Probing the Epoch of Reionization using synergies of line intensity mapping

TL;DR

This review discusses recent advances in line intensity mapping techniques to probe the Epoch of Reionization, emphasizing synergies among different tracers and highlighting experimental progress and future directions.

Contribution

It provides a comprehensive overview of the latest methods, including modeling, machine learning, and synergy strategies, with a focus on the Indian community's contributions to EoR studies.

Findings

Upper limits on [H I] 21cm power spectrum established

Synergy techniques improve EoR signal detection

Upcoming experiments aim to address current challenges

Abstract

The Epoch of Reionization (EoR) remains a poorly understood cosmic era for the most part. Yet, efforts are still going on to probe and understand this epoch. We present a review of the latest developments in the techniques (especially line-intensity mapping) to study the EoR and try to highlight the contribution of the Indian community in this field. Line-emissions like [H I]$_{\rm 21cm}$, Lyman-$\alpha$, [C II]$_{\text{158}\mu\text{m}}$ and their role as tracers in probing the EoR are discussed. While the [H I]$_{\rm 21cm}$ is an excellent probe of the early IGM, the others are mainly targeted to do an unresolved and large-scale survey of the reionizing sources. Techniques to model these signals include simulations and machine learning approaches, along with the challenge to tackle foregrounds or interlopers. We also discuss synergy opportunities among the various tracers that we mention. Synergy addresses different aspects of the problem, which otherwise is difficult or impossible to tackle. They include statistics like cross-power spectrum, cross-bispectrum, and other techniques such as follow-up studies. We present updates on the relevant experiments; these include the upper limits on the [H I]$_{\rm 21cm}$ power spectrum, along with some highlights on high-redshift galaxy surveys. Finally, we highlight what can be improved further within the community: applying machine learning and simulations based on hydrodynamic and radiative-transfer techniques. Next-generation experiments also need to be conceived to address issues currently beyond our reach.