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 bϕ
.
Knowledge of bϕ
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 bϕ
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 bϕ
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 bϕ
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 bϕ
can lead to an order-of-magnitude reduction in projected σ(f
loc
NL for these surveys.