Recovering 21cm Monopole Signals Without Smoothness

TL;DR

This paper proposes a novel method using normalizing flows to detect 21cm monopole signals from the dark ages and cosmic dawn without relying on spectral smoothness, addressing challenges posed by bright foregrounds.

Contribution

It introduces an unsupervised technique leveraging foreground fluctuation modeling with normalizing flows for 21cm signal detection, bypassing the need for spectral smoothness assumptions.

Findings

Normalizing flows enable efficient detection of 21cm signals.

Foreground fluctuation modeling improves signal separation.

Achromatic response enhances foreground removal.

Abstract

We expect the monopole signal at the lowest frequencies below $100\,$MHz to be composed of two components: the deep Rayleigh-Jeans tail of the cosmic microwave background and two distinct features: the dark ages trough at $\sim 17\,$MHz and the cosmic dawn trough at $\sim 75\,$Mhz. These are hidden under orders of magnitude brighter foregrounds whose emission is approximately a power-law with a spectral index $\approx -2.5$. It is usually assumed that monopole signals of interest are separable from foregrounds based on spectral smoothness. We argue that this is a difficult approach and likely impossible for the Dark Ages trough. Instead, we suggest that the fluctuations in the foreground emission around the sky should be used to build a model distribution of possible shapes of foregrounds, which can be used to constrain the presence of a monopole signal. We implement this idea using normalizing flows and show that this technique allows for efficient unsupervised detection of the amplitude, width, and center of the Dark Ages trough as well as the Rayleigh-Jeans tail of the cosmic microwave background for a sufficiently sensitive experiment. We show that achromatic and smooth response significantly helps with foreground separation. We discuss the limitations of the inherent assumptions in this method and the impact on the design of future low-frequency experiments.