The influence of short term variations in AM CVn systems on LISA measurements

TL;DR

This paper investigates how short term variations in AM CVn systems affect their gravitational wave signals and LISA's ability to detect and analyze these signals, highlighting the need for improved modeling.

Contribution

It introduces a model for short term variations in AM CVn systems and assesses their impact on gravitational wave detection and parameter estimation by LISA.

Findings

Short term variations significantly alter higher order terms in gravitational wave signals.

Such variations can cause spurious detection of frequency derivatives.

Detection of signals remains feasible with current templates despite variations.

Abstract

We study the effect of short term variations of the evolution of AM CVn systems on their gravitational wave emissions and in particular LISA observations. We model the systems according to their equilibrium mass-transfer evolution as driven by gravitational wave emission and tidal interaction, and determine their reaction to a sudden perturbation of the system. This is inspired by the suggestion to explain the orbital period evolution of the ultra-compact binary systems V407 Vul and RX-J0806+1527 by non-equilibrium mass transfer. The characteristics of the emitted gravitational wave signal are deduced from a Taylor expansion of a Newtonian quadrupolar emission model, and the changes in signal structure as visible to the LISA mission are determined. We show that short term variations can significantly change the higher order terms in the expansion, and thus lead to spurious (non) detection of frequency derivatives. This may hamper the estimation of the parameters of the system, in particular their masses and distances. However, we find that overall detection is still secured as signals still can be described by general templates. We conclude that a better modelling of the effects of short term variations is needed to prepare the community for astrophysical evaluations of real gravitational wave data of AM CVn systems.