Comment on: "Transverse-Mass M⊥M_\perp Dependence of Dilepton Emission from Preequilibrium and Quark-Gluon Plasma in High Energy Nucleus-Nucleus Collisions"

In a recent Letter, Geiger presents calculations of the dilepton emission from the early stage of ultrarelativistic heavy ion collisions using the parton cascade model (PCM). He shows that the $M_\perp$ scaling is not observed. In this Comment, we point out that this is largely due to a defect in the PCM.Comment: 3 pages, LaTex, LBL-3526

Flash of photons from the early stage of heavy-ion collisions

The dynamics of partonic cascades may be an important aspect for particle production in relativistic collisions of nuclei at CERN SPS and BNL RHIC energies. Within the Parton-Cascade Model, we estimate the production of single photons from such cascades due to scattering of quarks and gluons q g -> q gamma, quark-antiquark annihilation q qbar -> g gamma, or gamma gamma, and from electromagnetic brems-strahlung of quarks q -> q gamma. We find that the latter QED branching process plays the dominant role for photon production, similarly as the QCD branchings q -> q g and g -> g g play a crucial role for parton multiplication. We conclude therefore that photons accompanying the parton cascade evolution during the early stage of heavy-ion collisions shed light on the formation of a partonic plasma.Comment: 4 pages including 3 postscript figure

Fibre DFB lasers in a 4x10 Gbit/s WDM link with a single sinc-sampled fibre grating dispersion compensator

WDM transmission and dispersion compensation at 40 Gbit/s over 200 km standard fibre is demonstrated on a 100 GHz grid using four high power single-polarisation single-sided output DFB fibre laser based transmitters and a single 4 channel WDM chirped fibre Bragg grating dispersion compensator

Parton cascade description of relativistic heavy-ion collisions at CERN SPS energies ?

We examine Pb+Pb collisions at CERN SPS energy 158 A GeV, by employing the earlier developed and recently refined parton-cascade/cluster-hadronization model and its Monte Carlo implementation. This space-time model involves the dynamical interplay of perturbative QCD parton production and evolution, with non-perturbative parton-cluster formation and hadron production through cluster decays. Using computer simulations, we are able to follow the entwined time-evolution of parton and hadron degrees of freedom in both position and momentum space, from the instant of nuclear overlap to the final yield of particles. We present and discuss results for the multiplicity distributions, which agree well with the measured data from the CERN SPS, including those for K mesons. The transverse momentum distributions of the produced hadrons are also found to be in good agreement with the preliminary data measured by the NA49 and the WA98 collaboration for the collision of lead nuclei at the CERN SPS. The analysis of the time evolution of transverse energy deposited in the collision zone and the energy density suggests an existence of partonic matter for a time of more than 5 fm.Comment: 16 pages including 7 postscript figure

Deep Convolutional Neural Networks as strong gravitational lens detectors

Future large-scale surveys with high resolution imaging will provide us with a few $10^5$ new strong galaxy-scale lenses. These strong lensing systems however will be contained in large data amounts which are beyond the capacity of human experts to visually classify in a unbiased way. We present a new strong gravitational lens finder based on convolutional neural networks (CNNs). The method was applied to the Strong Lensing challenge organised by the Bologna Lens Factory. It achieved first and third place respectively on the space-based data-set and the ground-based data-set. The goal was to find a fully automated lens finder for ground-based and space-based surveys which minimizes human inspect. We compare the results of our CNN architecture and three new variations ("invariant" "views" and "residual") on the simulated data of the challenge. Each method has been trained separately 5 times on 17 000 simulated images, cross-validated using 3 000 images and then applied to a 100 000 image test set. We used two different metrics for evaluation, the area under the receiver operating characteristic curve (AUC) score and the recall with no false positive ($\mathrm{Recall}_{\mathrm{0FP}}$). For ground based data our best method achieved an AUC score of $0.977$ and a $\mathrm{Recall}_{\mathrm{0FP}}$ of $0.50$. For space-based data our best method achieved an AUC score of $0.940$ and a $\mathrm{Recall}_{\mathrm{0FP}}$ of $0.32$. On space-based data adding dihedral invariance to the CNN architecture diminished the overall score but achieved a higher no contamination recall. We found that using committees of 5 CNNs produce the best recall at zero contamination and consistenly score better AUC than a single CNN. We found that for every variation of our CNN lensfinder, we achieve AUC scores close to $1$ within $6\%$.Comment: 9 pages, accepted to A&

Stochastic Yield Catastrophes and Robustness in Self-Assembly

A guiding principle in self-assembly is that, for high production yield, nucleation of structures must be significantly slower than their growth. However, details of the mechanism that impedes nucleation are broadly considered irrelevant. Here, we analyze self-assembly into finite-sized target structures employing mathematical modeling. We investigate two key scenarios to delay nucleation: (i) by introducing a slow activation step for the assembling constituents and, (ii) by decreasing the dimerization rate. These scenarios have widely different characteristics. While the dimerization scenario exhibits robust behavior, the activation scenario is highly sensitive to demographic fluctuations. These demographic fluctuations ultimately disfavor growth compared to nucleation and can suppress yield completely. The occurrence of this stochastic yield catastrophe does not depend on model details but is generic as soon as number fluctuations between constituents are taken into account. On a broader perspective, our results reveal that stochasticity is an important limiting factor for self-assembly and that the specific implementation of the nucleation process plays a significant role in determining the yield