Advancing the matter bispectrum estimation of large-scale structure: a comparison of dark matter codes

Cosmological information from forthcoming galaxy surveys, such as LSST and Euclid, will soon exceed that available from the CMB. Higher order correlation functions, like the bispectrum, will be indispensable for realising this potential. The interpretation of this data faces many challenges because gravitational collapse of matter is a complex non-linear process, typically modelled by computationally expensive N-body simulations. Proposed alternatives using fast dark matter codes (e.g. 2LPT or particle-mesh) are primarily evaluated on their ability to reproduce clustering statistics linked to the matter power spectrum. The accuracy of these codes can be tested in more detail by looking at higher-order statistics, and in this paper we will present an efficient and optimal methodology (MODAL-LSS) to reconstruct the full bispectrum of any 3D density field. We make quantitative comparisons between a number of fast dark matter codes and Gadget at redshift $z=0.5$. This will serve as an important diagnostic tool for dark matter/halo mock catalogues and lays the foundation for realistic high precision analysis with the galaxy bispectrum. In particular, we show that the lack of small-scale power in the bispectrum of fast codes can be ameliorated by a simple `boosting' technique for the power spectrum. We also investigate the covariance of the MODAL-LSS bispectrum estimator, demonstrating the plateauing of non-Gaussian errors in contrast to simple Gaussian extrapolations. This has important consequences for the extraction of information from the bispectrum and hence parameter estimation. Finally we make quantitative comparisons of simulation bispectra with theoretical models, discussing the initial parameters required to create mock catalogues with accurate bispectra.