PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 15, 2026Journal of High Energy Physics3 citationsOpen Access

Massive inflationary amplitudes: new representations and degenerate limits

View Full Paper
ZXZhong-Zhi XianyuJZJiaju Zang

Key Points

  • The aim is to explore massive inflationary amplitudes and their representations in cosmological collider physics.
  • Derived new differential equations for tree graphs
  • Developed analytical solutions for massive inflationary amplitudes
  • Focused on folded limit and partial-energy limit at a vertex
  • Introduced simplified representations for cosmological correlators
  • Showed involvement of smaller transcendental weights for folded tree graphs
  • Expressed inflaton bispectrum using hypergeometric functions and a trivariate Kampé de Fériet function

Abstract

A bstract The particle model building of cosmological collider physics often involves boost-breaking bilinear mixing between a heavy particle and the nearly massless inflaton mode. In cosmological correlators, such a mixing is obtained by taking a folded limit of a generic tree graph, which is a special case of degenerate kinematics. In this work, we continue our exploration of massive inflationary amplitudes with a focus on degenerate kinematics. With a suitable change of variables, we derive new differential equations and full analytical solutions for generic tree graphs, making it trivial to take the folded limit and partial-energy limit at a vertex. Our result shows that folded tree graphs generally involve functions of smaller transcendental weights than their nondegenerate counterparts. In particular, the inflaton bispectrum with triple massive exchanges can be expressed in terms of a trivariate Kampé de Fériet function and simpler hypergeometric functions.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Xianyu et al. (2026) studied this question.

synapsesocial.com/papers/69b5ff6e83145bc643d1becfhttps://doi.org/10.1007/jhep03(2026)122
Ask AI
Helpful
Bookmark
Share
View Full Paper