| Abstract: |
To efficiently probe primordial non-Gaussianity using Cosmic Microwave Background (CMB) data, we require theoretical predictions that are factorizable, \textit{i.e.} those whose kinematic dependence can be separated. This property does not hold for many models, hindering their application to data. In this work, we introduce a general framework for constructing separable approximations to primordial bispectra, enabling direct CMB constraints on arbitrary models including those computed using numerical tools. In contrast to other approaches such as modal decompositions, we learn the basis functions directly from the data, allowing high-fidelity representations with just a handful of terms. This is practically implemented using machine-learning techniques, utilizing neural network basis functions and a loss function designed to mimic the CMB cosine similarity. We validate our pipeline using a variety of input bispectra, demonstrating that the approximations are >99.5% correlated with the truth with just three terms. By incorporating the neural basis into the PolySpec code, we derive KSW-type CMB estimators, which reproduce local- and equilateral-type non-Gaussianity to within 0.1σ. As a proof-of-concept, we constrain two inflationary bispectra from the `cosmological collider' scenario; these feature an additional strongly-mixed particle sector and cannot be computed analytically. By combining the numerical predictions from CosmoFlow with our factorizable approach (with just three terms), we place novel constraints on the collider models using Planck PR4 data, finding no detection of non-Gaussianity. Our method facilitates detailed studies of the inflationary paradigm, connecting modern theoretical tools with high-resolution observational data.
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