  The last few years have seen the emergence of a wide array of novel
techniques for analyzing high-precision data from upcoming galaxy surveys,
which aim to extend the statistical analysis of galaxy clustering data beyond
the linear regime and the canonical two-point (2pt) statistics. We test and
benchmark some of these new techniques in a community data challenge
"Beyond-2pt", initiated during the Aspen 2022 Summer Program "Large-Scale
Structure Cosmology beyond 2-Point Statistics," whose first round of results we
present here. The challenge dataset consists of high-precision mock galaxy
catalogs for clustering in real space, redshift space, and on a light cone.
Participants in the challenge have developed end-to-end pipelines to analyze
mock catalogs and extract unknown ("masked") cosmological parameters of the
underlying $\Lambda$CDM models with their methods. The methods represented are
density-split clustering, nearest neighbor statistics, BACCO power spectrum
emulator, void statistics, LEFTfield field-level inference using effective
field theory (EFT), and joint power spectrum and bispectrum analyses using both
EFT and simulation-based inference. In this work, we review the results of the
challenge, focusing on problems solved, lessons learned, and future research
needed to perfect the emerging beyond-2pt approaches. The unbiased parameter
recovery demonstrated in this challenge by multiple statistics and the
associated modeling and inference frameworks supports the credibility of
cosmology constraints from these methods. The challenge data set is publicly
available and we welcome future submissions from methods that are not yet
represented.
