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Constraining the dispersion measure redshift relation with simulation-based inference

K Konar, R Reischke, S Hagstotz, A Nicola… - arXiv preprint arXiv …, 2024 - arxiv.org
Astrophysics paper astro-ph.CO Suggest

… In this paper, we want to tackle these issues and present simulation-based inference (SBI) of cosmological and astrophysical models via the DM-𝑧 relation of FRBs. SBI, …

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BibTeX

@article{2410.07084v2,
Author        = {Koustav Konar and Robert Reischke and Steffen Hagstotz and Andrina Nicola and Hendrik Hildebrandt},
Title         = {Constraining the dispersion measure redshift relation with
  simulation-based inference},
Eprint        = {2410.07084v2},
DOI           = {10.33232/001c.142524},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {We use the dispersion measure (DM) of localised Fast Radio Bursts (FRBs) to
constrain cosmological and host galaxy parameters using simulation-based
inference (SBI) for the first time. By simulating the large-scale structure of
the electron density with the Generator for Large-Scale Structure (GLASS), we
generate log-normal realisations of the free electron density field, accurately
capturing the correlations between different FRBs. For the host galaxy
contribution, we rigorously test various models, including log-normal,
truncated Gaussian and Gamma distributions, while modelling the Milky Way
component using pulsar data. Through these simulations, we employ the truncated
sequential neural posterior estimation method to obtain the posterior. Using
current observational data, we successfully recover the amplitude of the
DM-redshift relation, consistent with Planck, while also fitting both the mean
host contribution and its shape. Notably, we find no clear preference for a
specific model of the host galaxy contribution. Although SBI may not yet be
strictly necessary for FRB inference, this work lays the groundwork for the
future, as the increasing volume of FRB data will demand precise modelling of
both the host and large-scale structure components. Our modular simulation
pipeline offers flexibility, allowing for easy integration of improved models
as they become available, ensuring scalability and adaptability for upcoming
analyses using FRBs. The pipeline is made publicly available under
https://github.com/koustav-konar/FastNeuralBurst.},
Year          = {2024},
Month         = {Oct},
Note          = {Open Journal of Astrophysics. vol 8 (2025)},
Url           = {http://arxiv.org/abs/2410.07084v2},
File          = {2410.07084v2.pdf}
}

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