism. © 2020 The Authors)