Lyman Alpha Absorber Correlations and the Bias of the Lyman Alpha Forest

نویسندگان

  • R Dave
  • N Katz
  • Romeel Davé
  • Neal Katz
  • David H. Weinberg
چکیده

Lyα absorber correlations contain information about the underlying density distribution associated with a particular class of absorbers. As such, they provide an opportunity to independently measure the “bias” of the Lyman alpha forest, i.e. the relationship between H I column density and underlying dark matter density. In these proceedings we use hydrodynamic simulations to investigate whether the evolution of this bias is measurable from observable correlations. Unfortunately, the increasingly complex physics in the IGM at z ∼ < 1 makes a direct measurement of the bias difficult. Nevertheless, current simulations do make predictions for H I absorber correlations that are in broad agreement with observations at both high and low redshift, thus reinforcing the bias evolution predictions given by these models. Recent results indicate that hydrodynamic simulations provide an accurate description of the local intergalactic medium as traced by weak (NHI ∼< 10 cm) Lyα absorption lines (e.g. Davé & Tripp 2001). A key free parameter in such models is the “bias” of the Lyα forest, i.e. the relationship between the column density of a given absorber and the density of the underlying dark matter associated with that absorber. (Since pressure forces are typically small at the low densities and temperatures in the diffuse IGM, local dark matter and baryon densities trace each other very well.) At high redshifts (z ∼ > 2), there is a tight relationship between these quantities (Hui & Gnedin 1997; Croft et al. 1998), hence the bias is well-defined (i.e. non-stochastic, to borrow a term from galaxy survey studies). However, by the present epoch, many baryons have collapsed into galaxies or been shock heated on filaments to warm-hot temperatures (Cen & Ostriker 1999; Davé et al. 2001), hence the relationship between Lyα absorption and the underlying mass distribution becomes more complex. The evolution of this bias is pri-

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تاریخ انتشار 2018