arXiv subjects

Renate Meyer

Publications and source records attributed to Renate Meyer.

52 records · Page 3Linked to original sources

arXiv2008

A Bayesian method to set upper limits on the strength of a periodic gravitational wave signal from the remnant of SN1987A: possible applications in LIGO searches

We present a method that assesses the theoretical detection limit of a Bayesian Markov chain Monte Carlo search for a periodic gravitational wave signal emitted by a neutron star. Inverse probability yields an upper limit estimate for the strength when a signal could not be detected in an observed data set. The proposed method is based on Bayesian model comparison that automatically quantifies Occam s Razor. It limits the complexity of a model by favoring the most parsimonious model that explains the data. By comparing the model with a signal from a pulsar to the null model that assumes solely noise, we derive the detection probability and an estimate for the upper limit that a search, for example, for a narrow-band emission for SN1987a, might yield on data at the sensitivity of LIGO data for an observation time of one year.

gr-qc↗

arXiv2008

Parameter estimation of spinning binary inspirals using Markov-chain Monte Carlo

We present a Markov-chain Monte-Carlo (MCMC) technique to study the source parameters of gravitational-wave signals from the inspirals of stellar-mass compact binaries detected with ground-based gravitational-wave detectors such as LIGO and Virgo, for the case where spin is present in the more massive compact object in the binary. We discuss aspects of the MCMC algorithm that allow us to sample the parameter space in an efficient way. We show sample runs that illustrate the possibilities of our MCMC code and the difficulties that we encounter.

gr-qc↗

arXiv2007

Report on the second Mock LISA Data Challenge

The Mock LISA Data Challenges are a program to demonstrate LISA data-analysis capabilities and to encourage their development. Each round of challenges consists of several data sets containing simulated instrument noise and gravitational-wave sources of undisclosed parameters. Participants are asked to analyze the data sets and report the maximum information about source parameters. The challenges are being released in rounds of increasing complexity and realism: in this proceeding we present the results of Challenge 2, issued in January 2007, which successfully demonstrated the recovery of signals from supermassive black-hole binaries, from ~20,000 overlapping Galactic white-dwarf binaries, and from the extreme-mass-ratio inspirals of compact objects into central galactic black holes.

gr-qc↗

arXiv2007

Inference on inspiral signals using LISA MLDC data

In this paper we describe a Bayesian inference framework for analysis of data obtained by LISA. We set up a model for binary inspiral signals as defined for the Mock LISA Data Challenge 1.2 (MLDC), and implemented a Markov chain Monte Carlo (MCMC) algorithm to facilitate exploration and integration of the posterior distribution over the 9-dimensional parameter space. Here we present intermediate results showing how, using this method, information about the 9 parameters can be extracted from the data.

gr-qc↗

arXiv2007

Coherent Bayesian analysis of inspiral signals

We present in this paper a Bayesian parameter estimation method for the analysis of interferometric gravitational wave observations of an inspiral of binary compact objects using data recorded simultaneously by a network of several interferometers at different sites. We consider neutron star or black hole inspirals that are modeled to 3.5 post-Newtonian (PN) order in phase and 2.5 PN in amplitude. Inference is facilitated using Markov chain Monte Carlo methods that are adapted in order to efficiently explore the particular parameter space. Examples are shown to illustrate how and what information about the different parameters can be derived from the data. This study uses simulated signals and data with noise characteristics that are assumed to be defined by the LIGO and Virgo detectors operating at their design sensitivities. Nine parameters are estimated, including those associated with the binary system, plus its location on the sky. We explain how this technique will be part of a detection pipeline for binary systems of compact objects with masses up to $20 \sunmass$, including cases where the ratio of the individual masses can be extreme.

gr-qc↗

arXiv2007

Coherent Bayesian inference on compact binary inspirals using a network of interferometric gravitational wave detectors

Presented in this paper is a Markov chain Monte Carlo (MCMC) routine for conducting coherent parameter estimation for interferometric gravitational wave observations of an inspiral of binary compact objects using data from multiple detectors. The MCMC technique uses data from several interferometers and infers all nine of the parameters (ignoring spin) associated with the binary system, including the distance to the source, the masses, and the location on the sky. The Metropolis-algorithm utilises advanced MCMC techniques, such as importance resampling and parallel tempering. The data is compared with time-domain inspiral templates that are 2.5 post-Newtonian (PN) in phase and 2.0 PN in amplitude. Our routine could be implemented as part of an inspiral detection pipeline for a world wide network of detectors. Examples are given for simulated signals and data as seen by the LIGO and Virgo detectors operating at their design sensitivity.

gr-qc↗

arXiv2007

Inference on white dwarf binary systems using the first round Mock LISA Data Challenges data sets

We report on the analysis of selected single source data sets from the first round of the Mock LISA Data Challenges (MLDC) for white dwarf binaries. We implemented an end-to-end pipeline consisting of a grid-based coherent pre-processing unit for signal detection, and an automatic Markov Chain Monte Carlo post-processing unit for signal evaluation. We demonstrate that signal detection with our coherent approach is secure and accurate, and is increased in accuracy and supplemented with additional information on the signal parameters by our Markov Chain Monte Carlo approach. We also demonstrate that the Markov Chain Monte Carlo routine is additionally able to determine accurately the noise level in the frequency window of interest.

gr-qc↗

arXiv2006

Bayesian inference on compact binary inspiral gravitational radiation signals in interferometric data

Presented is a description of a Markov chain Monte Carlo (MCMC) parameter estimation routine for use with interferometric gravitational radiational data in searches for binary neutron star inspiral signals. Five parameters associated with the inspiral can be estimated, and summary statistics are produced. Advanced MCMC methods were implemented, including importance resampling and prior distributions based on detection probability, in order to increase the efficiency of the code. An example is presented from an application using realistic, albeit fictitious, data.

gr-qc↗

arXiv2005

Bayesian modeling of source confusion in LISA data

One of the greatest data analysis challenges for the Laser Interferometer Space Antenna (LISA) is the need to account for a large number of gravitational wave signals from compact binary systems expected to be present in the data. We introduce the basis of a Bayesian method that we believe can address this challenge, and demonstrate its effectiveness on a simplified problem involving one hundred synthetic sinusoidal signals in noise. We use a reversible jump Markov chain Monte Carlo technique to infer simultaneously the number of signals present, the parameters of each identified signal, and the noise level. Our approach therefore tackles the detection and parameter estimation problems simultaneously, without the need to evaluate formal model selection criteria, such as the Akaike Information Criterion or explicit Bayes factors. The method does not require a stopping criterion to determine the number of signals, and produces results which compare very favorably with classical spectral techniques.

gr-qc↗

arXiv2005

A time-domain MCMC search and upper limit technique for gravitational waves of uncertain frequency from a targeted neutron star

It is computationally expensive to search the large parameter space associated with a gravitational wave signal of uncertain frequency, such as might be expected from the possible pulsar generated by SN1987A. To address this difficulty we have developed a Markov Chain Monte Carlo method that performs a time-domain Bayesian search for a signal over a 4 Hz frequency band and a spindown of magnitude of up to $1\ee{-9}$ Hz/s. We use Monte Carlo simulations to set upper limits on signal amplitude with this technique, which we intend to apply to a gravitational wave search.

gr-qc↗

arXiv2005

LISA source confusion: identification and characterization of signals

The Laser Interferometer Space Antenna (LISA) is expected to detect gravitational radiation from a large number of compact binary systems. We present a method by which these signals can be identified and have their parameters estimated. Our approach uses Bayesian inference, specifically the application of a Markov chain Monte Carlo method. The simulation study that we present here considers a large number of sinusoidal signals in noise, and our method estimates the number of periodic signals present in the data, the parameters for these signals and the noise level. The method is significantly better than classical spectral techniques at performing these tasks and does not use stopping criteria for estimating the number of signals present.

gr-qc↗

arXiv2004

Detecting gravitational radiation from neutron stars using a six-parameter adaptive MCMC method

We present a Markov chain Monte Carlo technique for detecting gravitational radiation from a neutron star in laser interferometer data. The algorithm can estimate up to six unknown parameters of the target, including the rotation frequency and frequency derivative, using reparametrization, delayed rejection and simulated annealing. We highlight how a simple extension of the method, distributed over multiple computer processors, will allow for a search over a narrow frequency band. The ultimate goal of this research is to search for sources at a known locations, but uncertain spin parameters, such as may be found in SN1987A.

gr-qc↗

arXiv2004

A Metropolis-Hastings algorithm for extracting periodic gravitational wave signals from laser interferometric detector data

The Markov chain Monte Carlo methods offer practical procedures for detecting signals characterized by a large number of parameters and under conditions of low signal-to-noise ratio. We present a Metropolis-Hastings algorithm capable of inferring the spin and orientation parameters of a neutron star from its periodic gravitational wave signature seen by laser interferometric detectors

gr-qc↗

arXiv2001

II: Bayesian Methods for Cosmological Parameter Estimation from Cosmic Microwave Background Measurements

We present a strategy for a statistically rigorous Bayesian approach to the problem of determining cosmological parameters from the results of observations of anisotropies in the cosmic microwave background. Our strategy relies on Markov chain Monte Carlo methods, specifically the Metropolis-Hastings algorithm, to perform the necessary high-dimensional integrals. We describe the Metropolis-Hastings algorithm in detail and discuss the results of our test on simulated data.

astro-ph↗

arXiv2001

Using Markov chain Monte Carlo methods for estimating parameters with gravitational radiation data

We present a Bayesian approach to the problem of determining parameters for coalescing binary systems observed with laser interferometric detectors. By applying a Markov Chain Monte Carlo (MCMC) algorithm, specifically the Gibbs sampler, we demonstrate the potential that MCMC techniques may hold for the computation of posterior distributions of parameters of the binary system that created the gravity radiation signal. We describe the use of the Gibbs sampler method, and present examples whereby signals are detected and analyzed from within noisy data.

gr-qc↗

arXiv2000

Bayesian Methods for Cosmological Parameter Estimation from Cosmic Microwave Background Measurements

We present a strategy for a statistically rigorous Bayesian approach to the problem of determining cosmological parameters from the results of observations of anisotropies in the cosmic microwave radiation background. We propose the application of Markov chain Monte Carlo methods, specifically the Metropolis-Hastings algorithm, to estimate the parameters. A complete statistical analysis is presented, with the Metropolis-Hastings algorithm described in detail.

astro-ph↗