Probing the cosmic web in Ly$α$ emission over large scales: an Intensity Mapping forecast for DECaLS/BASS and DESI (2406.18775v2)
Abstract: Being the most prominent HI line, Ly$\alpha$ permeates the cosmic web in emission. Despite its potential as a cosmological probe, its detection on large scales remains elusive. We present a new methodology to perform Ly$\alpha$ intensity mapping with broad-band optical images, by cross-correlating them with Ly$\alpha$ forest data using a custom one-parameter estimator. We also develop an analytical large-scale Ly$\alpha$ emission model with two parameters (average luminosity $\langle L_{\rm Ly\alpha} \rangle$ and bias $b_{\rm e}$) that respects observational constraints from QSO luminosity functions. We compute a forecast for DECaLS/BASS $g$-band images cross-correlated with DESI Ly$\alpha$ forest data, setting guidelines for reducing images into Ly$\alpha$ intensity maps. Given the transversal scales of our cross-correlation (26.4 arcmin, $\sim$33 cMpc/h), our study effectively integrates Ly$\alpha$ emission over all the cosmic volume inside the DESI footprint at $2.2 < z < 3.4$ (the $g$-band Ly$\alpha$ redshift range). Over the parameter space ($\langle L_{\rm Ly\alpha} \rangle$, $b_{\rm e}$) sampled by our forecast, we find a 3$\sigma$ of large-scale structure in Ly$\alpha$ likely, with a probability of detection of 23.95\% for DESI-DECaLS/BASS, and 54.93\% for a hypothetical DESI phase II with twice as much Ly$\alpha$ QSOs. Without a detection, we derive upper bounds on $\langle L_{\rm Ly\alpha} \rangle$ competitive with optimistic literature estimates ($2.3 \pm 1 \cdot 10{\rm 41}$ erg/s/cMpc$3$ for DESI, and $\sim$35\% lower for its hypothetical phase II). Extrapolation to the DESI-Rubin overlap shows that a detection of large-scale structure with Ly$\alpha$ intensity mapping using next-generation imaging surveys is certain. [abridged]
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.