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The DESI One-Percent survey: constructing galaxy-halo connections for ELGs and LRGs using auto and cross correlations

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arxiv 2306.06317 v3 pith:7ZOIFFLF submitted 2023-06-09 astro-ph.GA astro-ph.CO

The DESI One-Percent survey: constructing galaxy-halo connections for ELGs and LRGs using auto and cross correlations

classification astro-ph.GA astro-ph.CO
keywords elgslrgsmathrmautocorrelationsdesimodelcross
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In the current Dark Energy Spectroscopic Instrument (DESI) survey, emission line galaxies (ELGs) and luminous red galaxies (LRGs) are essential for mapping the dark matter distribution at $z \sim 1$. We measure the auto and cross correlation functions of ELGs and LRGs at $0.8<z\leq 1.0$ from the DESI One-Percent survey. Following Gao et al. (2022), we construct the galaxy-halo connections for ELGs and LRGs simultaneously. With the stellar-halo mass relation (SHMR) for the whole galaxy population (i.e. normal galaxies), LRGs can be selected directly by stellar mass, while ELGs can also be selected randomly based on the observed number density of each stellar mass, once the probability $P_{\mathrm{sat}}$ of a satellite galaxy becoming an ELG is determined. We demonstrate that the observed small scale clustering prefers a halo mass-dependent $P_{\mathrm{sat}}$ model rather than a constant. With this model, we can well reproduce the auto correlations of LRGs and the cross correlations between LRGs and ELGs at $r_{\mathrm{p}}>0.1$ $\mathrm{Mpc}\,h^{-1}$. We can also reproduce the auto correlations of ELGs at $r_{\mathrm{p}}>0.3$ $\mathrm{Mpc}\,h^{-1}$ ($s>1$ $\mathrm{Mpc}\,h^{-1}$) in real (redshift) space. Although our model has only seven parameters, we show that it can be extended to higher redshifts and reproduces the observed auto correlations of ELGs in the whole range of $0.8<z<1.6$, which enables us to generate a lightcone ELG mock for DESI. With the above model, we further derive halo occupation distributions (HODs) for ELGs which can be used to produce ELG mocks in coarse simulations without resolving subhalos.

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Cited by 1 Pith paper

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  1. DESI Data Release 2 ELGs: Property-dependent subsamples, imaging systematics, and clustering

    astro-ph.CO 2026-06 unverdicted novelty 4.0

    Property-dependent systematic weights derived separately on ELG subsamples, with separate DES footprint treatment, mitigate spurious clustering in ~10% of subsamples but are not optimal for the full sample.