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timedelay (version 1.0.11)

Time Delay Estimation for Stochastic Time Series of Gravitationally Lensed Quasars

Description

We provide a toolbox to estimate the time delay between the brightness time series of gravitationally lensed quasar images via Bayesian and profile likelihood approaches. The model is based on a state-space representation for irregularly observed time series data generated from a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian method adopts scientifically motivated hyper-prior distributions and a Metropolis-Hastings within Gibbs sampler, producing posterior samples of the model parameters that include the time delay. A profile likelihood of the time delay is a simple approximation to the marginal posterior distribution of the time delay. Both Bayesian and profile likelihood approaches complement each other, producing almost identical results; the Bayesian way is more principled but the profile likelihood is easier to implement. A new functionality is added in version 1.0.9 for estimating the time delay between doubly-lensed light curves observed in two bands. See also Tak et al. (2017) , Tak et al. (2018) , Hu and Tak (2020) .

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Install

install.packages('timedelay')

Monthly Downloads

218

Version

1.0.11

License

GPL-2

Maintainer

Last Published

May 19th, 2020

Functions in timedelay (1.0.11)

simple.band1

Simulated simple data of a doubly-lensed quasar observed in band 1
timedelay-internal

Internal bayesian functions
timedelay

Time Delay Estimation for Stochastic Time Series of Gravitationally Lensed Quasars
bayesian.multiband

Estimating the time delay between doubly-lensed multi-band light curves in a Bayesian way
bayesian

Estimating the time delay via the Bayesian method
entirelogprofilelikelihood

Calculating the entire profilel likelihood curve over the given grid values of the time delay
simple

Simulated simple data of a doubly-lensed quasar
simple.band2

Simulated simple data of a doubly-lensed quasar observed in band 2