Table 4.
Bayesian linear regression coefficients strue versus sX relation.
| s200, 500 | s200, 2500 | s500, 2500 | ||||
|---|---|---|---|---|---|---|
| z | mX | cX | mX | cX | mX | cX |
| 0.0 | 1.65 ± 0.30 | −0.99 ± 0.41 | 1.40 ± 0.33 | −1.20 ± 1.40 | 0.89 ± 0.37 | 0.50 ± 1.30 |
| 0.2 | 0.98 ± 0.29 | −0.07 ± 0.48 | 1.52 ± 0.35 | −1.80 ± 1.50 | 1.77 ± 0.29 | −1.83 ± 0.84 |
| 0.5 | 1.46 ± 0.25 | −0.76 ± 0.36 | 1.59 ± 0.29 | −2.07 ± 0.92 | 1.64 ± 0.12 | −1.30 ± 0.45 |
| 0.8 | 1.41 ± 0.28 | −0.65 ± 0.36 | 1.44 ± 0.20 | −1.71 ± 0.68 | 1.88 ± 0.28 | −2.60 ± 0.63 |
| 1.0 | 0.87 ± 0.21 | 0.11 ± 0.28 | 1.37 ± 0.24 | −1.54 ± 0.87 | 1.19 ± 0.35 | −0.49 ± 1.00 |
Notes. Average and 1σ errors on the slope mX and intercept cX from the Bayesian linear regression fit of the sparsities from the X-ray estimated masses vs. the true sparsities from the GADGET-X sample for different overdensity configurations (columns from left to right) at z = 0, 0.2, 0.5, 0.8 and 1 (rows from top to bottom).
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