Speaker
Description
We present a new Monte Carlo that generates events based on statistics
specified with any 1-point and 2-point function, including arbitrary
correlations.
Such a code can be useful for quickly generating events when
analytic formulas are known (for example from recent derivations of CGC
fluctuations), and for use in Bayesian analyses, where the initial state can
be characterized by physical-meaningful parameter such as mean energy
density, variance, skewness, correlation length, etc. We use the new code to
perform investigations of CGC fluctuations.
We study the effect of model parameters such as saturation scale and
regulators (both infrared and ultraviolet), as well as the effects of
higher order fluctuations, which are currently unknown.
This provides useful information, even beyond the particular model, of how
such fluctuation and correlation properties can appear in observable quantities. We investigate how correlated and uncorrelated events compare, and quantify the sensitivity of relevant properties such as eccentricities $\varepsilon_n$ to correlation lengths
