Probing the First Stars with the 21-cm Signal: Theory, Methods, and Forecasts

This thesis explores the potential for probing the properties of the first stars, in particular their mass distribution, using the cosmological 21-cm signal. We begin in Part I by introducing the first stars, 21-cm cosmology, and the link between them (Chapter 1), as well as describing in detail the semi-numerical 21-cm signal simulation code 21cmSPACE, used throughout this thesis (Chapter 2). Part II then presents a series of studies into different mechanisms by which the first stars and their mass distribution can impact the 21-cm signal. Chapter 3 considers the Lyman-photon mediated impacts of the first stars, including the Wouthuysen-Field effect, Lyman-α heating, and Lyman-Werner feedback. Chapter 4 investigates how cosmic rays emitted via the supernovae of the first stars can heat the intergalactic medium and imprint distinctive signatures on the 21-cm signal. And finally, Chapter 5 shows that variations in X-ray binary emissivity and abundances enhance the sensitivity of the 21-cm signal to the mass distribution of the first stars. Together, these three chapters construct a comprehensive theoretical model of the impacts of the first star mass distribution on the 21-cm signal. Next, in Part III, we perform a joint analysis of current 21-cm data sets and X-ray background measurements to determine the constraints these observations place on the potential properties of superconducting cosmic strings. Alongside providing insights into an alternative candidate for the first luminous objects in the Universe, this analysis demonstrates the methodology we hope to use to constrain the properties of the first stars using future 21-cm data sets. Part IV then details the headline results of this thesis, forecasts for the prospective constraints on the first star mass distribution from the REACH and SKA-Low 21-cm signal experiments (Chapter 7). Both experiments are found to be able to constrain the mass distribution at > 3σ at their projected sensitivities. Chapter 8 concludes the research content of this thesis with a discussion of a limitation of the Bayesian forecasting techniques employed in Chapter 7 and proposes a novel methodology for fully Bayesian forecasts which addresses this. Lastly, in Part V, we summarize the key conclusions of this thesis and discuss the further research directions these findings motivate.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2024

Author dc:creator

• Gessey-Jones, Thomas

Advisors dc:contributor.advisor

• De Lera Acedo, Eloy
• Fialkov, Anastasia
• Handley, William

• 21-cm Cosmology
• Bayesian Analysis
• Cosmology
• Machine Learning
• Stars

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/29ef82fd-9789-466e-afaa-1888e9d73106/download
• https://www.rioxx.net/licenses/all-rights-reserved/

Language dc:language

eng

Author Identifier

0000-0002-4425-8746

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/371844