3,063 research outputs found
Mock LISA data challenge for the galactic white dwarf binaries
We present data analysis methods used in detection and the estimation of parameters of gravitational wave signals from the white dwarf binaries in the mock LISA data challenge. Our main focus is on the analysis of challenge 3.1, where the gravitational wave signals from more than 50 mln. Galactic binaries were added to the simulated Gaussian instrumental noise. Majority of the signals at low frequencies are not resolved individually. The confusion between the signals is strongly reduced at frequencies above 5 mHz. Our basic data analysis procedure is the maximum likelihood detection method. We filter the data through the template bank at the first step of the search, then we refine parameters using the Nelder-Mead algorithm, we remove the strongest signal found and we repeat the procedure. We detect reliably and estimate parameters accurately of more than ten thousand signals from white dwarf binaries
Structure-Preserving Model-Reduction of Dissipative Hamiltonian Systems
Reduced basis methods are popular for approximately solving large and complex
systems of differential equations. However, conventional reduced basis methods
do not generally preserve conservation laws and symmetries of the full order
model. Here, we present an approach for reduced model construction, that
preserves the symplectic symmetry of dissipative Hamiltonian systems. The
method constructs a closed reduced Hamiltonian system by coupling the full
model with a canonical heat bath. This allows the reduced system to be
integrated with a symplectic integrator, resulting in a correct dissipation of
energy, preservation of the total energy and, ultimately, in the stability of
the solution. Accuracy and stability of the method are illustrated through the
numerical simulation of the dissipative wave equation and a port-Hamiltonian
model of an electric circuit
Structure Preserving Model Reduction of Parametric Hamiltonian Systems
While reduced-order models (ROMs) have been popular for efficiently solving
large systems of differential equations, the stability of reduced models over
long-time integration is of present challenges. We present a greedy approach
for ROM generation of parametric Hamiltonian systems that captures the
symplectic structure of Hamiltonian systems to ensure stability of the reduced
model. Through the greedy selection of basis vectors, two new vectors are added
at each iteration to the linear vector space to increase the accuracy of the
reduced basis. We use the error in the Hamiltonian due to model reduction as an
error indicator to search the parameter space and identify the next best basis
vectors. Under natural assumptions on the set of all solutions of the
Hamiltonian system under variation of the parameters, we show that the greedy
algorithm converges with exponential rate. Moreover, we demonstrate that
combining the greedy basis with the discrete empirical interpolation method
also preserves the symplectic structure. This enables the reduction of the
computational cost for nonlinear Hamiltonian systems. The efficiency, accuracy,
and stability of this model reduction technique is illustrated through
simulations of the parametric wave equation and the parametric Schrodinger
equation
The search for black hole binaries using a genetic algorithm
In this work we use genetic algorithm to search for the gravitational wave
signal from the inspiralling massive Black Hole binaries in the simulated LISA
data. We consider a single signal in the Gaussian instrumental noise. This is a
first step in preparation for analysis of the third round of the mock LISA data
challenge. We have extended a genetic algorithm utilizing the properties of the
signal and the detector response function. The performance of this method is
comparable, if not better, to already existing algorithms.Comment: 11 pages, 4 figures, proceeding for GWDAW13 (Puerto Rico
Searching for Galactic White Dwarf Binaries in Mock LISA Data using an F-Statistic Template Bank
We describe an F-statistic search for continuous gravitational waves from
galactic white-dwarf binaries in simulated LISA Data. Our search method employs
a hierarchical template-grid based exploration of the parameter space. In the
first stage, candidate sources are identified in searches using different
simulated laser signal combinations (known as TDI variables). Since each source
generates a primary maximum near its true "Doppler parameters" (intrinsic
frequency and sky position) as well as numerous secondary maxima of the
F-statistic in Doppler parameter space, a search for multiple sources needs to
distinguish between true signals and secondary maxima associated with other,
"louder" signals. Our method does this by applying a coincidence test to reject
candidates which are not found at nearby parameter space positions in searches
using each of the three TDI variables. For signals surviving the coincidence
test, we perform a fully coherent search over a refined parameter grid to
provide an accurate parameter estimation for the final candidates. Suitably
tuned, the pipeline is able to extract 1989 true signals with only 5 false
alarms. The use of the rigid adiabatic approximation allows recovery of signal
parameters with errors comparable to statistical expectations, although there
is still some systematic excess with respect to statistical errors expected
from Gaussian noise. An experimental iterative pipeline with seven rounds of
signal subtraction and re-analysis of the residuals allows us to increase the
number of signals recovered to a total of 3419 with 29 false alarms.Comment: 29 pages, 11 figures; submitted to Classical and Quantum Gravit
Cosmic Dust Aggregation with Stochastic Charging
The coagulation of cosmic dust grains is a fundamental process which takes
place in astrophysical environments, such as presolar nebulae and circumstellar
and protoplanetary disks. Cosmic dust grains can become charged through
interaction with their plasma environment or other processes, and the resultant
electrostatic force between dust grains can strongly affect their coagulation
rate. Since ions and electrons are collected on the surface of the dust grain
at random time intervals, the electrical charge of a dust grain experiences
stochastic fluctuations. In this study, a set of stochastic differential
equations is developed to model these fluctuations over the surface of an
irregularly-shaped aggregate. Then, employing the data produced, the influence
of the charge fluctuations on the coagulation process and the physical
characteristics of the aggregates formed is examined. It is shown that dust
with small charges (due to the small size of the dust grains or a tenuous
plasma environment) are affected most strongly
Pigment Melanin: Pattern for Iris Recognition
Recognition of iris based on Visible Light (VL) imaging is a difficult
problem because of the light reflection from the cornea. Nonetheless, pigment
melanin provides a rich feature source in VL, unavailable in Near-Infrared
(NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical
not stimulated in NIR. In this case, a plausible solution to observe such
patterns may be provided by an adaptive procedure using a variational technique
on the image histogram. To describe the patterns, a shape analysis method is
used to derive feature-code for each subject. An important question is how much
the melanin patterns, extracted from VL, are independent of iris texture in
NIR. With this question in mind, the present investigation proposes fusion of
features extracted from NIR and VL to boost the recognition performance. We
have collected our own database (UTIRIS) consisting of both NIR and VL images
of 158 eyes of 79 individuals. This investigation demonstrates that the
proposed algorithm is highly sensitive to the patterns of cromophores and
improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on
Instruments and Measurements, Volume 59, Issue number 4, April 201
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