Continuous-Time Modelling of Black Hole Binary Evolution with Neural ODEs
Julian Chan, Alessia Gualandris, Payel Das

TL;DR
This paper introduces a neural ODE-based surrogate model trained on N-body simulations to efficiently predict the long-term evolution and merger timescales of black hole binaries, aiding gravitational wave background analysis.
Contribution
The paper presents a parameterized neural ODE model conditioned on initial parameters, accurately emulating BHB evolution across a range of conditions, reducing computational costs compared to direct N-body simulations.
Findings
PNODE accurately reproduces orbital evolution metrics.
The model generalizes modestly to unseen high-resolution cases.
Predicted merger timescales align with direct simulation results.
Abstract
Pulsar timing arrays (PTAs) can detect the low-frequency stochastic gravitational-wave background (GWB) generated by an ensemble of supermassive black hole binaries (BHBs). Accurate determination of BHB merger timescales is essential for interpreting GWBs and constraining key astrophysical quantities such as black hole (BH) occupation fractions and galaxy coalescence rates. High-accuracy -body codes such as \texttt{Griffin} can resolve sub-pc BHB dynamics but are too costly to explore a wide range of initial conditions, motivating the need for surrogate models that emulate their long-term evolution at much lower computational cost. We investigate neural ordinary differential equations (NODEs) as surrogates for the secular orbital evolution of BHBs. Our primary contribution is a parameterised NODE (PNODE) trained on an ensemble of -body simulations of galaxy mergers spanning a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPulsars and Gravitational Waves Research · Statistical Mechanics and Entropy · Gamma-ray bursts and supernovae
