“Implicit Likelihood Cosmological Inference: Simulations & Diagnostics“
Ongoing cosmological surveys map the large-scale structure of the Universe across immense volumes. Extracting robust cosmological information from these data requires fast, high-precision simulations of the survey observables and tight control of systematics. Part I: I present the first framework to detect and avoid model misspecification in implicit likelihood cosmological inference. Using the SELFI algorithm to infer the initial matter power spectrum from a forward model of galaxy surveys, I quantify the imprint of galaxy bias, selection functions, survey masks, redshift errors, and approximate gravity solvers. The analysis relies on a single, joint suite of N-body simulations, which we recycle for score compression prior to cosmological parameter inference. I further show that misspecification at the per-cent level can shift constraints by up to 2σ in (Ωₘ, σ₈)—biases that our framework exposes and avoids. Part II: I show that COLA need not sacrifice small-scale accuracy for speed. Combining fully non-linear Particle–Particle–Particle–Mesh (P3M) forces with the COLA change of frame, I obtain GADGET-4-level precision at significantly reduced cost compared with pure P3M. Together, these advances pave the way to making the most of Stage-IV surveys with implicit likelihood inference.