Effect of noise characterization on the detection of mHz stochastic gravitational waves

Effect of noise characterization on the detection of mHz stochastic gravitational waves
Nikolaos Karnesis, Quentin Baghi, Jean-Baptiste Bayle, Nikiforos Galanis
arXiv:2601.19741v2 Announce Type: replace-cross
Abstract: Pulsar timing arrays’ hint for a stochastic gravitational-wave background (SGWB) leverages the expectations of a future detection in the millihertz band, particularly with the LISA space mission. However, finding an SGWB with a single orbiting detector is challenging: It calls for cautious modelling of instrumental noise, which is also mainly stochastic. It was shown that agnostic noise reconstruction methods provide robustness in the detection process. We build on previous work to include more realistic instrumental simulations and additional degrees of freedom in the noise inference model and analyze the impact of LISA’s sensitivity to SGWBs. Particularly, we model the two main types of noise sources with separate transfer functions and power spectral density spline fitting. We assess the detectability bounds and their dependence on the flexibility of the noise model and on the prior probability, allowing us to refine previously reported results.arXiv:2601.19741v2 Announce Type: replace-cross
Abstract: Pulsar timing arrays’ hint for a stochastic gravitational-wave background (SGWB) leverages the expectations of a future detection in the millihertz band, particularly with the LISA space mission. However, finding an SGWB with a single orbiting detector is challenging: It calls for cautious modelling of instrumental noise, which is also mainly stochastic. It was shown that agnostic noise reconstruction methods provide robustness in the detection process. We build on previous work to include more realistic instrumental simulations and additional degrees of freedom in the noise inference model and analyze the impact of LISA’s sensitivity to SGWBs. Particularly, we model the two main types of noise sources with separate transfer functions and power spectral density spline fitting. We assess the detectability bounds and their dependence on the flexibility of the noise model and on the prior probability, allowing us to refine previously reported results.