Towards optimal and robust $f_{\rm NL}$ constraints with multi-tracer analyses
Alexandre Barreira, Elisabeth Krause

TL;DR
This paper demonstrates that multi-tracer galaxy analyses can significantly improve constraints on primordial non-Gaussianity parameter $f_{ m NL}$, especially when splitting samples by galaxy color, and offers more robust methods less dependent on prior relations.
Contribution
It introduces a new multi-tracer technique that enhances $f_{ m NL}$ constraints and reduces dependence on prior bias relations, validated with galaxy simulations.
Findings
Splitting galaxy samples by color improves $f_{ m NL}$ detection significance.
Multi-tracer constraints are more robust to bias uncertainties than single-tracer.
Color-based splitting more than doubles the detection significance.
Abstract
We discuss the potential of the multi-tracer technique to improve observational constraints of the local primordial non-Gaussianity (PNG) parameter from the galaxy power spectrum. For two galaxy samples and , the constraining power is , where and are the linear and PNG galaxy bias parameters. We show this allows for significantly improved constraints compared to the traditional expectation based on naive universality-like relations where . Using IllustrisTNG galaxy simulation data, we find that different equal galaxy number splits of the full sample lead to different , and thus have different constraining power. Of all of the strategies explored, splitting by color is the most promising, more than doubling the significance of…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research · Gaussian Processes and Bayesian Inference
