Table A.2.
Summary of marginalised parameter constraints with 68% confidence intervals. Priors and parameter definitions are shown in Table 2.
| Parameter | KiDS-1000 PCL | KiDS-1000 PCL | KiDS-1000 PCL + SDSS | KiDS-1000 PCL + SDSS |
|---|---|---|---|---|
| (PJ-HPD) | (Marginal) | (PJ-HPD) | (Marginal) | |
| S8 |
 |
 |
 |
 |
| Ωch2 |
 |
 |
 |
 |
| Ωbh2 |
 |
 |
 |
 |
| h |
 |
 |
 |
 |
| ns |
 |
 |
 |
 |
| Ωm |
 |
 |
 |
 |
| σ8 |
 |
 |
 |
 |
| AIA |
 |
 |
 |
 |
| Abary |
 |
 |
 |
 |
| δz1 |
 |
 |
 |
 |
| δz2 |
 |
 |
 |
 |
| δz3 |
 |
 |
 |
 |
| δz4 |
 |
 |
 |
 |
| δz5 |
 |
 |
 |
 |
Notes. Here we show the marginalised constraints for using the maximum a posteriori values with the projected joint highest posterior density (PJ-HPD) intervals and the usual one-dimensional marginalised constraints (Marginal). As in Asgari et al. (2021), we show the constrained parameters in bold, while parameters that are not constrained by the current data or data combination are mostly just reflecting their priors. Although h is constrained by the SDSS data, the value we find is a reflection of our BBN-inspired prior on Ωbh2.
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