The EFT likelihood for large-scale structure
Abstract
We derive, using functional methods and the bias expansion, the conditional likelihood for observing a specific tracer field given an underlying matter field. This likelihood is necessary for Bayesian-inference methods. If we neglect all stochastic terms apart from the ones appearing in the auto two-point function of tracers, we recover the result of Schmidt et al., 2018 [1]. We then rigorously derive the corrections to this result, such as those coming from a non-Gaussian stochasticity (which include the stochastic corrections to the tracer bispectrum) and higher-derivative terms. We discuss how these corrections can affect current applications of Bayesian inference. We comment on possible extensions to our result, with a particular eye towards primordial non-Gaussianity. This work puts on solid theoretical grounds the effective-field-theory-(EFT-)based approach to Bayesian forward modeling.
- Publication:
-
Journal of Cosmology and Astroparticle Physics
- Pub Date:
- April 2020
- DOI:
- 10.1088/1475-7516/2020/04/042
- arXiv:
- arXiv:1909.04022
- Bibcode:
- 2020JCAP...04..042C
- Keywords:
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- Astrophysics - Cosmology and Nongalactic Astrophysics;
- High Energy Physics - Theory
- E-Print:
- 53 pages (36+17), 4 tables. v2: matches JCAP version. Added section to compare with Schmidt et al., 2018