In the realm of energy journalism, a recent study conducted by a team of researchers from various institutions, including the University of Manchester, the University of Paris-Saclay, and the University of Toulouse, has shed light on the potential of future Cosmic Microwave Background (CMB) measurements to provide valuable insights into the properties of galaxies during the Epoch of Reionisation. This period, which occurred roughly 13 billion years ago, marked the transition of the universe from a dark, opaque state to a transparent one, illuminated by the first stars and galaxies.
The study, led by Lisa McBride and her colleagues, focuses on the kinetic Sunyaev-Zel’dovich (kSZ) effect, a subtle distortion in the CMB caused by the motion of ionised gas in the early universe. By analyzing the angular power spectrum of this effect, the researchers aim to extract astrophysical and cosmological information from the Epoch of Reionisation. To achieve this, they repurposed the publicly available LoReLi II simulations, which track the evolution of neutral hydrogen during reionisation, to generate a training set of patchy kSZ angular power spectra. They then employed neural network regression to train an emulator, enabling efficient Bayesian inference and forecasts based on mock observations from current and future CMB experiments.
The researchers found that measurements of the kSZ power spectrum from such surveys can provide meaningful constraints on several astrophysical model parameters of the LoReLi II suite. Notably, they expect a 14% relative error in measuring the ionising escape fraction, a crucial parameter that determines the amount of ionising radiation that escapes galaxies and contributes to reionisation. Additionally, the kSZ power spectrum offers an independent measurement of the CMB optical depth, a measure of the scattering of CMB photons by free electrons, with error bars competitive with the cosmic variance limit from large-scale surveys.
The practical applications of this research for the energy sector lie in its potential to enhance our understanding of the early universe and the processes that govern the formation and evolution of galaxies. By probing the properties of reionisation-era galaxies, scientists can gain insights into the mechanisms that drive star formation and the interplay between galaxies and their surrounding intergalactic medium. This knowledge can, in turn, inform our understanding of the energy processes that shape the universe, from the nuclear fusion in stars to the large-scale structure of the cosmos.
Moreover, the development of advanced neural network techniques for analyzing CMB data can have broader implications for data analysis in the energy sector. As the volume and complexity of data continue to grow, machine learning algorithms like those employed in this study can help extract valuable insights and optimize decision-making processes. The research was published in the journal Physical Review D, and it represents a significant step forward in our quest to unravel the mysteries of the early universe and its implications for the energy landscape.
This article is based on research available at arXiv.