Learning to Concentrate: Multi-tracer Forecasts on Local Primordial Non-Gaussianity with Machine-Learned Bias

Understand

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 $b_{\phi}$.

• Knowledge of $b_{\phi}$ 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 $b_{\phi}$ for simulated halos exhibits significant secondary dependence on halo concentration.
• We leverage this fact to forecast multi-tracer constraints on $f_{NL}^{\mathrm{loc}}$.