scholarly journals A Markov chain Monte Carlo approach for measurement of jet precession in radio-loud active galactic nuclei

2020 ◽  
Vol 493 (3) ◽  
pp. 3911-3919 ◽  
Author(s):  
Maya A Horton ◽  
Martin J Hardcastle ◽  
Shaun C Read ◽  
Martin G H Krause
Keyword(s):  
Black Holes ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Active Galactic Nuclei ◽  
Galaxy Evolution ◽  
Binary Systems ◽  
Galactic Nuclei ◽  
Posterior Probability Distribution ◽  
Cygnus A

ABSTRACT Jet precession can reveal the presence of binary systems of supermassive black holes. The ability to accurately measure the parameters of jet precession from radio-loud active galactic nuclei is important for constraining the binary supermassive black hole population, which is expected as a result of hierarchical galaxy evolution. The age, morphology, and orientation along the line of sight of a given source often result in uncertainties regarding the jet path. This paper presents a new approach for efficient determination of precession parameters using a two-dimensional Markov chain Monte Carlo curve-fitting algorithm that provides us a full posterior probability distribution on the fitted parameters. Applying the method to Cygnus A, we find evidence for previous suggestions that the source is precessing. Interpreting in the context of binary black holes leads to a constraint of parsec scale and likely sub-parsec orbital separation for the putative supermassive binary.

scholarly journals Fitting Spectral Energy Distributions of AGN A Markov Chain Monte Carlo Approach

2013 ◽  
Vol 9 (S304) ◽  
pp. 228-229
Author(s):  
Gabriela Calistro Rivera ◽  
Elisabeta Lusso ◽  
Joseph F. Hennawi ◽  
David W. Hogg
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Galactic Nuclei ◽  
Physical Models ◽  
Monte Carlo Algorithm ◽  
Spectral Energy ◽  
Spectral Energy Distributions ◽  
Energy Distributions ◽  
Multiple Parameters

AbstractWe present AGNfitter: a Markov Chain Monte Carlo algorithm developed to fit the spectral energy distributions (SEDs) of active galactic nuclei (AGN) with different physical models of AGN components. This code is well suited to determine in a robust way multiple parameters and their uncertainties, which quantify the physical processes responsible for the panchromatic nature of active galaxies and quasars. We describe the technicalities of the code and test its capabilities in the context of X-ray selected obscured AGN using multiwavelength data from the XMM-COSMOS survey.

scholarly journals Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo

2019 ◽  
Author(s):  
Mohamadreza Fazel ◽  
Michael J. Wester ◽  
Hanieh Mazloom-Farsibaf ◽  
Marjolein B. M. Meddens ◽  
Alexandra Eklund ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Single Molecule ◽  
Posterior Probability ◽  
Super Resolution ◽  
Image Data ◽  
Reversible Jump ◽  
Posterior Probability Distribution ◽  
Resolution Imaging

In single molecule localization-based super-resolution imaging, high labeling density or the desire for greater data collection speed can lead to clusters of overlapping emitter images in the raw super-resolution image data. We describe a Bayesian inference approach to multiple-emitter fitting that uses Reversible Jump Markov Chain Monte Carlo to identify and localize the emitters in dense regions of data. This formalism can take advantage of any prior information, such as emitter intensity and density. The output is both a posterior probability distribution of emitter locations that includes uncertainty in the number of emitters and the background structure, and a set of coordinates and uncertainties from the most probable model.

Influence of Black Hole Spin on the Shape of the Fe Kα Spectral Line: The Case of 3C 405

2011 ◽  
Vol 20 (3) ◽  
Author(s):  
P. Jovanović ◽  
V. Borka Jovanović ◽  
D. Borka
Keyword(s):  
Black Holes ◽  
Black Hole ◽  
Active Galactic Nuclei ◽  
Accretion Disks ◽  
Galactic Nuclei ◽  
Kerr Metric ◽  
Line Shapes ◽  
Cygnus A ◽  
Angular Momenta ◽  
Innermost Region

AbstractHere we analyze how the angular momenta (spins) of black holes in the centers of Active Galactic Nuclei (AGN) affect the shape of the FeK line emitted from their accretion disks. For that purpose, we compared the observed line profile in the case of radio galaxy 3C 405 (Cygnus A) with its profiles, obtained by numerical simulations based on ray-tracing method in the Kerr metric. Our results show that the spins of rotating central black holes of AGN have significant influence on their FeKα line shapes. Also, we found that in the case of Cygnus A the observed line is probably emitted from the innermost region of its slightly inclined accretion disk around very slowly rotating or even stationary central black hole.

scholarly journals Solving for the Orbital Elements of Binary Systems using MCMC Simulations.

2013 ◽  
Vol 8 (S299) ◽  
pp. 52-53
Author(s):  
Kyle Mede ◽  
Timothy D. Brandt
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Observational Data ◽  
Confidence Intervals ◽  
Formation Mechanism ◽  
Binary Systems ◽  
Three Dimensional ◽  
Orbital Elements ◽  
Orbital Parameters

AbstractRecent simulation and observational data have been used to investigate the ability of Kozai oscillations to explain the formation of “hot Jupiter” planetary systems. One of the first exoplanets discovered, τ Boo Ab, orbits a star with a binary companion, making it an excellent testbed for this scenario. We have written a three-dimensional Markov Chain Monte Carlo (MCMC) simulator to constrain the orbit of the distant stellar companion τ Boo B, and are currently deriving orbital parameters and confidence intervals. These orbital parameters will confirm or reject Kozai oscillations as a plausible formation mechanism for τ Boo Ab.

scholarly journals Radio and X-ray detections of GX 339–4 in quiescence using MeerKAT and Swift

2020 ◽  
Vol 493 (1) ◽  
pp. L132-L137 ◽  
Author(s):  
E Tremou ◽  
S Corbel ◽  
R P Fender ◽  
P A Woudt ◽  
J C A Miller-Jones ◽  
...  
Keyword(s):  
Black Holes ◽  
Black Hole ◽  
Active Galactic Nuclei ◽  
Binary Systems ◽  
Accretion Rate ◽  
Galactic Nuclei ◽  
Stellar Mass ◽  
X Ray ◽  
Accretion Process ◽  
Upper Limits

ABSTRACT The radio–X-ray correlation that characterizes accreting black holes at all mass scales – from stellar mass black holes in binary systems to supermassive black holes powering active galactic nuclei – is one of the most important pieces of observational evidence supporting the existence of a connection between the accretion process and the generation of collimated outflows – or jets – in accreting systems. Although recent studies suggest that the correlation extends down to low luminosities, only a handful of stellar mass black holes have been clearly detected, and in general only upper limits (especially at radio wavelengths) can be obtained during quiescence. We recently obtained detections of the black hole X-ray binary (XRB) GX 339–4 in quiescence using the Meer Karoo Array Telescope (MeerKAT) radio telescope and Swift X-ray Telescope instrument on board the Neil Gehrels Swift Observatory, probing the lower end of the radio–X-ray correlation. We present the properties of accretion and of the connected generation of jets in the poorly studied low-accretion rate regime for this canonical black hole XRB system.

scholarly journals Nonlinear Parameter Estimation: Comparison of an Ensemble Kalman Smoother with a Markov Chain Monte Carlo Algorithm

2012 ◽  
Vol 140 (6) ◽  
pp. 1957-1974 ◽  
Author(s):  
Derek J. Posselt ◽  
Craig H. Bishop
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Parameter Uncertainty ◽  
Posterior Probability ◽  
Monte Carlo Algorithm ◽  
Parameter Estimates ◽  
Posterior Probability Distribution ◽  
Kalman Smoother ◽  
Non Gaussian

Abstract This paper explores the temporal evolution of cloud microphysical parameter uncertainty using an idealized 1D model of deep convection. Model parameter uncertainty is quantified using a Markov chain Monte Carlo (MCMC) algorithm. A new form of the ensemble transform Kalman smoother (ETKS) appropriate for the case where the number of ensemble members exceeds the number of observations is then used to obtain estimates of model uncertainty associated with variability in model physics parameters. Robustness of the parameter estimates and ensemble parameter distributions derived from ETKS is assessed via comparison with MCMC. Nonlinearity in the relationship between parameters and model output gives rise to a non-Gaussian posterior probability distribution for the parameters that exhibits skewness early and multimodality late in the simulation. The transition from unimodal to multimodal posterior probability density function (PDF) reflects the transition from convective to stratiform rainfall. ETKS-based estimates of the posterior mean are shown to be robust, as long as the posterior PDF has a single mode. Once multimodality manifests in the solution, the MCMC posterior parameter means and variances differ markedly from those from the ETKS. However, it is also shown that if the ETKS is given a multimode prior ensemble, multimodality is preserved in the ETKS posterior analysis. These results suggest that the primary limitation of the ETKS is not the inability to deal with multimodal, non-Gaussian priors. Rather it is the inability of the ETKS to represent posterior perturbations as nonlinear functions of prior perturbations that causes the most profound difference between MCMC posterior PDFs and ETKS posterior PDFs.

scholarly journals Joining forces of Bayesian and frequentist methodology: a study for inference in the presence of non-identifiability

2013 ◽  
Vol 371 (1984) ◽  
pp. 20110544 ◽  
Author(s):  
Andreas Raue ◽  
Clemens Kreutz ◽  
Fabian Joachim Theis ◽  
Jens Timmer
Keyword(s):  
Experimental Data ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Probability Distribution ◽  
Cell Biology ◽  
Posterior Probability ◽  
Monte Carlo Sampling ◽  
Posterior Probability Distribution ◽  
Model Predictions

Increasingly complex applications involve large datasets in combination with nonlinear and high-dimensional mathematical models. In this context, statistical inference is a challenging issue that calls for pragmatic approaches that take advantage of both Bayesian and frequentist methods. The elegance of Bayesian methodology is founded in the propagation of information content provided by experimental data and prior assumptions to the posterior probability distribution of model predictions. However, for complex applications, experimental data and prior assumptions potentially constrain the posterior probability distribution insufficiently. In these situations, Bayesian Markov chain Monte Carlo sampling can be infeasible. From a frequentist point of view, insufficient experimental data and prior assumptions can be interpreted as non-identifiability. The profile-likelihood approach offers to detect and to resolve non-identifiability by experimental design iteratively. Therefore, it allows one to better constrain the posterior probability distribution until Markov chain Monte Carlo sampling can be used securely. Using an application from cell biology, we compare both methods and show that a successive application of the two methods facilitates a realistic assessment of uncertainty in model predictions.

scholarly journals Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mohamadreza Fazel ◽  
Michael J. Wester ◽  
Hanieh Mazloom-Farsibaf ◽  
Marjolein B. M. Meddens ◽  
Alexandra S. Eklund ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Single Molecule ◽  
Posterior Probability ◽  
Super Resolution ◽  
Image Data ◽  
Reversible Jump ◽  
Posterior Probability Distribution ◽  
Resolution Imaging

Abstract In single molecule localization-based super-resolution imaging, high labeling density or the desire for greater data collection speed can lead to clusters of overlapping emitter images in the raw super-resolution image data. We describe a Bayesian inference approach to multiple-emitter fitting that uses Reversible Jump Markov Chain Monte Carlo to identify and localize the emitters in dense regions of data. This formalism can take advantage of any prior information, such as emitter intensity and density. The output is both a posterior probability distribution of emitter locations that includes uncertainty in the number of emitters and the background structure, and a set of coordinates and uncertainties from the most probable model.

Sign in / Sign up

Export Citation Format

Share Document