Detecting the turnover of the power spectrum with SKAO and other surveys

Detecting the turnover of the power spectrum with SKAO and other surveys. This thesis explores the potential of next-generation experiments, specifically the Square Kilometre Array and the Dark Energy Spectroscopic Instrument, to detect and constrain the power spectrum turnover, a key feature of the universe’s large-scale structure holding the key for probing ultra-large scales. Employing intensity mapping and galaxy surveys, we utilize Fisher forecasts and Markov Chain Monte Carlo simulations to model and analyze this subtle cosmological signature. Our findings reveal that SKA-MID Band 1 demonstrates the highest detection significance and Figure of Merit, establishing it as the most promising instrument for this endeavor. However, foregrounds introduce a bias on the detected value of the turnover position. While DESI surveys excel at probing small-scale structures, they effectively complement intensity mapping surveys, crucial for a holistic understanding of the universe’s matter distribution. We address significant challenges such as foreground contamination and analyze the effects of the telescope beam, which can obscure the turnover signal for intensity mapping observations. Our study emphasizes the importance of advanced foreground cleaning techniques and beam-induced bias correction for accurate cosmological analysis. This work confirms the feasibility of detecting the power spectrum turnover with observational tools and provides a critical comparative evaluation of current methodologies across various surveys. The insights discussed lay a robust foundation for future research dedicated to refining our understanding of cosmic evolution and its governing parameters as well as addressing the Hubble tension.

Grantor dc:publisher.institution

University of the Western Cape

Year dc:date.issued

2025

Author dc:creator

• Dube, Yolanda

• SKAO
• Square Kilometre Array
• Energy Spectroscopic Instrument
• Fisher Forecasts
• Markov Chain

Licence

• https://uwcscholar.uwc.ac.za/bitstreams/d6949957-5860-4c2d-b209-35d4b61b354b/download