ETHOS -- An Effective Theory of Structure Formation: Impact of Dark Acoustic Oscillations on Cosmic Dawn

TL;DR

This paper demonstrates how upcoming 21-cm hydrogen line observations during cosmic dawn can be used to probe the nature of dark matter, especially distinguishing ETHOS models with dark acoustic oscillations from other dark matter scenarios.

Contribution

The study connects ETHOS dark matter models with 21-cm signal predictions, showing the potential of future observations to constrain small-scale dark matter physics.

Findings

21-cm observations can distinguish ETHOS models from CDM at small scales.

Dark acoustic oscillations produce unique signatures in the 21-cm power spectrum.

21-cm data can probe scales an order of magnitude smaller than current constraints.

Abstract

Upcoming data of the 21-cm hydrogen line during cosmic dawn ($z\sim 10-30$) will revolutionize our understanding of the astrophysics of the first galaxies. Here we present a case study on how to exploit those same measurements to learn about the nature of dark matter (DM) at small scales. Focusing on the Effective Theory of Structure Formation (ETHOS) paradigm, we run a suite of simulations covering a broad range of DM microphysics, connecting the output of $N$-body simulations to dedicated 21-cm simulations to predict the evolution of the 21-cm signal across the entire cosmic dawn. We find that observatories targeting both the global signal and the 21-cm power spectrum are sensitive to all ETHOS models we study, and can distinguish them from CDM if the suppression wavenumber is smaller than $k\approx 300\, h/$Mpc, even when accounting for feedback with a phenomenological model. This is an order of magnitude smaller comoving scales than currently constrained by other data sets, including the Lyman-$\alpha$ forest. Moreover, if a prospective 21-cm detection confirmed a deficiency of power at small scales, we show that ETHOS models with strong dark acoustic oscillations can be discriminated from the pure suppression of warm dark matter, showing the power of 21-cm data to understand the behavior of DM at the smallest physical scales.