EMRI parameter inference no longer needs a true-value start: a neural network narrows the search, and a six-month signal yields posteriors in about ten hours
Bayesian parameter inference for extreme-mass-ratio inspirals (EMRIs) has relied on narrow priors centered on the true parameters or Markov chains initialized near them, information real observations do not provide. A deep-learning-assisted framework first uses a neural network to produce approximate posteriors from the data and shrink the search region, then particle swarm optimization finds a high-likelihood starting point, and staged Markov chain Monte Carlo sampling produces the final posteriors. On two noise-free six-month simulated signals, the resulting posteriors agree closely with reference analyses initialized near the true values, and once the network is trained each signal takes about ten hours.