ROUGH DRAFT authorea.com/44794
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  • Tests for paper

    Here I am proposing a series of tests that could be useful to understand better the systematics in the logR statistics.

    Following the work that George has done already, let’s start simple:

    Power law-imposition

    The first thing to do is actually reducing the complexity of the log[T(\(\Delta\))] vs log(\(\Delta\)) distribution for all the simulations models, at each redshift.

    We can fit the log[T(\(\Delta\))]-log(\(\Delta\)) relation in the lower overdensity region (\(\Delta<10\)) of each model distribution and substitute directly the fit to the T(\(\Delta\)) array: in this way, for the first check we are avoiding to consider any further effect that is not due to the temperature-density relation in the simulations

    LogR-\(\gamma\) relation

    Using the simplified relations we can then compute the logR statistics at each redshift (let’s start from one ..the one already computed by George z=3.211). This should correspond to the orange circles computed in the preliminary tests. In this plot each dot represents a different simulation model. The \(\gamma\) values of the contaminant foreground Ly-\(\alpha\) at z=2.553 (\(\gamma_{\alpha f}\)) are almost the same as the Ly-\(\beta\) at z=3.211 (\(\gamma_{beta}\)) for each model. Similarly, \(T0_{\alpha f}\sim T0_{beta}\) because the simulations predict a small evolution in T0 and \(\gamma\) as a function of redshift. The density fields (\(\rho\)) corresponding to each model, for both the redshifts of the foreground Ly-\(\alpha\) and the Ly-\(\beta\) forests, are the ones used to compute the LogR-\(\gamma\) relation presented in the paper (that we can call “original relation” from now on) without any further modification.

    For example the value of logR corresponding to the D15 model is computed using the density field of this simulation for both the redshifts z=2.553 and z=3.211 and imposing a power law with T0\(\sim18200\) and \(\gamma\sim 1.5\).

    Comparing the “

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