Universal predictions of screened modified gravity on cluster scales

TL;DR

This paper proposes a universal observable related to the fifth force in screened modified gravity models, enabling model-independent tests on cluster scales by rescaling predictions into a near universal form.

Contribution

It introduces a universal form for the observable related to the fifth force across various screened modified gravity models, simplifying their testing on cluster scales.

Findings

Predictions can be rescaled into a near universal form for many models.

The universal form depends on three key parameters: strength, mass, and width.

This approach enables model-independent searches for modified gravity signatures.

Abstract

Modified gravity models require a screening mechanism to be able to evade the stringent constraints from local gravity experiments and, at the same time, give rise to observable astrophysical and cosmological signatures. Such screened modified gravity models necessarily have dynamics determined by complex nonlinear equations that usually need to be solved on a model-by-model basis to produce predictions. This makes testing them a cumbersome process. In this paper, we investigate whether there is a common signature for all the different models that is suitable to testing them on cluster scales. To do this we propose an observable related to the fifth force, which can be observationally related to the ratio of dynamical-to-lensing mass of a halo, and then show that the predictions for this observable can be rescaled to a near universal form for a large class of modified gravity models. We demonstrate this using the Hu-Sawicki $f(R)$, the Symmetron, the nDGP, and the Dilaton models, as well as unifying parametrizations. The universal form is determined by only three quantities: a strength, a mass, and a width parameter. We also show how these parameters can be derived from a specific theory. This self-similarity in the predictions can hopefully be used to search for signatures of modified gravity on cluster scales in a model-independent way.