Static Potential and Local Color Fields in Unquenched Three-Dimensional Lattice QCD

String breaking by dynamical quarks in (2+1)-d lattice QCD is demonstrated in this project, by measuring the static potential and the local color-electric field strength between a heavy quark and antiquark pair at large separations. Simulations are done for unquenched SU(2) color with two flavors of staggered quarks. An improved gluon action is used which allows simulations to be done on coarse lattices, providing an extremely efficient means to access the quark separations and propagation times at which string breaking occurs. The static quark potential is extracted using only Wilson loop operators and hence no valence quarks are present in the trial states. Results give unambiguous evidence for string breaking as the static quark potential completely saturates at twice the heavy-light meson mass at large separations. It is also shown that the local color-electric field strength between the quark pair tends toward vacuum values at large separations. Implications of these results for unquenched simulations of QCD in 4-d are drawn.Comment: 3 pages, contribution to Lattice 2002 proceedings (Confinement

On the screening of the potential between adjoint sources in QCD3QCD_3

We calculate the potential between adjoint sources in $SU(2)$ pure gauge theory in three dimensions. We investigate whether the potential saturates at large separations due to the creation of a pair of gluelumps, colour-singlet states formed when glue binds to an adjoint source.Comment: 3 pages, uuencoded Z-compressed postscript file, contribution to Lattice '9

Analyzing Neuronal Dendritic Trees with Convolutional Neural Networks

In the biological sciences, image analysis software are used to detect, segment or classify a variety of features encountered in living matter. However, the algorithms that accomplish these tasks are often designed for a specific dataset, making them hardly portable to accomplish the same tasks on images of different biological structures. Recently, convolutional neural networks have been used to perform complex image analysis on a multitude of datasets. While applications of these networks abound in the technology industry and computer science, use cases are not as common in the academic sciences. Motivated by the generalizability of neural networks, we aim to develop a machine learning algorithm to detect morphological features in the dendritic trees of Drosophila Melanogaster class IV neurons. Our approach is based on the Single Shot Multibox Detector (Liu et. al.) and our training dataset is synthesized from simulations of dendritic trees that we previously developed. Our preliminary results show that the network performs well on the training set. However, on the test set, it sometimes misses objects of interest, which calls for further improvements