Learning to concentrate: multi-tracer forecasts on local primordial non-Gaussianity with machine-learned bias
James M. Sullivan, Tijan Prijon, Uroš Seljak
Abstract
Abstract Local primordial non-Gaussianity (LPNG) is predicted by many non-minimal models of inflation, and creates a scale-dependent contribution to the power spectrum of large-scale structure (LSS) tracers, whose amplitude is characterized by b ϕ . Knowledge of b ϕ for the observed tracer population is therefore crucial for learning about inflation from LSS. Recently, it has been shown that the relationship between linear bias b 1 and b ϕ for simulated halos exhibits significant secondary dependence on halo concentration. We leverage this fact to forecast multi-tracer constraints on f loc NL . We train a machine learning model on observable properties of simulated IllustrisTNG galaxies to predict b ϕ for samples constructed to approximate DESI emission line galaxies (ELGs) and luminous red galaxies (LRGs). We find σ ( f loc NL ) = 2.3, and σ ( f loc NL = 3.7, respectively. These forecasted errors are roughly factors of 3, and 35% improvements over the single-tracer case for each sample, respectively. When considering both ELGs and LRGs in their overlap region, we forecast σ ( f loc NL ) = 1.5 is attainable with our learned model, more than a factor of 3 improvement over the single-tracer case, while the ideal split by b ϕ could reach σ ( f loc NL ) < 1. We also perform multi-tracer forecasts for upcoming spectroscopic surveys targeting LPNG (MegaMapper, SPHEREx) and show that splitting tracer samples by b ϕ can lead to an order-of-magnitude reduction in projected σ ( f loc NL for these surveys.