%0 Generic %1 lochhaas2015modeling %A Lochhaas, Cassandra %A Weinberg, David H. %A Peirani, Sébastien %A Dubois, Yohan %A Colombi, Stéphane %A Blaizot, Jérémy %A Font-Ribera, Andreu %A Pichon, Christophe %A Devriendt, Julien %D 2015 %K bias correlation halos lya %T Modeling Lyman-\alpha\ Forest Cross-Correlations with LyMAS %U http://arxiv.org/abs/1511.04454 %X We use the Ly-$\alpha$ Mass Association Scheme (LyMAS; Peirani et al. 2014) to predict cross-correlations at z = 2.5 between dark matter halos and transmitted flux in the Ly-$\alpha$ forest, and we compare these predictions to cross-correlations measured for quasars and damped Ly-$\alpha$ systems (DLAs) from the Baryon Oscillation Spectroscopic Survey (BOSS) by Font-Ribera et al. (2012, 2013). We calibrate and test LyMAS using Horizon-AGN hydrodynamical cosmological simulations of a $(100\ h^-1\ Mpc)^3$ comoving volume with and without AGN feedback. We apply this calibration to a $(1\ h^-1\ Gpc)^3$ simulation realized with $2048^3$ dark matter particles for our primary predictions. In the $100\ h^-1\ Mpc$ box, LyMAS reproduces the halo-flux correlations computed from the full hydrodynamic gas distribution essentially perfectly. In the $1\ h^-1\ Gpc$ box, the amplitude of the cross-correlation tracks the halo bias as expected, and the correlation for a halo sample with a distribution of masses scales linearly with the number-weighted mean bias. We provide empirical fitting functions that describe our numerical results. In the transverse separation bins used for the BOSS analyses, LyMAS cross-correlation predictions follow linear theory accurately down to small scales, though the quadrupole departs from linear theory on scales below $\sim15\ h^-1\ Mpc$. Fitting the BOSS measurements requires inclusion of random velocity errors; we find best-fit RMS velocity errors of 399 km/s and 252 km/s for quasars and DLAs, respectively. We infer bias-weighted mean halo masses of $M_h/10^12\ h^-1\ M_= 2.19^+0.16_-0.15$ and $0.69^+0.16_-0.14$ for the host halos of quasars and DLAs, with ~ 0.2 dex systematic uncertainty associated with redshift evolution, IGM parameters, and selection of data fitting range.

@misc{lochhaas2015modeling, abstract = {We use the Ly-$\alpha$ Mass Association Scheme (LyMAS; Peirani et al. 2014) to predict cross-correlations at z = 2.5 between dark matter halos and transmitted flux in the Ly-$\alpha$ forest, and we compare these predictions to cross-correlations measured for quasars and damped Ly-$\alpha$ systems (DLAs) from the Baryon Oscillation Spectroscopic Survey (BOSS) by Font-Ribera et al. (2012, 2013). We calibrate and test LyMAS using Horizon-AGN hydrodynamical cosmological simulations of a $(100\ h^{-1}\ \rm{Mpc})^3$ comoving volume with and without AGN feedback. We apply this calibration to a $(1\ h^{-1}\ \rm{Gpc})^3$ simulation realized with $2048^3$ dark matter particles for our primary predictions. In the $100\ h^{-1}\ \rm{Mpc}$ box, LyMAS reproduces the halo-flux correlations computed from the full hydrodynamic gas distribution essentially perfectly. In the $1\ h^{-1}\ \rm{Gpc}$ box, the amplitude of the cross-correlation tracks the halo bias as expected, and the correlation for a halo sample with a distribution of masses scales linearly with the number-weighted mean bias. We provide empirical fitting functions that describe our numerical results. In the transverse separation bins used for the BOSS analyses, LyMAS cross-correlation predictions follow linear theory accurately down to small scales, though the quadrupole departs from linear theory on scales below $\sim15\ h^{-1}\ \rm{Mpc}$. Fitting the BOSS measurements requires inclusion of random velocity errors; we find best-fit RMS velocity errors of 399 km/s and 252 km/s for quasars and DLAs, respectively. We infer bias-weighted mean halo masses of $M_h/10^{12}\ h^{-1}\ M_\odot = 2.19^{+0.16}_{-0.15}$ and $0.69^{+0.16}_{-0.14}$ for the host halos of quasars and DLAs, with ~ 0.2 dex systematic uncertainty associated with redshift evolution, IGM parameters, and selection of data fitting range.}, added-at = {2015-11-17T10:22:59.000+0100}, author = {Lochhaas, Cassandra and Weinberg, David H. and Peirani, Sébastien and Dubois, Yohan and Colombi, Stéphane and Blaizot, Jérémy and Font-Ribera, Andreu and Pichon, Christophe and Devriendt, Julien}, biburl = {https://www.bibsonomy.org/bibtex/28eda0ff27e16930876a9aef2004152db/miki}, description = {[1511.04454] Modeling Lyman-\alpha\ Forest Cross-Correlations with LyMAS}, interhash = {04ab3feba0b1a2ac07865c59b1b12838}, intrahash = {8eda0ff27e16930876a9aef2004152db}, keywords = {bias correlation halos lya}, note = {cite arxiv:1511.04454Comment: Submitted to MNRAS, 22 pages, 19 figures For a short video summarizing this paper, please visit: https:https://youtu.be/9ghGNtF16JA}, timestamp = {2015-11-17T10:22:59.000+0100}, title = {Modeling Lyman-\alpha\ Forest Cross-Correlations with LyMAS}, url = {http://arxiv.org/abs/1511.04454}, year = 2015 }