Constraining Dark Acoustic Oscillations with the High-Redshift UV Luminosity Function

TL;DR

The work develops a four-parameter, model-agnostic DAO transfer function to map DAO signatures into the nonlinear halo mass function and the high-redshift UVLF. It calibrates an EPS-based halo mass function with DAO-inclusive N-body simulations and uses GALLUMI to connect halos to UV luminosities, fitting to UVLF data across z~3-9. It finds that the first DAO peak must occur at k_peak roughly above 50 h/Mpc unless the interacting fraction f is below about 0.07; for larger k_peak, f can be up to 1. These UVLF constraints complement and can exceed certain CMB bounds for DAO, highlighting the potential of high-redshift galaxy observations to probe small-scale dark-sector physics, with future surveys expected to sharpen these limits.

Abstract

Dark acoustic oscillations (DAOs) in the matter power spectrum can arise in many different dark sector models, and can imprint on a variety of cosmological observables. In this work we use measurements of the galactic UV luminosity function (UVLF) at high redshifts to constrain the dark acoustic oscillation feature at small scales in a model-agnostic way. We introduce a phenomenological transfer function model for a dark sector with a species undergoing DAOs which can accommodate sub-dominant dark matter abundances, and obtain constraints on its parameters. In order to predict the UVLF, we employ an Extended Press-Schechter formalism which we calibrate using N-body simulations with initial conditions featuring DAOs. Using measurements from the Hubble Space Telescope, James Webb Space Telescope, Subaru Telescope, and Canada-France-Hawaii Telescope, we constrain the wave number of the first DAO peak to be at $k \gtrsim 50\ h/\mathrm{Mpc}$, unless the fraction of dark matter undergoing DAOs is less than $0.07$.

Figures (12)

• Figure 1: An example transfer function obtained using $\texttt{CLASS-aDM}$ and the best-fit $T(k)$ model, along with illustrations of the effect of each model parameter on the transfer function shape. Because we are interested in probing oscillatory behavior in the matter power spectrum, we prioritize matching the DAOs with our model over exactly matching the initial suppression of the power spectrum. The atomic dark matter parameters used to generate this transfer function are $f_{D}=0.3$, $\xi_{D}=0.1$, $m_{p_{D}}=m_{p}$, $m_{e_{D}}=m_{e}$, $\alpha_{D}=3\alpha$, using the notation of Bansal:2022qbi.
• Figure 2: Three transfer functions generated for a model with DM-baryon elastic scattering interactions using a modified version of CLASS Gluscevic:2017ywpBoddy:2018kfvNguyen:2021cnb, along with the best fits from our model. These parameter choices are taken from Nadler:2025fcv, in which single-halo zoom-in simulations were performed for alternative dark matter models. $n$ is the index of the power law governing the DM-baryon scattering rate. We successfully match the numerically computed transfer functions.
• Figure 3: Relationships between physical quantities and best-fit transfer function parameters for 380 different choices of aDM parameters. Left: Fraction of atomic dark matter. Center: DAO scale, with best-fit power law $k_{\rm{peak}} = 1.30 \left(\frac{k_{\rm{DAO}}}{h/\mathrm{Mpc}} \right)^{0.944}$. Right: Dark diffusion damping scale, with best-fit power law $k_{\rm{damp}}=1.17 \left(\frac{k_{\mathrm{diffusion}}}{h/\mathrm{Mpc}} \right)^{1.03}$.
• Figure 4: Images of the matter density in our simulations at $z=4.9$. The low-resolution CDM simulation is on the left, with the chosen zoom-in region highlighted. On the right are the high-resolution zoom-in regions for both CDM and the DAO model with $f=1$, $k_{\rm{DAO}}=66\ h/\mathrm{Mpc}$. The smoothing of structure on small scales is clearly visible.
• Figure 5: Halo mass function derived from our simulations (solid) and analytic theory (EPS, dashed) for CDM (left) for aDM with $f = 1$, $k_{\rm{DAO}} \approx$ 66 $h$/Mpc (right) at various redshifts relevant to UVLFs.