Baseline-Aware Dependence fitting for DAmping Timescales (BADDAT) is a lightweight toolkit for forward modeling and statistical inference of AGN variability timescales and their dependence on physical parameters. It is particularly useful for addressing biases in DRW timescale measurements and exploring correlations with properties such as black hole mass, luminosity, and wavelength.
import BADDAT
flat_samples = BADDAT.DependenceFitter1(tau, baseline, n_cadence, log_M_BH).fit()
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See demo.ipynb for a toy example demonstrating basic usage.
This project makes use of and adapts portions of code from open-source libraries taufit by Colin J. Burke – parts of the functions for DRW likelihood evaluation and modeling were referenced and customized for this toolkit.
We thank the authors for making their work openly available.