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[ascl:2107.012] PyROA: Modeling quasar light curves

PyROA models quasar light curves where the variability is described using a running optimal average (ROA), and parameters are sampled using Markov Chain Monte Carlo (MCMC) techniques using emcee (ascl:1303.002). Using a Bayesian approach, priors can be used on the sampled parameters. Currently it has three main uses: 1.) Determining the time delay between lightcurves at different wavelengths; 2.) Intercalibrating light curves from multiple telescopes, merging them into a single lightcurve; and 3.) Determining the time delay between images of lensed quasars, where the microlensing effects are also modeled. PyROA also includes a noise model, where there is a parameter for each light curve that adds extra variance to the flux measurments, to account for underestimated errors; this can be turned off if required. Example jupyter notebooks that demonstrate each of the three main uses of the code are provided.

Code site:
https://github.com/FergusDonnan/PyROA
Used in:
https://ui.adsabs.harvard.edu/abs/2021arXiv210712318D
Bibcode:
2021ascl.soft07012D

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ascl:2107.012
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