Numerical polology framework samples coupling space to discover ghost-free tensor field theories up to rank three for cosmology, then applies resulting priors to black hole superradiance, dynamical dark energy, and GW data.
Bootstrapping gravity: a consistent approach to energy-momentum self-coupling
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abstract
It is generally believed that coupling the graviton (a classical Fierz-Pauli massless spin-2 field) to its own energy-momentum tensor successfully recreates the dynamics of the Einstein field equations order by order; however the validity of this idea has recently been brought into doubt [1]. Motivated by this, we present a graviton action for which energy-momentum self-coupling is indeed consistent with the Einstein field equations. The Hilbert energy-momentum tensor for this graviton is calculated explicitly and shown to supply the correct second-order term in the field equations; in contrast, the Fierz-Pauli action fails to supply the correct term. A formalism for perturbative expansions of metric-based gravitational theories is then developed, and these techniques employed to demonstrate that our graviton action is a starting point for a straightforward energy-momentum self-coupling procedure that, order by order, generates the Einstein-Hilbert action (up to a classically irrelevant surface term). The perturbative formalism is extended to include matter and a cosmological constant, and interactions between perturbations of a free matter field and the gravitational field are studied in a vacuum background. Finally, the effect of a non-vacuum background is examined, and the graviton is found to develop a non-vanishing ``mass-term'' in the action.
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astro-ph.CO 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Numerical polology: towards next-generation model-building for cosmology
Numerical polology framework samples coupling space to discover ghost-free tensor field theories up to rank three for cosmology, then applies resulting priors to black hole superradiance, dynamical dark energy, and GW data.