Probing Fuzzy Dark Matter in the 21 cm Signal via Wavelet Scattering Transform

TL;DR

This paper demonstrates that wavelet scattering transform analysis of 21 cm signals can effectively distinguish fuzzy dark matter from cold dark matter, even with realistic observational noise, by capturing non-Gaussian features sensitive to FDM particle mass.

Contribution

It introduces the use of wavelet scattering transform to analyze 21 cm signals for probing fuzzy dark matter effects, showing robustness under realistic noise conditions.

Findings

WST coefficients effectively differentiate FDM from CDM scenarios.

Low-order wavelet couplings are highly sensitive to FDM particle mass.

WST analysis remains robust under SKA1-Low-like thermal noise.

Abstract

We explore the imprints of fuzzy dark matter (FDM) on the redshifted 21~cm signal from the Cosmic Dawn and the Epoch of Reionization by employing the wavelet scattering transform (WST). FDM, composed of ultralight scalar particles with masses $m_{\mathrm{FDM}} \sim 10^{-22}\,\mathrm{eV}$, exhibits quantum pressure that suppresses the formation of small-scale structures below the de~Broglie wavelength, thereby delaying star formation and modifying the thermal history of the intergalactic medium. Using modified \texttt{21cmFAST} simulations that incorporate both linear and nonlinear effects of FDM on structure formation, we analyze the two-dimensional 21~cm brightness temperature fields through the first- and second-order WST coefficients. The first-order coefficients, $S_1(j)$, quantify scale-dependent variance analogous to the power spectrum, while the normalized second-order ratio $R(j_1,j_2)=S_2/S_1$ captures non-Gaussian cross-scale couplings. We find that low-order couplings, particularly between large and intermediate scales, are highly sensitive to the FDM particle mass and remain robust under SKA1-Low-like thermal noise. Quantitatively, the WST coefficients yield pairwise distances of $\Delta \simeq 225$ between CDM and FDM with $m_{\mathrm{FDM}}=10^{-22}\,\mathrm{eV}$, demonstrating that this framework can effectively discriminate between wave-like and cold dark matter scenarios even under realistic observational conditions. Our results establish the WST as a powerful, noise-tolerant statistical tool for probing the wave nature of dark matter through forthcoming 21~cm observations.