A Gaussian-Remainder Hierarchy for Sums of Random Variables with Big-Jump Statistics

We develop an exact Gaussian-remainder hierarchy for the probability density of the sum of $N$ independent, identically distributed random variables with broad, finite-variance distribution for the summands. The hierarchy separates the Gaussian fixed-point contribution from residual sectors that retain the original single-summand density. For subexponential densities, the first nontrivial truncation yields a simple finite-$N$ approximation that involves one convolution with a Gaussian background and a subtraction that removes Gaussian overcounting. This approximation captures the Gaussian center, the crossover region, and the big-jump tail within a single expression, as demonstrated numerically for stretched-exponential and finite-variance power-law examples. The same first-order approximation reproduces the known asymptotic anomalous rate function for sums of stretched-exponential random variables and also provides an accurate approximation to the corresponding finite-$N$ rate function.