Papers › Learning to Concentrate: Multi-tracer Forecasts on Local Primordial Non-Gaussianity...
Learning to Concentrate: Multi-tracer Forecasts on Local Primordial Non-Gaussianity with Machine-Learned Bias
James M Sullivan, Tijan Prijon, Uros Seljak
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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 b₁ and b_ϕ for simulated halos exhibits significant secondary dependence on halo concentration. We leverage this fact to forecast multi-tracer constraints on f_(NL)ˡᵒᶜ. We train a machine learning model on observable properties of simulated Illustris-TNG galaxies to predict b_ϕ for samples constructed to approximate DESI emission line galaxies (ELGs) and luminous red galaxies (LRGs). We find σ(f_(NL)ˡᵒᶜ) = 2.3, and σ(f_(NL)ˡᵒᶜ) = 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_(NL)ˡᵒᶜ) = 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_(NL)ˡᵒᶜ) <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_(NL)ˡᵒᶜ) for these surveys.
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