Selection of high-redshift Lyman-Break Galaxies from broadband and wide photometric surveys

Abstract

In this paper, we investigate the possibility of selecting high-redshift Lyman-Break Galaxies (LBG) using current and future broadband wide photometric surveys, such as UNIONS or the Vera C. Rubin LSST. This work is conducted in the context of DESI-II, the next phase of DESI, which will start around 2029. We use deep imaging data from HSC and CLAUDS on the COSMOS and XMM-LSS fields. To predict the selection performance of LBGs with image quality similar to UNIONS, we degrade the $u,g,r,i$ and $z$ bands to UNIONS depth. The Random Forest algorithm is trained with the $u,g,r,i$ and $z$ bands to classify LBGs in the $2.5<z<3.5$ range. We find that fixing a target density budget of $1,100$ deg${}^{-2}$, the Random Forest approach gives a density of $z>2$ targets of $873$ deg${}^{-2}$, and a density of $493$ deg${}^{-2}$ of confirmed LBGs after spectroscopic confirmation with DESI. This UNIONS-like selection was tested in a dedicated spectroscopic observation campaign of 1,000 targets with DESI on the COSMOS field, providing a safe spectroscopic sample with a mean redshift of 3. This sample is used to derive forecasts for DESI-II, assuming a sky coverage of 5,000 deg${}^2$. We predict uncertainties on Alcock-Paczynski parameters $\hat{I}{\pm }_{\perp }$ and $\hat{I}{\pm }_{\parallel }$ to be 0.7$\%$ and 1$\%$ for $2.6<z<3.2$, resulting in a potential 2$\%$ measurement of the dark energy fraction at high redshift. Additionally, we estimate the uncertainty in local non-Gaussianity and predict $\ddot{I}{ƒ}_{f_{\mathrm{NL}}}\approx 7$, which would be comparable to the current best precision achieved by \textit{Planck}. The latter forecast suggests that achieving the precision required to place stringent constraints on inflationary models ($\ddot{I}{ƒ}_{f_{\mathrm{NL}}}\approx 1$) using spectroscopic galaxy surveys necessitates the development of a next-generation (Stage V) spectroscopic survey.

40 pages, 35 figures, 3 tables, published in JCAP