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Table 4.

CURN + CGW and GWB + CGW search results on all five datasets containing either a GWB or a CGW.

Dataset B CURN CURN + CGW $ \mathcal{B}^{\mathrm{CURN+CGW}}_{\mathrm{CURN}} $ fcgw [nHz] log10hcgw B GWB GWB + CGW $ \mathcal{B}^{\mathrm{GWB+CGW}}_{\mathrm{GWB}} $ log10AGWB γGWB log10h95%
injGWB01 3.6 2 . 48 1.21 + 14.01 $ 2.48_{-1.21}^{+14.01} $ 14 . 12 0.84 + 0.63 $ -14.12_{-0.84}^{+0.63} $ 0.75 14 . 71 0.37 + 0.30 $ -14.71_{-0.37}^{+0.30} $ 4 . 49 0.70 + 0.65 $ 4.49_{-0.70}^{+0.65} $ −14.19
injGWB02 1.0 3 . 42 1.22 + 23.58 $ 3.42_{-1.22}^{+23.58} $ 14 . 29 0.93 + 0.19 $ -14.29_{-0.93}^{+0.19} $ 0.7 14 . 16 0.19 + 0.11 $ -14.16^{+0.11}_{-0.19} $ 3 . 41 0.24 + 0.35 $ 3.41^{+0.35}_{-0.24} $ −13.92
injGWB03 1.3 4 . 71 2.57 + 31.31 $ 4.71_{-2.57}^{+31.31} $ 14 . 40 1.11 + 0.62 $ -14.40_{-1.11}^{+0.62} $ 0.6 14 . 46 0.16 + 0.15 $ -14.46_{-0.16}^{+0.15} $ 4 . 07 0.30 + 0.32 $ 4.07_{-0.30}^{+0.32} $ −13.71
injCGW5 > 1000 5 . 07 0.06 + 0.04 $ 5.07_{-0.06}^{+0.04} $ 13 . 90 0.16 + 0.14 $ -13.90_{-0.16}^{+0.14} $ > 1000 15 . 57 1.17 + 0.99 $ -15.57_{-1.17}^{+0.99} $ 4 . 33 2.19 + 1.59 $ 4.33_{-2.19}^{+1.59} $
injCGW20 > 1000 20 . 04 0.06 + 0.08 $ 20.04_{-0.06}^{+0.08} $ 13 . 66 0.10 + 0.11 $ -13.66_{-0.10}^{+0.11} $ > 1000 14 . 09 1.37 + 0.19 $ -14.09_{-1.37}^{+0.19} $ 2 . 84 0.45 + 1.81 $ 2.84_{-0.45}^{+1.81} $

Notes. Columns are: 1) dataset to which the search is applied, 2) Bayes factor of CURN + CGW vs. CURN models, 3) median and 68% credible interval of the CGW frequency and 4) CGW amplitude recovered with the CURN + CGW model, 5) Bayes factor of GWB + CGW vs. GWB models, 6) median and 68% of the GWB amplitude and 7) GWB slope recovered with GWB + CGW model, 8) 95% upper limit on the CGW amplitude recovered with the GWB + CGW model. The last column is not reported for the injCGW datasets, for which the CGW was detected and the posterior on its amplitude well constrained.

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