Multi-Component Decomposition of Cosmic Infrared Background Fluctuations

TL;DR

This paper develops a maximum likelihood method for separating the epoch of reionization signal from other infrared background sources using multi-wavelength data, enabling better understanding of early universe galaxy formation.

Contribution

It introduces a novel frequency tomography technique that effectively disentangles EoR fluctuations from foregrounds in simulated infrared data.

Findings

Successfully reconstructs EoR, IHL, and foreground signals in simulations.

Demonstrates the potential of multi-wavelength analysis for early universe studies.

Provides a framework for future observational separation of cosmic infrared components.

Abstract

The near-infrared background between 0.5 $\mu$m to 2 $\mu$m contains a wealth of information related to radiative processes in the universe. Infrared background anisotropies encode the redshift-weighted total emission over cosmic history, including any spatially diffuse and extended contributions. The anisotropy power spectrum is dominated by undetected galaxies at small angular scales and diffuse background of Galactic emission at large angular scales. In addition to these known sources, the infrared background also arises from intra-halo light (IHL) at $z < 3$ associated with tidally-stripped stars during galaxy mergers. Moreover, it contains information on the very first galaxies from the epoch of reionization (EoR). The EoR signal has a spectral energy distribution (SED) that goes to zero near optical wavelengths due to Lyman absorption, while other signals have spectra that vary smoothly with frequency. Due to differences in SEDs and spatial clustering, these components may be separated in a multi-wavelength-fluctuation experiment. To study the extent to which EoR fluctuations can be separated in the presence of IHL, extra-galactic and Galactic foregrounds, we develop a maximum likelihood technique that incorporates a full covariance matrix among all the frequencies at different angular scales. We apply this technique to simulated deep imaging data over a 2$\times$100 deg$^2$ sky area from 0.75 $\mu$m to 5 $\mu$m in 9 bands and find that such a "frequency tomography" can successfully reconstruct both the amplitude and spectral shape for representative EoR, IHL and the foreground signals.