Efficient Summation of Arbitrary Masks -- ESAM

TL;DR

ESAM is a novel method that efficiently computes the convolution of arbitrary masks, significantly reducing computational costs in astrophysical transient searches like FRB dedispersion while maintaining accuracy.

Contribution

Introduces ESAM, a new approach for fast, accurate convolution of arbitrary masks, enabling more efficient dedispersion in radio transient searches.

Findings

Reduces dedispersion computational cost by ~10 times.

Maintains Signal-to-Noise ratio accuracy comparable to brute force methods.

Offers flexibility to choose arbitrary masks and optimize performance.

Abstract

Searches for impulsive, astrophysical transients are often highly computationally demanding. A notable example is the dedispersion process required for performing blind searches for Fast Radio Bursts (FRBs) in radio telescope data. We introduce a novel approach - Efficient Summation of Arbitrary Masks (ESAM) - which efficiently computes 1-D convolution of many arbitrary 2-D masks, and can be used to carry out dedispersion over thousands of dispersion trials efficiently. Our method matches the accuracy of the traditional brute force technique in recovering the desired Signal-to-Noise ratio (S/N) while reducing computational cost by around a factor of 10. We compare its performance with existing dedispersion algorithms, such as the Fast Dispersion Measure Transform (FDMT) algorithm, and demonstrate how ESAM provides freedom to choose arbitrary masks and further optimise computational cost versus accuracy. We explore the potential applications of ESAM beyond FRB searches.