3,306 research outputs found
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
state is quite different from that of the state and the
dipole-inactive state. In particular, the state is
rather weakly coupled to the lattice and has a rather small relaxation energy
ca. 0.1 eV. In contrast, the and 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 , and
states. The difference in relaxation energies of the
and 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 Infected Recovered (SIR) epidemic model of Kermack and McKendrick provides, for any given time , 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
Recommended from our members
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
- …