Bayesian large-scale structure inference with cosmological velocities and fast radio bursts
File(s)
Author(s)
Prideaux-Ghee, James
Type
Thesis
Abstract
This thesis is concerned with extracting information about cosmology from peculiar velocities,
the motion of objects induced by cosmic matter field inhomogeneities, and fast radio
bursts, short-lived radio signals of unknown origin. The former provide information about the
dark matter peculiar velocity field, while the latter inform about the integrated dark matter
density contrast. I develop upon the Bayesian hierarchical model, BORG in order to constrain
the 3D matter distribution and dynamics by including non-linear physical modelling into the
reconstruction of the local peculiar field for the first time. I demonstrate this method as a
proof-of-concept with simulated data, and apply it to the Second Amendment catalogue of
type-1a supernovae. I study the statistics of the dark matter velocity dispersion tensor, which
is an avenue to study the behaviour of objects undergoing gravitational collapse. In particular,
I investigate how the statistics of the velocity dispersion vary as a function of simulation and
cosmological parameters. I further explore the behaviour of the distributions of the velocity
dispersion conditional on density. These provide a method to include velocity dispersion information
into velocity field reconstructions. Lastly, I explore what cosmological information
can be extracted from existing fast radio burst datasets. For the first time, these bursts are
modelled as existing within an inhomogeneous universe. The method of Bayesian inference
is used to constrain cosmological parameters from a set of localised and a set of unlocalised
fast radio bursts. Furthermore, I demonstrate a proof-of-concept method for constraining the
dark matter initial conditions using these bursts, finding that we will need at least three orders
of magnitude more data than currently exists. I also investigate using these bursts to augment
conventional datasets used in initial conditions inference, demonstrating that, with sufficient
bursts, their addition can improve our knowledge of the initial conditions.
the motion of objects induced by cosmic matter field inhomogeneities, and fast radio
bursts, short-lived radio signals of unknown origin. The former provide information about the
dark matter peculiar velocity field, while the latter inform about the integrated dark matter
density contrast. I develop upon the Bayesian hierarchical model, BORG in order to constrain
the 3D matter distribution and dynamics by including non-linear physical modelling into the
reconstruction of the local peculiar field for the first time. I demonstrate this method as a
proof-of-concept with simulated data, and apply it to the Second Amendment catalogue of
type-1a supernovae. I study the statistics of the dark matter velocity dispersion tensor, which
is an avenue to study the behaviour of objects undergoing gravitational collapse. In particular,
I investigate how the statistics of the velocity dispersion vary as a function of simulation and
cosmological parameters. I further explore the behaviour of the distributions of the velocity
dispersion conditional on density. These provide a method to include velocity dispersion information
into velocity field reconstructions. Lastly, I explore what cosmological information
can be extracted from existing fast radio burst datasets. For the first time, these bursts are
modelled as existing within an inhomogeneous universe. The method of Bayesian inference
is used to constrain cosmological parameters from a set of localised and a set of unlocalised
fast radio bursts. Furthermore, I demonstrate a proof-of-concept method for constraining the
dark matter initial conditions using these bursts, finding that we will need at least three orders
of magnitude more data than currently exists. I also investigate using these bursts to augment
conventional datasets used in initial conditions inference, demonstrating that, with sufficient
bursts, their addition can improve our knowledge of the initial conditions.
Version
Open Access
Date Issued
2023-08
Date Awarded
2024-02
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
Advisor
Heavens, Alan
Leclercq, Florent
Sponsor
Science and Technology Facilities Council (Great Britain)
Publisher Department
Physics
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)