Distinguish the environmental effects and modified theory of gravity with multiple massive black-hole binaries

TL;DR

This paper explores how environmental effects and modified gravity theories influence gravitational wave signals from black-hole binaries, proposing a statistical method to distinguish different models using multiple detections.

Contribution

It introduces a statistical approach to differentiate between environmental effects and modified gravity theories in gravitational wave data analysis using multiple black-hole binary events.

Findings

The proposed statistic effectively distinguishes between different models.

Varying-G theory and dark matter spike effects modify waveforms at -4PN order.

Multiple sources can be used to identify degenerate effects in gravitational wave signals.

Abstract

In the typical data analysis and waveform modeling of the gravitational waves (GWs) signals for binary black holes (BBHs), it is assumed to be isolate sources in the vacuum within the theory of general relativity (GR). However, various kinds of matter may exist around the source or on the path to the detector, and there also exist many different kinds of modified theories of gravity. The effects of these modifications can be characterized within the parametrized post-Einstein (ppE) framework, and the corresponding phase corrections on the waveform at leading post-Newtonian (PN) order are also expressed by the additional parameters for these effects. In this work, we consider the varying-G theory and the dynamical friction of the dark matter spike as an example. Both of these two effects will modify the waveform at -4PN order, if we choose the suitable power law index for the spike. We choose to use a statistic to characterize the dispersion between the posterior of $\dot G$ for different events. For different astronomical models, we find that this statistic can distinguish these two models very effectively. This result indicates that we could use this statistic to distinguish other degenerate effects with the detection of multiple sources.