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

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 $b_{\varphi }$. Knowledge of $b_{\varphi }$ 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 $b_{\varphi }$ for simulated halos exhibits significant secondary dependence on halo concentration. We leverage this fact to forecast multi-tracer constraints on $f_{NL}^{\mathrm{loc}}$. We train a machine learning model on observable properties of simulated Illustris-TNG galaxies to predict $b_{\varphi }$ for samples constructed to approximate DESI emission line galaxies (ELGs) and luminous red galaxies (LRGs). We find $\sigma (f_{NL}^{\mathrm{loc}})=2.3$, and $\sigma (f_{NL}^{\mathrm{loc}})=3.7$, 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 $\sigma (f_{NL}^{\mathrm{loc}})=1.5$ is attainable with our learned model, more than a factor of 3 improvement over the single-tracer case, while the ideal split by $b_{\varphi }$ could reach $\sigma (f_{NL}^{\mathrm{loc}})<1$. We also perform multi-tracer forecasts for upcoming spectroscopic surveys targeting LPNG (MegaMapper, SPHEREx) and show that splitting tracer samples by $b_{\varphi }$ can lead to an order-of-magnitude reduction in projected $\sigma (f_{NL}^{\mathrm{loc}})$ for these surveys.

32 pages, 9 figures, 4 tables, Published version

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• Quijote-PNG: The Information Content of the Halo Mass Function
• A multitracer analysis for the eBOSS galaxy sample based on the effective field theory of large-scale structure
• Pairwise kSZ signal extraction efficacy and optical depth estimation
• Constraining primordial non-Gaussianity by combining photometric galaxy and 21cm intensity mapping surveys
• Constraining $f_{NL}$ using the Large-Scale Modulation of Small-Scale Statistics
• Linear Relativistic Corrections in the Spherical Fourier-Bessel Power Spectrum
• Primordial non-Gaussianity systematics from redshift mismatch with SPHEREx
• Large-scale Modeling of the Observed Power Spectrum Multipoles
• Clustering analysis of medium-band selected high-redshift galaxies
• Predicting Halo Formation Time Using Machine Learning