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Simulation Based Inference of BNS Kilonova Properties A Case Study with AT2017gfo

PA Darc, CR Bom, BMO Fraga, CD Kilpatrick - arXiv preprint arXiv …, 2023 - arxiv.org
Education paper astro-ph.HE Suggest

… We propose here to use Simulation-based Inference (SBI) techniques to infer the physical parameters of BNS kilonovae from their spectra, using simulations produced …

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BibTeX

@article{2311.09471v2,
Author        = {P. Darc and Clecio R. Bom and Bernardo M. O. Fraga and Charlie D. Kilpatrick},
Title         = {Kilonova Spectral Inverse Modelling with Simulation-Based Inference: An
  Amortized Neural Posterior Estimation Analysis},
Eprint        = {2311.09471v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.HE},
Abstract      = {Kilonovae represent a category of astrophysical transients, identifiable as
the electromagnetic observable counterparts associated with the coalescence
events of binary systems comprising neutron stars and neutron star-black hole
pairs. They act as probes for heavy-element nucleosynthesis in astrophysical
environments. These studies rely on inference of the physical parameters (e.g.,
ejecta mass, velocity, composition) that describe kilonovae based on
electromagnetic observations. This is a complex inverse problem typically
addressed with sampling-based methods such as Markov-chain Monte Carlo (MCMC)
or nested sampling algorithms. However, repeated inferences can be
computationally expensive due to the sequential nature of these methods. This
poses a significant challenge to ensuring the reliability and statistical
validity of the posterior approximations and, thus, the inferred kilonova
parameters themselves. We present a novel approach: Simulation-Based Inference
(SBI) using simulations produced by KilonovaNet. Our method employs an ensemble
of Amortized Neural Posterior Estimation (ANPE) with an embedding network to
directly predict posterior distributions from simulated spectral energy
distributions (SEDs). We take advantage of the quasi-instantaneous inference
time of ANPE to demonstrate the reliability of our posterior approximations
using diagnostics tools, including coverage diagnostic and posterior predictive
checks. We further test our model with real observations from AT2017gfo, the
only kilonova with multi-messenger data, demonstrating agreement with previous
likelihood-based methods while reducing inference time down to a few seconds.
The inference results produced by ANPE appear to be conservative and reliable,
paving the way for testable and more efficient kilonova parameter inference.},
Year          = {2023},
Month         = {Nov},
Url           = {http://arxiv.org/abs/2311.09471v2},
File          = {2311.09471v2.pdf}
}

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