Higher-order laser modes are analyzed as a method to control focusing forces and improve the electron bunch quality in laser-plasma accelerators. In the linear wake regime, the focusing force is proportional to the transverse gradient of the laser intensity, which can be shaped by a superposition of modes. In particular, the transverse wakefield can be arbitrarily small in a region about the axis by adjusting the laser modes. Plasma channel effects, which prohibit the formation of the controlled-focusing region, can be mitigating by introducing a delay between the modes. Modes with parallel polarization produce a beat interference in the laser intensity, which lead to deflecting forces. This can be avoided by using modes with orthogonal polarization, different frequencies, or short pulses that do not overlap. Particle-in-cell simulations are performed of a laser-plasma accelerator in the quasilinear regime driven by high-order modes. Simulations show that, by including the first-order mode, the matched radius of the electron bunch is substantially increased, which for fixed bunch density and emittance implies an increase in the beam charge.

The Ornstein-Uhlenbeck process has been proposed as a model for the spontaneous activity of a neuron. In this model, the firing of the neuron corresponds to the first passage of the process to a constant boundary, or threshold. While the Laplace transform of the first-passage time distribution is available, the probability distribution function has not been obtained in any tractable form. We address the problem of estimating the parameters of the process when the only available data from a neuron are the interspike intervals, or the times between firings. In particular, we give an algorithm for computing maximum likelihood estimates and their corresponding confidence regions for the three identifiable (of the five model) parameters by numerically inverting the Laplace transform. A comparison of the two-parameter algorithm (where the time constant tau is known a priori) to the three-parameter algorithm shows that significantly more data is required in the latter case to achieve comparable parameter resolution as measured by 95% confidence intervals widths. The computational methods described here are a efficient alternative to other well known estimation techniques for leaky integrate-and-fire models. Moreover, it could serve as a template for performing parameter inference on more complex integrate-and-fire neuronal models.

Abstract. Laser and particle beam driven plasma wakefield accelerators produce accelerating fields thousands of times higher than radio-frequency accelerators, offering compactness and ultrafast bunches to extend the frontiers of high energy physics and to enable laboratory-scale radiation sources. Large-scale kinetic simulations provide essential understanding of accelerator physics to advance beam performance and stability, and showed and predicted the physics behind recent demonstration of narrow energy spread bunches. Benchmarking between codes is establishing validity of the models used, and by testing new reduced models is extending the reach of simulations to cover upcoming meter-scale multi-GeV experiments. This includes new models which exploit Lorentz boosted simulation frames to speed calculations. Simulations of experiments showed that recently demonstrated plasma gradient injection of electrons can be used as an injector to increase beam quality by orders of magnitude. Simulations are now also modeling accelerator stages of 10's of GeV, staging of modules, and new positron sources to design next generation experiments and for applications in high energy physics and light sources.

We present an interface and an implementation of the General Matrix Multiply (GEMM) routine for multiple small matrices processed simultaneously on NVIDIA graphics processing units (GPUs). We focus on matrix sizes under 16. The implementation can be easily extended to larger sizes. For single precision matrices, our implementation is 30% to 600% faster than the batched cuBLAS implementation distributed in the CUDA Toolkit 5.0 on NVIDIA Tesla K20c. For example, we obtain 104 GFlop/s and 216 GFlop/s when multiplying 100,000 independent matrix pairs of size 10 and 16, respectively. Similar improvement in performance is obtained for other sizes, in single and double precision for real and complex types, and when the number of matrices is smaller. Apart from our implementation, our different function interface also plays an important role in the improved performance. Applications of this software include Finite Element computation on GPUs.