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Rapid Likelihood Free Inference of Compact Binary Coalescences using Accelerated Hardware

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arxiv 2407.19048 v1 pith:7PJ2Y3IE submitted 2024-07-26 gr-qc astro-ph.IMcs.LG

Deep Chatterjee , Ethan Marx , William Benoit , Ravi Kumar , Malina Desai , Ekaterina Govorkova , Alec Gunny , Eric Moreno
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Rafia Omer Ryan Raikman Muhammed Saleem Shrey Aggarwal Michael W. Coughlin Philip Harris Erik Katsavounidis
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classification gr-qcastro-ph.IMcs.LG
keywords amplfibinarydataacceleratedaframealgorithmcandidatescompact
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Ab Initio Real-Time Gravitational-Wave Parameter Estimation

    gr-qc 2026-07 accept novelty 6.0 of 10

    Slice-within-Gibbs nested sampling on modern GPUs delivers well-calibrated BNS parameter estimation in ~12 minutes uncompressed and ~89 seconds with heterodyning, from cold priors.

  2. Flexible Gravitational-Wave Parameter Estimation with Transformers

    gr-qc 2025-12 conditional novelty 6.0 of 10

    Dingo-T1 is one transformer model that adapts at inference to arbitrary detector subsets and frequency cuts for gravitational-wave parameter estimation.

  3. Progress toward the detection of the gravitational-wave background from stellar-mass binary black holes: a mock data challenge

    gr-qc 2025-06 conditional novelty 6.0 of 10

    A mock data challenge shows that a phase-coherent search for the binary black hole background can recover injected signal fractions in realistic noise, using new treatments of noise uncertainty, finite-duration effect...

  4. A machine learning-enabled search for binary black hole mergers in LIGO-Virgo-KAGRAs third observing run

    astro-ph.IM 2025-05 conditional novelty 5.0 of 10

    Aframe, a neural-network gravitational-wave search, recovers 38 previously known binary black hole mergers from O3 data and finds no new candidates, showing ML pipelines are viable but not yet superior to matched filtering.

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