scholarly journals Kepler Object of Interest Network

2018 ◽  
Vol 618 ◽  
pp. A41 ◽  
Author(s):  
J. Freudenthal ◽  
C. von Essen ◽  
S. Dreizler ◽  
S. Wedemeyer ◽  
E. Agol ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Comprehensive Analysis ◽  
Time Span ◽  
Monte Carlo Algorithm ◽  
High Latitudes ◽  
Low Latitudes ◽  
Curve Model ◽  
Site Network

Context. The Kepler Object of Interest Network (KOINet) is a multi-site network of telescopes around the globe organised to follow up transiting planet-candidate Kepler objects of interest (KOIs) with large transit timing variations (TTVs). Its main goal is to complete their TTV curves, as the Kepler telescope no longer observes the original Kepler field. Aims. Combining Kepler and new ground-based transit data we improve the modelling of these systems. To this end, we have developed a photodynamical model, and we demonstrate its performance using the Kepler-9 system as an example. Methods. Our comprehensive analysis combines the numerical integration of the system’s dynamics over the time span of the observations along with the transit light curve model. This provides a coherent description of all observations simultaneously. This model is coupled with a Markov chain Monte Carlo algorithm, allowing for the exploration of the model parameter space. Results. Applied to the Kepler-9 long cadence data, short cadence data, and 13 new transit observations collected by KOINet between the years 2014 and 2017, our modelling provides well constrained predictions for the next transits and the system’s parameters. We have determined the densities of the planets Kepler-9b and 9c to the very precise values of ρb = 0.439 ± 0.023 g cm−3 and ρc = 0.322 ± 0.017 g cm−3. Our analysis reveals that Kepler-9c will stop transiting in about 30 yr due to strong dynamical interactions between Kepler-9b and 9c, near 2:1 resonance, leading to a periodic change in inclination. Conclusions. Over the next 30 years, the inclination of Kepler-9c (-9b) will decrease (increase) slowly. This should be measurable by a substantial decrease (increase) in the transit duration, in as soon as a few years’ time. Observations that contradict this prediction might indicate the presence of additional objects in this system. If this prediction turns out to be accurate, this behaviour opens up a unique chance to scan the different latitudes of a star: high latitudes with planet c and low latitudes with planet b.

scholarly journals Kepler Object of Interest Network

2019 ◽  
Vol 628 ◽  
pp. A108 ◽  
Author(s):  
J. Freudenthal ◽  
C. von Essen ◽  
A. Ofir ◽  
S. Dreizler ◽  
E. Agol ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Time Span ◽  
Monte Carlo Algorithm ◽  
Outer Planet ◽  
Dynamical Interaction ◽  
Curve Model ◽  
Site Network

Context. The Kepler Object of Interest Network (KOINet) is a multi-site network of telescopes around the globe organised for follow-up observations of transiting planet candidate Kepler objects of interest with large transit timing variations (TTVs). The main goal of KOINet is the completion of their TTV curves as the Kepler telescope stopped observing the original Kepler field in 2013. Aims. We ensure a comprehensive characterisation of the investigated systems by analysing Kepler data combined with new ground-based transit data using a photodynamical model. This method is applied to the Kepler-82 system leading to its first dynamic analysis. Methods. In order to provide a coherent description of all observations simultaneously, we combine the numerical integration of the gravitational dynamics of a system over the time span of observations with a transit light curve model. To explore the model parameter space, this photodynamical model is coupled with a Markov chain Monte Carlo algorithm. Results. The Kepler-82b/c system shows sinusoidal TTVs due to their near 2:1 resonance dynamical interaction. An additional chopping effect in the TTVs of Kepler-82c hints to a further planet near the 3:2 or 3:1 resonance. We photodynamically analysed Kepler long- and short-cadence data and three new transit observations obtained by KOINet between 2014 and 2018. Our result reveals a non-transiting outer planet with a mass of mf = 20.9 ± 1.0 M⊕ near the 3:2 resonance to the outermost known planet, Kepler-82c. Furthermore, we determined the densities of planets b and c to the significantly more precise values ρb = 0.98−0.14+0.10 g cm−3 and ρc = 0.494−0.077+0.066 g cm−3.

scholarly journals Estimation of trace gas fluxes with objectively determined basis functions using reversible-jump Markov chain Monte Carlo

2016 ◽  
Vol 9 (9) ◽  
pp. 3213-3229 ◽  
Author(s):  
Mark F. Lunt ◽  
Matt Rigby ◽  
Anita L. Ganesan ◽  
Alistair J. Manning
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Parameter Space ◽  
Spatial Domain ◽  
Monte Carlo Algorithm ◽  
Trace Gas ◽  
Reversible Jump ◽  
Pseudo Data ◽  
Atmospheric Trace Gas

Abstract. Atmospheric trace gas inversions often attempt to attribute fluxes to a high-dimensional grid using observations. To make this problem computationally feasible, and to reduce the degree of under-determination, some form of dimension reduction is usually performed. Here, we present an objective method for reducing the spatial dimension of the parameter space in atmospheric trace gas inversions. In addition to solving for a set of unknowns that govern emissions of a trace gas, we set out a framework that considers the number of unknowns to itself be an unknown. We rely on the well-established reversible-jump Markov chain Monte Carlo algorithm to use the data to determine the dimension of the parameter space. This framework provides a single-step process that solves for both the resolution of the inversion grid, as well as the magnitude of fluxes from this grid. Therefore, the uncertainty that surrounds the choice of aggregation is accounted for in the posterior parameter distribution. The posterior distribution of this transdimensional Markov chain provides a naturally smoothed solution, formed from an ensemble of coarser partitions of the spatial domain. We describe the form of the reversible-jump algorithm and how it may be applied to trace gas inversions. We build the system into a hierarchical Bayesian framework in which other unknown factors, such as the magnitude of the model uncertainty, can also be explored. A pseudo-data example is used to show the usefulness of this approach when compared to a subjectively chosen partitioning of a spatial domain. An inversion using real data is also shown to illustrate the scales at which the data allow for methane emissions over north-west Europe to be resolved.

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.

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