1,105 research outputs found
Resonances in Fock Space: Optimization of a SASER device
We model the Fock space for the electronic resonant tunneling through a
double barrier including the coherent effects of the electron-phonon
interaction. The geometry is optimized to achieve the maximal optical phonon
emission required by a SASER (ultrasound emitter) device.Comment: 4 pages, 3 figures, to be published in Proceedings of the VI Latin
American Workshop on Nonlinear Phenomena, special issue of Physica
Self-organization in a phonon laser
We make an adaptation of laser modelling equations to describe the behavior
of a phonon laser (saser). Our saser consists of an AlGaAs/GaAs double barrier
heterostructure designed to generate an intense beam of transversal acoustic
(TA) phonons. To study our system, we begin with a Hamiltonian that describes
the decay of primary longitudinal optical phonons (LO_1) into secondary (LO_2)
and TA (LO_1 -> LO_2 + TA) and its inverse process (recombination). Using this
Hamiltonian, a set of coupled equations of motion for the phonons is obtained.
We also consider the interaction between the phonons and its reservoirs. These
interactions are introduced in the equations of motion leading to a set of
coupled Langevin equations. In order to obtain an expression to describe our
saser we apply, in the Langevin equations, an adiabatic elimination of some
variables of the subsystem. Following the method above we obtain the value of
the injection threshold for the operation of our phonon laser. At this
threshold occurs a phase transition from a disordered to a coherent state. It
is shown that it is not necessary a big "optical" pumping to get a sasing
region.Comment: 4 figure
A Neural Network Gravitational Arc Finder based on the Mediatrix filamentation Method
Automated arc detection methods are needed to scan the ongoing and
next-generation wide-field imaging surveys, which are expected to contain
thousands of strong lensing systems. Arc finders are also required for a
quantitative comparison between predictions and observations of arc abundance.
Several algorithms have been proposed to this end, but machine learning methods
have remained as a relatively unexplored step in the arc finding process. In
this work we introduce a new arc finder based on pattern recognition, which
uses a set of morphological measurements derived from the Mediatrix
Filamentation Method as entries to an Artificial Neural Network (ANN). We show
a full example of the application of the arc finder, first training and
validating the ANN on simulated arcs and then applying the code on four Hubble
Space Telescope (HST) images of strong lensing systems. The simulated arcs use
simple prescriptions for the lens and the source, while mimicking HST
observational conditions. We also consider a sample of objects from HST images
with no arcs in the training of the ANN classification. We use the training and
validation process to determine a suitable set of ANN configurations, including
the combination of inputs from the Mediatrix method, so as to maximize the
completeness while keeping the false positives low. In the simulations the
method was able to achieve a completeness of about 90% with respect to the arcs
that are input to the ANN after a preselection. However, this completeness
drops to 70% on the HST images. The false detections are of the order of
3% of the objects detected in these images. The combination of Mediatrix
measurements with an ANN is a promising tool for the pattern recognition phase
of arc finding. More realistic simulations and a larger set of real systems are
needed for a better training and assessment of the efficiency of the method.Comment: Updated to match published versio
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