Related papers: Transit modelling of selected Kepler systems
We present a fast Markov Chain Monte-Carlo exploration of cosmological parameter space. We perform a joint analysis of results from recent CMB experiments and provide parameter constraints, including sigma_8, from the CMB independent of…
In the first part of this paper we study approximations of trajectories of Piecewise Deter-ministic Processes (PDP) when the flow is not explicit by the thinning method. We also establish a strong error estimate for PDPs as well as a weak…
Kepler provides light curves of 156,000 stars with unprecedented precision. However, the raw data as they come from the spacecraft contain significant systematic and stochastic errors. These errors, which include discontinuities, systematic…
Adaptive and interacting Markov chain Monte Carlo algorithms (MCMC) have been recently introduced in the literature. These novel simulation algorithms are designed to increase the simulation efficiency to sample complex distributions.…
We fit a dynamical model to Kepler systems that contain four or more transiting planets using the analytic method AnalyticLC, and obtain physical and orbital parameters for 101 planets in 23 systems, of which 95 are of mass significance…
Several studies have shown that stellar activity features, such as occulted and non-occulted starspots, can affect the measurement of transit parameters biasing studies of transit timing variations and transmission spectra. We present…
Very precise measurements of exoplanet transit lightcurves both from ground and space based observatories make it now possible to fit the limb-darkening coefficients in the transit-fitting procedure rather than fix them to theoretical…
Markov chain Monte Carlo (MCMC) sampling of posterior distributions arising in Bayesian inverse problems is challenging when evaluations of the forward model are computationally expensive. Replacing the forward model with a low-cost,…
Recently there has been a renewed interest in the power-2 limb darkening law for modeling exoplanet transits. This law provides a better match to the intensities generated by spherical stellar atmosphere models than other 2-parameter laws.…
We present a model-independent technique for calculating the time of mid-transits. This technique, named "barycenter method", uses the light-curve's symmetry to determine the transit timing by calculating the transit light-curve barycenter.…
With years of Kepler data currently available, it can now be attempted to measure variations in planetary transit depths over time. To do so, it is of primary importance to understand which systematic effects may affect the measurement of…
We present a framework to conservatively estimate the probability that any particular planet-like transit signal observed by the Kepler mission is in fact a planet, prior to any ground-based follow-up efforts. We use Monte Carlo methods…
We perform dynamical fits to the Transit Timing Variations (TTVs) of Kepler-419b. The TTVs from 17 Kepler quarters are obtained from Holczer et al (2016, ApJS 225,9). The dynamical fits are performed using the MultiNest Bayesian inference…
We develop and apply methods to extract planet masses and eccentricities from observed transit time variations (TTVs). First, we derive simple analytic expressions for the TTV that include the effects of both first- and second-order…
Quasi-Monte Carlo (QMC) methods for estimating integrals are attractive since the resulting estimators typically converge at a faster rate than pseudo-random Monte Carlo. However, they can be difficult to set up on arbitrary posterior…
We present ground-based observations from the Discovery Channel Telescope (DCT) of three transits of Kepler-445c---a supposed super-Earth exoplanet with properties resembling GJ 1214b---and demonstrate that the transit depth is…
In the era of Big Data, Markov chain Monte Carlo (MCMC) methods, which are currently essential for Bayesian estimation, face significant computational challenges owing to their sequential nature. To achieve a faster and more effective…
We present a novel method for reducing the computational complexity of rigorously estimating the partition functions (normalizing constants) of Gibbs (Boltzmann) distributions, which arise ubiquitously in probabilistic graphical models. A…
We introduce a new Markov Chain Monte Carlo (MCMC) algorithm with parallel tempering for fitting theoretical models of horizon-scale images of black holes to the interferometric data from the Event Horizon Telescope (EHT). The algorithm…
A common tool in the practice of Markov Chain Monte Carlo is to use approximating transition kernels to speed up computation when the desired kernel is slow to evaluate or intractable. A limited set of quantitative tools exist to assess the…