# A fast estimator for the bispectrum and beyond - A practical method for measuring non-Gaussianity in 21-cm maps

## TL;DR

This paper introduces a fast, robust FFT-based estimator for the bispectrum and higher-order polyspectra, enabling efficient analysis of non-Gaussian features in 21-cm maps and other fields.

## Contribution

The authors develop a generalized, computationally efficient FFT-based estimator for polyspectra, applicable to any order, with demonstrated accuracy and speed for cosmological data analysis.

## Key findings

- Estimator remains accurate over a wide k-range
- Significantly faster than direct sampling methods
- Applicable to non-Gaussian simulations and observations

## Abstract

In this paper we establish the accuracy and robustness of a fast estimator for the bispectrum - the "FFT bispectrum estimator". The implementation of the estimator presented here offers speed and simplicity benefits over a direct sampling approach. We also generalise the derivation so it may be easily be applied to any order polyspectra, such as the trispectrum, with the cost of only a handful of FFTs. All lower order statistics can also be calculated simultaneously for little extra cost. To test the estimator we make use of a non-linear density field, and for a more strongly non-Gaussian test case we use a toy-model of reionization in which ionized bubbles at a given redshift are all of equal size and are randomly distributed. Our tests find that the FFT estimator remains accurate over a wide range of k, and so should be extremely useful for analysis of 21-cm observations. The speed of the FFT bispectrum estimator makes it suitable for sampling applications, such as Bayesian inference. The algorithm we describe should prove valuable in the analysis of simulations and observations, and whilst we apply it within the field of cosmology, this estimator is useful in any field that deals with non-Gaussian data.