Time delay likelihoods modeled with Gaussian processes develop a boundary-driven W-shape with a global maximum at the true delay and rises at observation window edges, misleading nested sampling and biasing H0 high.
If σis comparable toAor larger,y 1 andy 2 lose their mu- tual correlation, which does not align with the real data sets considered here
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Global structure of the time delay likelihood
Time delay likelihoods modeled with Gaussian processes develop a boundary-driven W-shape with a global maximum at the true delay and rises at observation window edges, misleading nested sampling and biasing H0 high.