Driving positron beam acceleration with coherent transition radiation

Positron acceleration in plasma wakefield faces significant challenges since the positron beam must be pre-generated and precisely coupled into the wakefield, and most critically, suffers from defocusing issues. Here we propose a scheme that utilizes laser-driven electrons to produce, inject and accelerate positrons in a single set-up. The high-charge electron beam from wakefield acceleration creates copious electron-positron pairs via the Bethe-Heitler process, followed by enormous coherent transition radiation due to the electrons' exiting from the metallic foil. Simulation results show that the coherent transition radiation field reaches up to 10's GV m-1, which captures and accelerates the positrons to cut-off energy of 1.5 GeV with energy peak of 500 MeV and energy spread is about 24.3%. An external longitudinal magnetic field of 30 T is also applied to guide the electrons and positrons during the acceleration process. This proposed method offers a promising way to obtain GeV fast positron sources

Generation of Ultra-intense Gamma-ray Train by QED Harmonics

When laser intensity exceeds 10^22W/cm^2, photons with energy above MeV can be generated from high-order harmonics process in the laser-plasma interaction. We find that under such laser intensity, QED effect plays a dominating role in the radiation pattern. Contrast to the gas and relativistic HHG processes, both the occurrence and energy of gamma-ray emission produced by QED harmonics are random and QED harmonics are usually not coherent, while the property of high intensity and ultra-short duration is conserved. Our simulation shows that the period of gamma-ray train is half of the laser period and the peak intensity is 1.4e22W/cm^2. This new harmonic production with QED effects are crucial to light-matter interaction in strong field and can be verified in experiments by 10PW laser facilities in the near future.Comment: 12 pages, 4 figure

Ultra-bright, ultra-broadband hard x-ray driven by laser-produced energetic electron beams

We propose a new method of obtaining a compact ultra-bright, ultra-broadband hard X-ray source. This X-ray source has a high peak brightness in the order of 1022 photons/(s mm2 mrad2 0.1\%BW), an ultrashort duration (10 fs), and a broadband spectrum (flat distribution from 0.1 MeV to 4 MeV), and thus has wide-ranging potential applications, such as in ultrafast Laue diffraction experiments. In our scheme, laser-plasma accelerators (LPAs) provide driven electron beams. A foil target is placed oblique to the beam direction so that the target normal sheath field (TNSF) is used to provide a bending force. Using this TNSF-kick scheme, we can fully utilize the advantages of current LPAs, including their high charge, high energy, and low emittance

Scheme for proton-driven plasma-wakefield acceleration of positively charged particles in a hollow plasma channel

A new scheme for accelerating positively charged particles in a plasma wakefield accelerator is proposed. If the proton drive beam propagates in a hollow plasma channel, and the beam radius is of order of the channel width, the space charge force of the driver causes charge separation at the channel wall, which helps to focus the positively charged witness bunch propagating along the beam axis. In the channel, the acceleration buckets for positively charged particles are much larger than in the blowout regime of the uniform plasma, and stable acceleration over long distances is possible. In addition, phasing of the witness with respect to the wave can be tuned by changing the radius of the channel to ensure the acceleration is optimal. Two dimensional simulations suggest that, for proton drivers likely available in future, positively charged particles can be stably accelerated over 1 km with the average acceleration gradient of 1.3 GeV/m.Comment: 16 pages, 4 figures, 25 reference

Detection of limited-energy α particles using CR-39 in laser-induced p −11B reaction

Due to the harsh radiation environment produced by strong laser plasma, most of the detectors based on semiconductors cannot perform well. So, it is important to develop new detecting techniques with higher detection thresholds and highly charged particle resolution for investigating nuclear fusion reactions in laser-plasma environments. The Columbia Resin No. 39 (CR-39) detector is mainly sensitive to ions and insensitive to the backgrounds, such as electrons and photons. The detector has been widely used to detect charged particles in laser-plasma environments. In this work, we used a potassium–ethanol–water (PEW) etching solution to reduce the proton sensitivity of CR-39, by raising the detection threshold for the research of laser-induced 11B(p, α)2α reaction. We calibrated the 3–5 MeV α particles in an etching condition of 60°C PEW-25 solution (17% KOH + 25%C2H5OH + 58%H2O) and compared them with the manufacturer’s recommended etching conditions of 6.25 N NaOH aqueous solution at 98°C in our laser-induced nuclear reaction experiment. The results indicate, with the PEW-25 solution, that CR-39 is more suitable to distinguish α tracks from the proton background in our experiment. We also present a method to estimate the minimum detection range of α energy on specific etching conditions in our experiment

Long-term exposure to air pollution and lung function among children in China: Association and effect modification

BackgroundChildren are vulnerable to the respiratory effects of air pollution, and their lung function has been associated with long-term exposure to low air pollution level in developed countries. However, the impact of contemporary air pollution level in developing countries as a result of recent efforts to improve air quality on children's lung function is less understood.MethodsWe obtained a cross-sectional sample of 617 schoolchildren living in three differently polluted areas in Anhui province, China. 2-year average concentrations of air pollutants at the year of spirometry and the previous year (2017–2018) obtained from district-level air monitoring stations were used to characterize long-term exposure. Forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), and forced expiratory flow between 25 and 75% of FVC (FEF25−75) were determined under strict quality control. Multivariable regression was employed to evaluate the associations between air pollution level and lung function parameters, overall and by demographic characteristics, lifestyle, and vitamin D that was determined by liquid chromatography tandem mass spectrometry.ResultsMean concentration of fine particulate matter was 44.7 μg/m3, which is slightly above the interim target 1 standard of the World Health Organization. After adjusting for confounders, FVC, FEV1, and FEF25−75 showed inverse trends with increasing air pollution levels, with children in high exposure group exhibiting 87.9 [95% confidence interval (CI): 9.5, 166.4] mL decrement in FEV1 and 195.3 (95% CI: 30.5, 360.1) mL/s decrement in FEF25−75 compared with those in low exposure group. Additionally, the above negative associations were more pronounced among those who were younger, girls, not exposed to secondhand smoke, non-overweight, physically inactive, or vitamin D deficient.ConclusionsOur study suggests that long-term exposure to relatively high air pollution was associated with impaired lung function in children. More stringent pollution control measures and intervention strategies accounting for effect modification are needed for vulnerable populations in China and other developing countries

Thickness Distribution Prediction for Tectonically Deformed Coal with a Deep Belief Network: A Case Study

Thickness of tectonically deformed coal (TDC) has positive correlations with the susceptible gas outbursts in coal mines. To predict the TDC thickness of the coalbed, we proposed a prediction method using seismic attributes based on the deep belief network (DBN) and dimensionality reduction. Firstly, we built a DBN prediction model using the extracted attributes from a synthetic seismic section. Next, we transformed the possibly correlated seismic attributes into principal components through principal components analysis. Then, we compared the true TDC thickness with the predicted TDC thicknesses to evaluate the prediction accuracy of different models, i.e., a DBN model, a support vector machine model, and an extreme learning machine model. Finally, we used the DBN model to predict the TDC thickness of coalbed No. 8 in an operational coal mine based on synthetic experiments. Our studies showed that the predicted distribution of TDC thickness followed the regional characteristics of TDC development well and was positively correlated with the burial depth, coalbed thickness, and tectonic development. In summary, the proposed DBN model provided a reliable method for predicting TDC thickness and reducing gas outbursts in coal mine operations