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.</p

Recombinant amelogenin peptide TRAP promoting remineralization of early enamel caries: An in vitro study

Objective: To explore the regulatory effect of recombinant amelogenin peptide TRAP on the remineralization of early enamel carious lesions.Methods: Forty-eight bovine enamel blocks that prepared initial lesions in vitro were split at random into four groups for immersion treatment for 12 days: 1) remineralizing medium; 2) studied peptide 1 (consisting of the N- and C-termini of porcine amelogenin) + remineralizing medium; 3) studied peptide 2 (TRAP) + remineralizing medium; 4) fluoride + remineralizing medium. After demineralization and remineralization immersion, each specimen’s mean mineral loss and lesion depth were measured using micro-computed tomography (micro-CT). The changes in lesion depth (∆LD) and mineral gain (∆Z) were computed following remineralization. The enamel samples were then cut into sections and examined with polarized light microscopy (PLM). The cross-section morphology was observed by scanning electron microscopy (SEM). The crystal phase was analyzed by an X-ray micro-diffractometer (XRD). The calcium-binding properties were determined using isothermal titration calorimetry (ITC).Results: Micro-CT analysis revealed a significant reduction in mineral loss in the four groups following the remineralization treatment (p &lt; 0.05). The treatment with fluoride resulted in the greatest ∆Z and ∆LD, whereas the treatment with a remineralizing medium showed the least ∆Z and ∆LD among all groups. The ∆Z and ∆LD of the studied peptide 1 and studied peptide 2 groups were greater than those of the remineralizing medium group. However, there was no significant difference between the studied peptide 1 and studied peptide 2 groups (p &gt; 0.05). All of the samples that the PLM analyzed had a thickening of the surface layer. A negative birefringent band changed in the lesion’s body. The SEM displayed that minerals were formed in all four groups of samples. The XRD results indicated that the products of remineralization of the studied peptide were hydroxyapatite crystals (HA). ITC showed that there were two binding modes between the calcium and peptide TRAP.Conclusion: This study confirmed the potential of the recombinant amelogenin peptide TRAP as a key functional motif of amelogenin protein for enamel remineralization and provided a promising biomaterial for remineralization in initial enamel carious lesion treatment

Tango Controls and data pipeline for petawatt laser experiments

The Centre for Advanced Laser Applications in Garching, Germany, is home to the ATLAS-3000 multi-petawatt laser, dedicated to research on laser particle acceleration and its applications. A control system based on Tango Controls is implemented for both the laser and four experimental areas. The device server approach features high modularity, which, in addition to the hardware control, enables a quick extension of the system and allows for automated data acquisition of the laser parameters and experimental data for each laser shot. In this paper we present an overview of our implementation of the control system, as well as our advances in terms of experimental operation, online supervision and data processing. We also give an outlook on advanced experimental supervision and online data evaluation – where the data can be processed in a pipeline – which is being developed on the basis of this infrastructure

Electron Acceleration and Radiation Generation from Relativistic Laser-Plasma Interactions and Statistical Methods 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. MeV-level attosecond electron bunches from the interactions between ultrashort pulses (30 fs, 0.8 $mu m$, 12 mJ) and solid targets (fused silica and copper). Attosecond electron bunches are only observed at grazing incidence , and the bunch duration is measured in acf{PIC} simulations. The effects of carrier-envelope phase, preplasma density profile, laser intensity, and the focal spot size are analyzed. Surface acs{HHG} and corresponding phenomena are studied using femtosecond mid-infrared laser pulses (2 $mu m$, 1.6 mJ, 67 fs) interacting with solid targets (fused silica and silicon). Experimental measurements of the acs{HHG} spectra and the beam divergence are reported. The power-law scaling of harmonic efficiency vs. harmonic order is examined. The intensity of polarization of the harmonics are measured when the driving laser pulses are polarized in linear and circular directions. The scaling of harmonic efficiency vs. laser intensity is investigated. Characteristic x-ray emissions from laser-solid interactions are presented. Laser pulses with various wavelengths and pulse energies are used to interact with a molybdenum target with various preplasma density profiles. The study is performed both experimentally with hundreds of thousands of laser shots, and computationally with acs{PIC} simulations scanning over the 4-dimensional parameter space consisting of laser wavelength, pulse energy, preplasma profile, and x-ray emission. Statistical methods are used to improve the focus of laser beams in high numerical aperture. A method that optimizes the focus of a high-power laser without attenuation is demonstrated experimentally using near-infrared (0.8 $mu m$) and mid-infrared (2 $mu m$) laser pulses, where the second harmonic generation at full intensity in a low-pressure gas provides a figure of merit for optimizing the laser wavefront via a genetic algorithm. Coherent control of the dynamics of laser-wakefield acceleration driven by ultrashort ($sim 100$ fs) mid-infrared ($sim 3.9~mu$m) laser pulses is demonstrated, where plasma densities up to $3times 10^{19}cm^{-3}$ (or $40$ of the critical density at $lambda=3.9 mu m$) are used. MeV-level, collimated electron beams with non-thermal, peaked energy spectra are generated. Optimization of electron beam qualities is realized through adaptive control of the laser wavefront using a deformable mirror and a genetic algorithm. The improvement in the electron beam quality is explained by acs{PIC} simulations using the optimal wavefront. Applications of machine learning techniques in relativistic laser-plasma experiments are explored 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 on the trained models shows that specific aberrations in the laser wavefront are favored. 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.PHDNuclear Engineering & Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169882/1/linjinp_1.pd

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

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

Abstract 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.</jats:p