BibTeX

@article{2509.13842v1,
Author        = {Iván Martín Vílchez and Carlos F. Sopuerta},
Title         = {Simulation-based Inference of Massive Black Hole Binaries using
  Sequential Neural Likelihood},
Eprint        = {2509.13842v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {gr-qc},
Abstract      = {We propose a machine learning-based approach for parameter estimation of
Massive Black Hole Binaries (MBHBs), leveraging normalizing flows to
approximate the likelihood function. By training these flows on simulated data,
we can generate posterior samples via Markov Chain Monte Carlo with a
relatively reduced computational cost. Our method enables iterative refinement
of smaller models targeting specific MBHB events, with significantly fewer
waveform template evaluations. However, dimensionality reduction is crucial to
make the method computationally feasible: it dictates both the quality and time
efficiency of the method. We present initial results for a single MBHB with
Gaussian noise and aim to extend our work to increasingly realistic scenarios,
including waveforms with higher modes, non-stationary noise, glitches, and data
gaps.},
Year          = {2025},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2509.13842v1},
File          = {2509.13842v1.pdf}
}