5,722 research outputs found
Pressure induced magnetic phase separation in LaCaMnO manganite
The pressure dependence of the Curie temperature T in
LaCaMnO was determined by neutron diffraction up to 8
GPa, and compared with the metallization temperature T \cite{irprl}.
The behavior of the two temperatures appears similar over the whole pressure
range suggesting a key role of magnetic double exchange also in the pressure
regime where the superexchange interaction is dominant. Coexistence of
antiferromagnetic and ferromagnetic peaks at high pressure and low temperature
indicates a phase separated regime which is well reproduced with a dynamical
mean-field calculation for a simplified model. A new P-T phase diagram has been
proposed on the basis of the whole set of experimental data.Comment: 5 pages, 4 figure
Electron beam transfer line design for plasma driven Free Electron Lasers
Plasma driven particle accelerators represent the future of compact
accelerating machines and Free Electron Lasers are going to benefit from these
new technologies. One of the main issue of this new approach to FEL machines is
the design of the transfer line needed to match of the electron-beam with the
magnetic undulators. Despite the reduction of the chromaticity of plasma beams
is one of the main goals, the target of this line is to be effective even in
cases of beams with a considerable value of chromaticity. The method here
explained is based on the code GIOTTO [1] that works using a homemade genetic
algorithm and that is capable of finding optimal matching line layouts directly
using a full 3D tracking code.Comment: 9 Pages, 4 Figures. A related poster was presented at EAAC 201
Identifying Galaxy Mergers in Observations and Simulations with Deep Learning
Mergers are an important aspect of galaxy formation and evolution. We aim to
test whether deep learning techniques can be used to reproduce visual
classification of observations, physical classification of simulations and
highlight any differences between these two classifications. With one of the
main difficulties of merger studies being the lack of a truth sample, we can
use our method to test biases in visually identified merger catalogues. A
convolutional neural network architecture was developed and trained in two
ways: one with observations from SDSS and one with simulated galaxies from
EAGLE, processed to mimic the SDSS observations. The SDSS images were also
classified by the simulation trained network and the EAGLE images classified by
the observation trained network. The observationally trained network achieves
an accuracy of 91.5% while the simulation trained network achieves 65.2% on the
visually classified SDSS and physically classified EAGLE images respectively.
Classifying the SDSS images with the simulation trained network was less
successful, only achieving an accuracy of 64.6%, while classifying the EAGLE
images with the observation network was very poor, achieving an accuracy of
only 53.0% with preferential assignment to the non-merger classification. This
suggests that most of the simulated mergers do not have conspicuous merger
features and visually identified merger catalogues from observations are
incomplete and biased towards certain merger types. The networks trained and
tested with the same data perform the best, with observations performing better
than simulations, a result of the observational sample being biased towards
conspicuous mergers. Classifying SDSS observations with the simulation trained
network has proven to work, providing tantalizing prospects for using
simulation trained networks for galaxy identification in large surveys.Comment: Submitted to A&A, revised after first referee report. 20 pages, 22
figures, 14 tables, 1 appendi
Ergodicity breaking in strong and network-forming glassy system
The temperature dependence of the non-ergodicity factor of vitreous GeO,
, as deduced from elastic and quasi-elastic neutron scattering
experiments, is analyzed. The data are collected in a wide range of
temperatures from the glassy phase, up to the glass transition temperature, and
well above into the undercooled liquid state. Notwithstanding the investigated
system is classified as prototype of strong glass, it is found that the
temperature- and the -behavior of follow some of the predictions
of Mode Coupling Theory. The experimental data support the hypothesis of the
existence of an ergodic to non-ergodic transition occurring also in network
forming glassy systems
Up-frequency conversion in a two-resonant-wave high-gain free-electron-laser amplifier
A free-electron laser is able to resonate at two different frequencies, both in free space and in a waveguide. The two waves have positive and negative slippage. We describe the nonlinear interaction between the two waves by a set of partial differential equations which in free space do not require the slowly varying envelope approximation (SVEA). In a waveguide a less restrictive SVEA is applied to each wave. By injecting a small signal at the low frequency, a strong signal and bunching are produced at the high frequency. This effect suggests a new method of generating short wavelength radiation
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