4.6 Article

Improved reconstruction of a stochastic gravitational wave background with LISA

出版社

IOP Publishing Ltd
DOI: 10.1088/1475-7516/2021/01/059

关键词

gravitational wave detectors; gravitational waves / experiments; gravitational waves / sources; gravitational waves / theory

资金

  1. Department of Energy [DE-SC0009919]
  2. Simons Foundation [SFARI 560536]
  3. Science and Technology Facilities Council [ST/P000762/1]
  4. National Science Foundation [NSF PHY-1748958]
  5. ROMFORSK grant [302640]
  6. GWverse COST Action Black holes, gravitational waves and fundamental physics [CA16104]
  7. CNES DIA-PF post-doctoral fellowship program
  8. Italian Ministry of Education, University and Research (MIUR) through the Dipartimenti di eccellenza project Science of the Universe
  9. STFC [ST/P000762/1] Funding Source: UKRI

向作者/读者索取更多资源

A data analysis methodology was proposed for model-independent reconstruction of the spectral shape of a stochastic gravitational wave background with LISA; The improved algorithm includes a complete set of TDI channels, reducing uncertainties, avoiding local extrema in likelihood maximization, and breaking degeneracies between signal and instrumental noise; Testing with case studies showed that additional channels are helpful in various ways for the reconstruction process.
We present a data analysis methodology for a model-independent reconstruction of the spectral shape of a stochastic gravitational wave background with LISA. We improve a previously proposed reconstruction algorithm that relied on a single Time-Delay-Interferometry (TDI) channel by including a complete set of TDI channels. As in the earlier work, we assume an idealized equilateral configuration. We test the improved algorithm with a number of case studies, including reconstruction in the presence of two different astrophysical foreground signals. We find that including additional channels helps in different ways: it reduces the uncertainties on the reconstruction; it makes the global likelihood maximization less prone to falling into local extrema; and it efficiently breaks degeneracies between the signal and the instrumental noise.

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