2025ApJS..280....7A

Searching for novel small signals in noisy data is preferably pursued by correlation in two or more independently operating channels. Signals of potential interest exist in tails beyond κσ, where κ denotes a multiple of the standard deviation σ of the data. Since moving data is a major cost factor in big-data analysis and heterogeneous computing more generally, efficiency may be optimized by restricting the computation of correlations to the tails of two-channel data exceeding κσ. Already, a moderate value κ ≳ 2 realizes a data reduction by at least an order of magnitude. Here, we study this approach using a novel excess probability ratio (EPR), correlating Boolean data resulting from tails beyond a cutoff κσ. We compare and rank EPR performance against conventional direct cross correlation and the Pearson coefficient (PC), applicable to the original data with no cutoff. This benchmark is performed over different combinations of background noise (Gaussian, Poisson and uniform) and signals (Gaussian, Poisson, uniform, chirps and sine waves). Results show the performance of EPR to be comparable to that of the PC, providing a new approach for significant improvements in efficiency with essentially no loss of sensitivity, relevant to the present era of big-data observatories.