Uploaded July 2022 | Updated September 2026, 2 hours ago
Extreme Mass Ratio Inspirals (EMRIs) are expected to be among the primary sources of gravitational wave for LISA. In general, the SNR threshold for the EMRI detectability is fixed to 20, to allow the complexity of the waveform, but mainly just the systems at z less than 1 can reach this value. For that reason, the ensemble of sources below the detection threshold will add up together in unresolved confusion noise.
In this work we compute the stochastic gravitational wave background (SGWB) for different cosmic population of EMRIs, which have been built by considering the most important ingredients affecting the EMRI formations.
We decide to use the Augmented Analytic Kludge (AAK) with the 5PN fluxes for generic Kerr orbits to compute the EMRI waveform.
After injecting the signals in LISA, we estimate the SGWB removing the resolvable sources with an iterative algorithm.
Eventually we perform a comparison of the outcomes with the backgrounds computed exploiting the Analytic Kludge (AK).
Authors: Federico Pozzoli, Stanislav Babak, Nikolaos Karnesis, Alberto Sesana, Matteo Bonetti
Presenter: Federico Pozzoli
Extreme Mass Ratio Inspirals (EMRIs) are expected to be among the primary sources of gravitational wave for LISA. In general, the SNR threshold for the EMRI detectability is fixed to 20, to allow the complexity of the waveform, but mainly just the systems at z less than 1 can reach this value. For that reason, the ensemble of sources below the detection threshold will add up together in unresolved confusion noise.
In this work we compute the stochastic gravitational wave background (SGWB) for different cosmic population of EMRIs, which have been built by considering the most important ingredients affecting the EMRI formations.
We decide to use the Augmented Analytic Kludge (AAK) with the 5PN fluxes for generic Kerr orbits to compute the EMRI waveform.
After injecting the signals in LISA, we estimate the SGWB removing the resolvable sources with an iterative algorithm.
Eventually we perform a comparison of the outcomes with the backgrounds computed exploiting the Analytic Kludge (AK).
Authors: Federico Pozzoli, Stanislav Babak, Nikolaos Karnesis, Alberto Sesana, Matteo Bonetti
Presenter: Federico Pozzoli










