Relaxation energies and excited state structures of poly(para-phenylene)

We investigate the relaxation energies and excited state geometries of the light emitting polymer, poly(para-phenylene). We solve the Pariser-Parr-Pople-Peierls model using the density matrix renormalization group method. We find that the lattice relaxation of the dipole-active $1^1B_{1u}^-$ state is quite different from that of the $1^3B_{1u}^+$ state and the dipole-inactive $2^1A_g^+$ state. In particular, the $1^1B_{1u}^-$ state is rather weakly coupled to the lattice and has a rather small relaxation energy ca. 0.1 eV. In contrast, the $1^3B_{1u}^+$ and $2^1A_g^+$ states are strongly coupled with relaxation energies of ca. 0.5 and ca. 1.0 eV, respectively. By analogy to linear polyenes, we argue that this difference can be understood by the different kind of solitons present in the $1^1B_{1u}^-$, $1^3B_{1u}^+$ and $2^1A_g^+$ states. The difference in relaxation energies of the $1^1B_{1u}^-$ and $1^3B_{1u}^+$ states accounts for approximately one-third of the exchange gap in light-emitting polymers.Comment: Submitted to Physical Review

The deterministic Kermack-McKendrick model bounds the general stochastic epidemic

We prove that, for Poisson transmission and recovery processes, the classic Susceptible $\to$ Infected $\to$ Recovered (SIR) epidemic model of Kermack and McKendrick provides, for any given time $t>0$, a strict lower bound on the expected number of suscpetibles and a strict upper bound on the expected number of recoveries in the general stochastic SIR epidemic. The proof is based on the recent message passing representation of SIR epidemics applied to a complete graph

Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees

We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that these star/galaxy classifications are expected to be reliable for approximately 22 million objects with r < ~20. The general machine learning environment Data-to-Knowledge and supercomputing resources enabled extensive investigation of the decision tree parameter space. This work presents the first public release of objects classified in this way for an entire SDSS data release. The objects are classified as either galaxy, star or nsng (neither star nor galaxy), with an associated probability for each class. To demonstrate how to effectively make use of these classifications, we perform several important tests. First, we detail selection criteria within the probability space defined by the three classes to extract samples of stars and galaxies to a given completeness and efficiency. Second, we investigate the efficacy of the classifications and the effect of extrapolating from the spectroscopic regime by performing blind tests on objects in the SDSS, 2dF Galaxy Redshift and 2dF QSO Redshift (2QZ) surveys. Given the photometric limits of our spectroscopic training data, we effectively begin to extrapolate past our star-galaxy training set at r ~ 18. By comparing the number counts of our training sample with the classified sources, however, we find that our efficiencies appear to remain robust to r ~ 20. As a result, we expect our classifications to be accurate for 900,000 galaxies and 6.7 million stars, and remain robust via extrapolation for a total of 8.0 million galaxies and 13.9 million stars. [Abridged]Comment: 27 pages, 12 figures, to be published in ApJ, uses emulateapj.cl

The Detection of Ionizing Radiation by Plasma Panel Sensors: Cosmic Muons, Ion Beams and Cancer Therapy

The plasma panel sensor is an ionizing photon and particle radiation detector derived from PDP technology with high gain and nanosecond response. Experimental results in detecting cosmic ray muons and beta particles from radioactive sources are described along with applications including high energy and nuclear physics, homeland security and cancer therapeuticsComment: Presented at SID Symposium, June 201

Plasma Panel Sensors for Particle and Beam Detection

The plasma panel sensor (PPS) is an inherently digital, high gain, novel variant of micropattern gas detectors inspired by many operational and fabrication principles common to plasma display panels (PDPs). The PPS is comprised of a dense array of small, plasma discharge, gas cells within a hermetically-sealed glass panel, and is assembled from non-reactive, intrinsically radiation-hard materials such as glass substrates, metal electrodes and mostly inert gas mixtures. We are developing the technology to fabricate these devices with very low mass and small thickness, using gas gaps of at least a few hundred micrometers. Our tests with these devices demonstrate a spatial resolution of about 1 mm. We intend to make PPS devices with much smaller cells and the potential for much finer position resolutions. Our PPS tests also show response times of several nanoseconds. We report here our results in detecting betas, cosmic-ray muons, and our first proton beam tests.Comment: 2012 IEEE NS

Development of a plasma panel radiation detector: recent progress and key issues

A radiation detector based on plasma display panel technology, which is the principal component of plasma television displays is presented. Plasma Panel Sensor (PPS) technology is a variant of micropattern gas radiation detectors. The PPS is conceived as an array of sealed plasma discharge gas cells which can be used for fast response (O(5ns) per pixel), high spatial resolution detection (pixel pitch can be less than 100 micrometer) of ionizing and minimum ionizing particles. The PPS is assembled from non-reactive, intrinsically radiation-hard materials: glass substrates, metal electrodes and inert gas mixtures. We report on the PPS development program, including simulations and design and the first laboratory studies which demonstrate the usage of plasma display panels in measurements of cosmic ray muons, as well as the expansion of experimental results on the detection of betas from radioactive sources.Comment: presented at IEEE NSS 2011 (Barcelona

The characteristics of cognitive neuroscience tests in a schizophrenia cognition clinical trial: Psychometric properties and correlations with standard measures.

In comparison to batteries of standard neuropsychological tests, cognitive neuroscience tests may offer a more specific assessment of discrete neurobiological processes that may be aberrant in schizophrenia. However, more information regarding psychometric properties and correlations with standard neuropsychological tests and functional measures is warranted to establish their validity as treatment outcome measures. The N-back and AX-Continuous Performance Task (AX-CPT) are two promising cognitive neuroscience tests designed to measure specific components of working memory and contextual processing respectively. In the current study, we report the psychometric properties of multiple outcome measures from these two tests as well as their correlations with standard neuropsychological measures and functional capacity measures. The results suggest that while the AX-CPT and N-back display favorable psychometric properties, they do not exhibit greater sensitivity or specificity with functional measures than standard neurocognitive tests