Uploaded August 2020 | Updated September 2026, 1 hour ago
Maximum Likelihood Map-making with LISA
Arianna Renzini
We have developed a map-maker based on an optimal quadratic estimator to assess the abilities of the LISA satellite network to reconstruct
anisotropies of different angular scales and in different directions on the sky. The resulting maps are maximum likelihood representations of the GWB intensity on the sky integrated over a broad range of frequencies and averaged over observation time. I will present the mapping recipe and show tests of the algorithm obtained by reconstructing known input maps with different input distributions over different frequency ranges, with different SNRs. I will also present maps of the directional dependence of LISA noise, providing insight on the directional sky sensitivity we may expect.
Maximum Likelihood Map-making with LISA
Arianna Renzini
We have developed a map-maker based on an optimal quadratic estimator to assess the abilities of the LISA satellite network to reconstruct
anisotropies of different angular scales and in different directions on the sky. The resulting maps are maximum likelihood representations of the GWB intensity on the sky integrated over a broad range of frequencies and averaged over observation time. I will present the mapping recipe and show tests of the algorithm obtained by reconstructing known input maps with different input distributions over different frequency ranges, with different SNRs. I will also present maps of the directional dependence of LISA noise, providing insight on the directional sky sensitivity we may expect.
