Table 2.
Robust polynomial fits to Bayesian estimates of Δη and ζw.
| Dependent | a0 | a1 | a2 | a3 |
|---|---|---|---|---|
| variable | ||||
| x = log Mstar | ||||
| Δη | 0.80 ± 0.14 | −0.02 ± 0.01 | − | − |
| log ζw(b) | 22.518 ± 16.4 | −5.4453 ± 5.24 | 0.4462 ± 0.56 | −0.0120 ± 0.02 |
| log ζw(c) | 39.750 ± 1.9 | −10.7839 ± 0.61 | 0.9972 ± 0.06 | −0.0310 ± 0.002 |
| x = Fg | ||||
| Δη(a) | α | α d log Mg/d log Mstar | – | – |
| ζw(b) | 4.699 ± 0.54 | 3.4871 ± 1.39 | −1.0556 ± 0.76 | 0.1468 ± 0.11 |
| ζw(c) | 3.394 ± 0.34 | 2.0313 ± 0.87 | −0.2162 ± 0.48 | 0.0469 ± 0.07 |
Notes. d(x) = a0 + a1 x + a2 x2 + a3 x3, where d corresponds to the dependent variable, either Δη, ζw, or log ζw as defined in the table body, and x is the independent variable, either Fg or log Mstar. The coefficients are given to several digits because of the high degree of the polynomials; with only two digits, as would be consistent with the large uncertainties, the resulting values of the dependent variable would be incorrect.
This is not a fit, but rather Eq. (27).
Prior Δη ≥0 (Fig. A.2).
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