Table 1.
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Model posteriors as |$[P(\rm {\Lambda CDM}\vert \boldsymbol{d}), \mathit{ P}(\rm {EDE}\vert \boldsymbol{d})]$| for two model prior assumptions: (1) the flat prior is a 50 per cent prior for each model and (2) the Occam’s prior with a 90 per cent prior on ΛCDM versus a 10  per cent prior on EDE.

Model posterior (ΛCDM, EDE) in
|$[P(\rm {\Lambda CDM}\vert \boldsymbol{d}), \mathit{ P}(\rm {EDE}\vert \boldsymbol{d})]$| (in per cent)
Data setFlat priorOccam’s prior
CMB[50.9, 49.1][90.3, 9.7]
CMB + Lensing + BAO + SN[1.2, 98.8][9.5, 90.5]
BAO + BBN[78.7, 21.3][97.1, 2.9]
BAO + BBN + SN[0.3, 99.7][2.5, 97.5]
SN[82.8, 17.2][97.7, 2.3]
Model posterior (ΛCDM, EDE) in
|$[P(\rm {\Lambda CDM}\vert \boldsymbol{d}), \mathit{ P}(\rm {EDE}\vert \boldsymbol{d})]$| (in per cent)
Data setFlat priorOccam’s prior
CMB[50.9, 49.1][90.3, 9.7]
CMB + Lensing + BAO + SN[1.2, 98.8][9.5, 90.5]
BAO + BBN[78.7, 21.3][97.1, 2.9]
BAO + BBN + SN[0.3, 99.7][2.5, 97.5]
SN[82.8, 17.2][97.7, 2.3]
Table 1.
Open in new tab

Model posteriors as |$[P(\rm {\Lambda CDM}\vert \boldsymbol{d}), \mathit{ P}(\rm {EDE}\vert \boldsymbol{d})]$| for two model prior assumptions: (1) the flat prior is a 50 per cent prior for each model and (2) the Occam’s prior with a 90 per cent prior on ΛCDM versus a 10  per cent prior on EDE.

Model posterior (ΛCDM, EDE) in
|$[P(\rm {\Lambda CDM}\vert \boldsymbol{d}), \mathit{ P}(\rm {EDE}\vert \boldsymbol{d})]$| (in per cent)
Data setFlat priorOccam’s prior
CMB[50.9, 49.1][90.3, 9.7]
CMB + Lensing + BAO + SN[1.2, 98.8][9.5, 90.5]
BAO + BBN[78.7, 21.3][97.1, 2.9]
BAO + BBN + SN[0.3, 99.7][2.5, 97.5]
SN[82.8, 17.2][97.7, 2.3]
Model posterior (ΛCDM, EDE) in
|$[P(\rm {\Lambda CDM}\vert \boldsymbol{d}), \mathit{ P}(\rm {EDE}\vert \boldsymbol{d})]$| (in per cent)
Data setFlat priorOccam’s prior
CMB[50.9, 49.1][90.3, 9.7]
CMB + Lensing + BAO + SN[1.2, 98.8][9.5, 90.5]
BAO + BBN[78.7, 21.3][97.1, 2.9]
BAO + BBN + SN[0.3, 99.7][2.5, 97.5]
SN[82.8, 17.2][97.7, 2.3]
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