Rapid Likelihood Free Inference of Compact Binary Coalescences using Accelerated Hardware
<|reference_start|>Rapid Likelihood Free Inference of Compact Binary Coalescences using Accelerated Hardware: We report a gravitational-wave parameter estimation algorithm, AMPLFI, based on likelihood-free inference using normalizing flows. The focus of AMPLFI is to perform real-time parameter estimation for candidates detected by machine-learning based compact binary coalescence search, Aframe. We present details of our algorithm and optimizations done related to data-loading and pre-processing on accelerated hardware. We train our model using binary black-hole (BBH) simulations on real LIGO-Virgo detector noise. Our model has $\sim 6$ million trainable parameters with training times $\lesssim 24$ hours. Based on online deployment on a mock data stream of LIGO-Virgo data, Aframe + AMPLFI is able to pick up BBH candidates and infer parameters for real-time alerts from data acquisition with a net latency of $\sim 6$s.<|reference_end|>
@article{chatterjee2024rapid,
    title={Rapid Likelihood Free Inference of Compact Binary Coalescences using
  Accelerated Hardware},
    author={Deep Chatterjee and Ethan Marx and William Benoit and Ravi Kumar and
  Malina Desai and Ekaterina Govorkova and Alec Gunny and Eric Moreno and Rafia
  Omer and Ryan Raikman and Muhammed Saleem and Shrey Aggarwal and Michael W.
  Coughlin and Philip Harris and Erik Katsavounidis},
    journal={arXiv preprint arXiv:2407.19048},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19048},
    primaryClass={gr-qc astro-ph.IM cs.LG}
}