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LyMAS: Predicting Large-Scale Lyman-alpha Forest Statistics from the Dark Matter Density Field by Peirani, Sébastien; Weinberg, David H.; Colombi, Stéphane; Blaizot, Jérémy; Dubois, Yohan; Pichon, Christophe is a scholarly article available to read on EtoBox.

What is LyMAS: Predicting Large-Scale Lyman-alpha Forest Statistics from the Dark Matter Density Field about?

[abridged] We describe LyMAS (Ly-alpha Mass Association Scheme), a method of predicting clustering statistics in the Ly-alpha forest on large scales from moderate resolution simulations of the dark matter distribution, with calibration from high-resolution hydrodynamic simulations of smaller volumes. We use the "Horizon MareNostrum" simulation, a 50 Mpc/h comoving volume evolved with the adaptive mesh hydrodynamic code RAMSES, to compute the conditional probability distribution P(F_s|delta_s) of the transmitted flux F_s, smoothed (1-dimensionally) over the spectral resolution scale, on the dark matter density contrast delta_s, smoothed (3-dimensionally) over a similar scale. In this study we adopt the spectral resolution of the SDSS-III BOSS at z=2.5, and we find optimal results for a dark matter smoothing length sigma=0.3 Mpc/h (comoving). In extended form, LyMAS exactly reproduces both the 1-dimensional power spectrum and 1-point flux distribution of the hydro simulation spectra. Applied to the MareNostrum dark matter field, LyMAS accurately predicts the 2-point conditional flux distribution and flux correlation function of the full hydro simulation for transverse sightline separ

Author

Peirani, Sébastien; Weinberg, David H.; Colombi, Stéphane; Blaizot, Jérémy; Dubois, Yohan; Pichon, Christophe

Published

2013

Language

EN