A new method to study the number of colors in the final-state interactions of hadrons

We match the $\pi\pi\to\pi\pi$ scattering amplitudes of Chiral Perturbation Theory with those from dispersion relations that respect analyticity and coupled channel unitarity, as well as accurately describing experiment. Their dependence on the number of colors ($N_C$) is obtained. By varying $N_C$ the trajectories of the poles and residues (the couplings to $\pi\pi$) of light mesons, the $\sigma$, $f_0(980)$, $\rho(770)$ and $f_2(1270)$ are investigated. Our results show that the method proposed is a reliable way to study the $N_C$ dependence in hadron-hadron scattering with final-state interactions.Comment: 7 pages, 3 figures, improved $N_C$ behaviou

J/ψ→γη′π+π−J/\psi \to \gamma\eta'\pi^+\pi^- and the structure observed around the pˉp\bar pp threshold

We analyze the origin of the structure observed in the reaction $J/\psi \to \gamma \eta'\pi^+\pi^-$ for $\eta'\pi^+\pi^-$ invariant masses close to the antiproton-proton ($\bar pp$) threshold, commonly associated with the $X(1835)$ resonance. Specifically, we explore the effect of a possible contribution from the two-step process $J/\psi \to \gamma \bar NN \to \gamma \eta'\pi^+\pi^-$. The calculation is performed in distorted-wave Born approximation which allows an appropriate inclusion of the $\bar NN$ interaction in the transition amplitude. The $\bar NN$ amplitude itself is generated from a corresponding potential recently derived within chiral effective field theory. We are able to reproduce the measured spectra for the reactions $J/\psi \to \gamma \bar pp$ and $J/\psi \to \gamma \eta'\pi^+\pi^-$ for invariant masses around the $\bar pp$ threshold. The structure seen in the $\eta'\pi^+\pi^-$ spectrum emerges as a threshold effect due to the opening of the $\bar pp$ channel.Comment: 9 pages, 5 figure

Re-examining the X(4630)X(4630) resonance in the reaction e+e−→Λc+Λˉc−e^+e^-\rightarrow \Lambda^+_c\bar\Lambda^-_c

The reaction $e^+e^-\rightarrow \Lambda^+_c\bar\Lambda^-_c$ is investigated at energies close to the threshold with emphasis on the role played by the $X(4630)$ resonance. The interaction in the final $\Lambda^+_c \bar\Lambda^-_c$ system, constructed within chiral effective field theory and supplemented by a pole diagram that represents a bare $X(4630)$ resonance, is taken into account rigorously. The pole parameters of the $X(4630)$ are extracted and found to be compatible with the ones of the $X(4660)$ resonance that have been established in the reaction $e^+e^- \to \pi^+\pi^-\psi(2S)$. The actual result for the $X(4630)$ is $M = (4652.5\pm 3.4)$ MeV and $\Gamma = (62.6\pm 5.6)$ MeV. Predictions for the $\Lambda^+_c$ electromagnetic form factors in the timelike region are presented.Comment: 11 pages, 3 figure

Distortion of genealogical properties when the sample is very large

Study sample sizes in human genetics are growing rapidly, and in due course it will become routine to analyze samples with hundreds of thousands if not millions of individuals. In addition to posing computational challenges, such large sample sizes call for carefully re-examining the theoretical foundation underlying commonly-used analytical tools. Here, we study the accuracy of the coalescent, a central model for studying the ancestry of a sample of individuals. The coalescent arises as a limit of a large class of random mating models and it is an accurate approximation to the original model provided that the population size is sufficiently larger than the sample size. We develop a method for performing exact computation in the discrete-time Wright-Fisher (DTWF) model and compare several key genealogical quantities of interest with the coalescent predictions. For realistic demographic scenarios, we find that there are a significant number of multiple- and simultaneous-merger events under the DTWF model, which are absent in the coalescent by construction. Furthermore, for large sample sizes, there are noticeable differences in the expected number of rare variants between the coalescent and the DTWF model. To balance the tradeoff between accuracy and computational efficiency, we propose a hybrid algorithm that utilizes the DTWF model for the recent past and the coalescent for the more distant past. Our results demonstrate that the hybrid method with only a handful of generations of the DTWF model leads to a frequency spectrum that is quite close to the prediction of the full DTWF model.Comment: 27 pages, 2 tables, 14 figure

Identification of the Sequence of Steps Intrinsic to Spheromak Formation

A planar coaxial electrostatic helicity source is used for studying the relaxation process intrinsic to spheromak formation Experimental observations reveal that spheromak formation involves: (1) breakdown and creation of a number of distinct, arched, filamentary, plasma-filled flux loops that span from cathode to anode gas nozzles, (2) merging of these loops to form a central column, (3) jet-like expansion of the central column, (4) kink instability of the central column, (5) conversion of toroidal flux to poloidal flux by the kink instability. Steps 1 and 3 indicate that spheromak formation involves an MHD pumping of plasma from the gas nozzles into the magnetic flux tube linking the nozzles. In order to measure this pumping, the gas puffing system has been modified to permit simultaneous injection of different gas species into the two ends of the flux tube linking the wall. Gated CCD cameras with narrow-band optical filters are used to track the pumped flows

DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car

We present DeepPicar, a low-cost deep neural network based autonomous car platform. DeepPicar is a small scale replication of a real self-driving car called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN), which takes images from a front-facing camera as input and produces car steering angles as output. DeepPicar uses the same network architecture---9 layers, 27 million connections and 250K parameters---and can drive itself in real-time using a web camera and a Raspberry Pi 3 quad-core platform. Using DeepPicar, we analyze the Pi 3's computing capabilities to support end-to-end deep learning based real-time control of autonomous vehicles. We also systematically compare other contemporary embedded computing platforms using the DeepPicar's CNN-based real-time control workload. We find that all tested platforms, including the Pi 3, are capable of supporting the CNN-based real-time control, from 20 Hz up to 100 Hz, depending on hardware platform. However, we find that shared resource contention remains an important issue that must be considered in applying CNN models on shared memory based embedded computing platforms; we observe up to 11.6X execution time increase in the CNN based control loop due to shared resource contention. To protect the CNN workload, we also evaluate state-of-the-art cache partitioning and memory bandwidth throttling techniques on the Pi 3. We find that cache partitioning is ineffective, while memory bandwidth throttling is an effective solution.Comment: To be published as a conference paper at RTCSA 201