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Bispectrum at 1-loop in the Effective Field Theory of Inflation

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arxiv 2405.10374 v2 pith:AGPDAYDZ submitted 2024-05-16 hep-th gr-qchep-ph

classification hep-thgr-qchep-ph
keywords loopbispectrumcomputedeffectivefeaturefieldinflationleft
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

In this paper we compute 1-loop corrections to the bispectrum in the decoupling limit of the Effective Field Theory of Inflation (EFToI). We regulate the divergences by employing dimensional regularization and work in $d=3+\delta$ dimensions. We find that the final results feature analytic structures of the form $\log{\left(k_i/k_T\right)}$ and $\log{\left(H/\mu\right)}$, where $H$ is the Hubble parameter and $\mu$ is the renormalisation scale. An interesting outcome of our calculations is that unlike the 1-loop correction to the power-spectrum computed in arXiv:0912.2734 the unrenormalised answers always produce unphysical logarithms of co-moving momenta. These unphysical logarithms are cancelled only after renormalisation. We expect this to be a generic feature for loop computations unless there is some cancellation as in the previously computed 1-loop result for the power-spectrum.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Scale-Dependent Loop Corrections to the Inflationary Power Spectrum

    astro-ph.CO 2026-03 conditional novelty 7.0 of 10

    One-loop gravitational corrections in inflationary models with scale-dependent features are renormalizable and vanish on large and small scales, preserving perturbativity of CMB-fit feature models.

  2. Cosmological cutting rules for Bogoliubov initial states: any mass and spin

    hep-th 2025-02 conditional novelty 6.0 of 10

    The paper derives modified propagator identities and discontinuity operations that generalize Bogoliubov-state cosmological cutting rules to fields of arbitrary mass and spin.

  3. Efficient training of photonic quantum generative models

    quant-ph 2026-03 unverdicted novelty 5.0 of 10

    Photonic quantum generative models can be trained classically via maximum mean discrepancy, with deployment corresponding to boson sampling.

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