168 research outputs found
Tests of Bayesian Model Selection Techniques for Gravitational Wave Astronomy
The analysis of gravitational wave data involves many model selection
problems. The most important example is the detection problem of selecting
between the data being consistent with instrument noise alone, or instrument
noise and a gravitational wave signal. The analysis of data from ground based
gravitational wave detectors is mostly conducted using classical statistics,
and methods such as the Neyman-Pearson criteria are used for model selection.
Future space based detectors, such as the \emph{Laser Interferometer Space
Antenna} (LISA), are expected to produced rich data streams containing the
signals from many millions of sources. Determining the number of sources that
are resolvable, and the most appropriate description of each source poses a
challenging model selection problem that may best be addressed in a Bayesian
framework. An important class of LISA sources are the millions of low-mass
binary systems within our own galaxy, tens of thousands of which will be
detectable. Not only are the number of sources unknown, but so are the number
of parameters required to model the waveforms. For example, a significant
subset of the resolvable galactic binaries will exhibit orbital frequency
evolution, while a smaller number will have measurable eccentricity. In the
Bayesian approach to model selection one needs to compute the Bayes factor
between competing models. Here we explore various methods for computing Bayes
factors in the context of determining which galactic binaries have measurable
frequency evolution. The methods explored include a Reverse Jump Markov Chain
Monte Carlo (RJMCMC) algorithm, Savage-Dickie density ratios, the Schwarz-Bayes
Information Criterion (BIC), and the Laplace approximation to the model
evidence. We find good agreement between all of the approaches.Comment: 11 pages, 6 figure
Nonparametric Reconstruction of the Dark Energy Equation of State from Diverse Data Sets
The cause of the accelerated expansion of the Universe poses one of the most
fundamental questions in physics today. In the absence of a compelling theory
to explain the observations, a first task is to develop a robust phenomenology.
If the acceleration is driven by some form of dark energy, then, the
phenomenology is determined by the dark energy equation of state w. A major aim
of ongoing and upcoming cosmological surveys is to measure w and its time
dependence at high accuracy. Since w(z) is not directly accessible to
measurement, powerful reconstruction methods are needed to extract it reliably
from observations. We have recently introduced a new reconstruction method for
w(z) based on Gaussian process modeling. This method can capture nontrivial
time-dependences in w(z) and, most importantly, it yields controlled and
unbaised error estimates. In this paper we extend the method to include a
diverse set of measurements: baryon acoustic oscillations, cosmic microwave
background measurements, and supernova data. We analyze currently available
data sets and present the resulting constraints on w(z), finding that current
observations are in very good agreement with a cosmological constant. In
addition we explore how well our method captures nontrivial behavior of w(z) by
analyzing simulated data assuming high-quality observations from future
surveys. We find that the baryon acoustic oscillation measurements by
themselves already lead to remarkably good reconstruction results and that the
combination of different high-quality probes allows us to reconstruct w(z) very
reliably with small error bounds.Comment: 14 pages, 9 figures, 3 table
Current Migration Trends in Northeast Asia and the Russian Far East
The purpose of this work is to analyse migration trends currently observed in the countries of Northeast Asia. In the context of the pandemic, and then the beginning of a tough phase of confrontation between Russia and Western countries, certain migration trends have changed. Comparative and historical approaches were used in the study to examine the features of the migration agenda in the region, as well as the recent transformation of migration flows in different countries. It was concluded that nowadays all countries of the region need migrants, create conditions to attract them and compete for labour resources.Цель данной работы является анализ современных миграционных тенденций, присущих странам Северо-Восточной Азии на современном этапе. В условиях пандемии, а затем и начала жесткой фазы конфронтации между Россией и западными странами отдельные миграционные тренды претерпели изменения. В рамках данного исследования были использованы компаративистский и исторический подходы, которые дали возможность проанализировать особенности формирования миграционной повестки в регионе, а также того, как трансформировались в последние годы миграционные потоки в разных странах. В рамках данной работы автор приходит к выводу, что абсолютно все страны региона на сегодняшний день остро нуждаются в мигрантах, создают для их привлечения условия и включаются в международную борьбу за трудовые ресурсы
PROBLEMS OF NATIONAL SECURITY AND EASTERN REgIONS OF RUSSIA IN THE CONTEXT OF RUSSIA’S PIVOT TO THE EAST
В рамках данной работы автор анализирует проблемы национальной безопасности России на современном этапе, сквозь призму их преломления на азиатских регионах нашей страны. Сибирь, и, в особенности, Дальний Восток, значительно отстают от западных регионов России в своем экономическом развитии, в развитии человеческого капитала и в иных сферах. В последние годы принят целый ряд программ, предпринимается комплекс мер, которые должны способствовать развитию регионов, преодолению их отставания. Автор стремится проанализировать эти меры, насколько они являются своевременными и эффективными.Within the framework of this work, the author analyzes the problems of Russia’s national security at the present stage, through the prism of their refraction in the Asian regions of our country. Siberia, and, in particular, the Far East, lag significantly behind the western regions of Russia in its economic development, in the development of human capital and in other areas. In recent years, a number of programs have been adopted, a set of measures is being taken that should contribute to the development of regions and overcome their lag. The author seeks to analyze these measures, to what extent they are timely and effectiv
A Bayesian Approach to the Detection Problem in Gravitational Wave Astronomy
The analysis of data from gravitational wave detectors can be divided into
three phases: search, characterization, and evaluation. The evaluation of the
detection - determining whether a candidate event is astrophysical in origin or
some artifact created by instrument noise - is a crucial step in the analysis.
The on-going analyses of data from ground based detectors employ a frequentist
approach to the detection problem. A detection statistic is chosen, for which
background levels and detection efficiencies are estimated from Monte Carlo
studies. This approach frames the detection problem in terms of an infinite
collection of trials, with the actual measurement corresponding to some
realization of this hypothetical set. Here we explore an alternative, Bayesian
approach to the detection problem, that considers prior information and the
actual data in hand. Our particular focus is on the computational techniques
used to implement the Bayesian analysis. We find that the Parallel Tempered
Markov Chain Monte Carlo (PTMCMC) algorithm is able to address all three phases
of the anaylsis in a coherent framework. The signals are found by locating the
posterior modes, the model parameters are characterized by mapping out the
joint posterior distribution, and finally, the model evidence is computed by
thermodynamic integration. As a demonstration, we consider the detection
problem of selecting between models describing the data as instrument noise, or
instrument noise plus the signal from a single compact galactic binary. The
evidence ratios, or Bayes factors, computed by the PTMCMC algorithm are found
to be in close agreement with those computed using a Reversible Jump Markov
Chain Monte Carlo algorithm.Comment: 19 pages, 12 figures, revised to address referee's comment
Nonparametric Reconstruction of the Dark Energy Equation of State
A basic aim of ongoing and upcoming cosmological surveys is to unravel the
mystery of dark energy. In the absence of a compelling theory to test, a
natural approach is to better characterize the properties of dark energy in
search of clues that can lead to a more fundamental understanding. One way to
view this characterization is the improved determination of the
redshift-dependence of the dark energy equation of state parameter, w(z). To do
this requires a robust and bias-free method for reconstructing w(z) from data
that does not rely on restrictive expansion schemes or assumed functional forms
for w(z). We present a new nonparametric reconstruction method that solves for
w(z) as a statistical inverse problem, based on a Gaussian Process
representation. This method reliably captures nontrivial behavior of w(z) and
provides controlled error bounds. We demonstrate the power of the method on
different sets of simulated supernova data; the approach can be easily extended
to include diverse cosmological probes.Comment: 16 pages, 11 figures, accepted for publication in Physical Review
Extracting galactic binary signals from the first round of Mock LISA Data Challenges
We report on the performance of an end-to-end Bayesian analysis pipeline for
detecting and characterizing galactic binary signals in simulated LISA data.
Our principal analysis tool is the Blocked-Annealed Metropolis Hasting (BAM)
algorithm, which has been optimized to search for tens of thousands of
overlapping signals across the LISA band. The BAM algorithm employs Bayesian
model selection to determine the number of resolvable sources, and provides
posterior distribution functions for all the model parameters. The BAM
algorithm performed almost flawlessly on all the Round 1 Mock LISA Data
Challenge data sets, including those with many highly overlapping sources. The
only misses were later traced to a coding error that affected high frequency
sources. In addition to the BAM algorithm we also successfully tested a Genetic
Algorithm (GA), but only on data sets with isolated signals as the GA has yet
to be optimized to handle large numbers of overlapping signals.Comment: 13 pages, 4 figures, submitted to Proceedings of GWDAW-11 (Berlin,
Dec. '06
From cosmic deceleration to acceleration: new constraints from SN Ia and BAO/CMB
We use type Ia supernovae (SN Ia) data in combination with recent baryonic
acoustic oscillations (BAO) and cosmic microwave background (CMB) observations
to constrain a kink-like parametrization of the deceleration parameter ().
This -parametrization can be written in terms of the initial () and
present () values of the deceleration parameter, the redshift of the
cosmic transition from deceleration to acceleration () and the redshift
width of such transition (). By assuming a flat space geometry,
and adopting a likelihood approach to deal with the SN Ia data we obtain, at
the 68% confidence level (C.L.), that: ,
and when we combine
BAO/CMB observations with SN Ia data processed with the MLCS2k2 light-curve
fitter. When in this combination we use the SALT2 fitter we get instead, at the
same C.L.: , and
. Our results indicate, with a quite general and
model independent approach, that MLCS2k2 favors Dvali-Gabadadze-Porrati-like
cosmological models, while SALT2 favors CDM-like ones. Progress in
determining the transition redshift and/or the present value of the
deceleration parameter depends crucially on solving the issue of the difference
obtained when using these two light-curve fitters.Comment: 25 pages, 9 figure
Bayesian estimation of incomplete data using conditionally specified priors
In this paper, a class of conjugate prior for estimating incomplete count data based on a broad class of conjugate prior distributions is presented. The new class of prior distributions arises from a conditional perspective, making use of the conditional specification methodology and can be considered as the generalisation of the form of prior distributions that have been used previously in the estimation of in- complete count data well. Finally, some examples of simulated and real data are given
Automated classification of periodic variable stars{Improved methodology for the automated classification of periodic variable stars}
We present a novel automated methodology to detect and classify periodic
variable stars in a large database of photometric time series. The methods are
based on multivariate Bayesian statistics and use a multi-stage approach. We
applied our method to the ground-based data of the TrES Lyr1 field, which is
also observed by the Kepler satellite, covering ~26000 stars. We found many
eclipsing binaries as well as classical non-radial pulsators, such as slowly
pulsating B stars, Gamma Doradus, Beta Cephei and Delta Scuti stars. Also a few
classical radial pulsators were found.Comment: 11 pages, 6 figures Accepted for publication in MNRA
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