Table 1
Results of fitting different distributions to the observed geometric albedo data (see Figure 3).
| Distribution | Parameter | Best-fit value | BIC |
|---|---|---|---|
| Rayleigh | σCH | 0.088 ± 0.013 | −336.2 |
| σT | 0.089 ± 0.021 | –349.3 | |
| Half-Gaussian | σCH | 0.128 ± 0.027 | −335.4 |
| σT | 0.129 ± 0.037 | –348.7 | |
| Beta | (log(aCH), | (0.264 ± 0.209, | |
| log(bCH)) | 1.221 ± 0.216) | –333.3 | |
| (log(aT), | (0.509 ± 0.528 | ||
| log(bT)) | 1.488 ± 0.496) | –346.3 |
Notes. We fitted independent distributions to the CKC and TESS datasets. Both Rayleigh and the half-Gaussian distribution have only one free parameter (σ) whereas the Beta distribution has two (a, b), which we fitted for in log space to improve the efficiency of our sampling. The best-fit values show that all parameters are consistent across bandpass, indicating the distributions (CKC and TESS) are not significantly different. The BIC results show the three models fit the data similarly well, as the values are all within ten points of each other. However we still do see - and one can confirm this by eye by looking at Figure 3 - that the Rayleigh distribution performs the best for both bandpass datasets, as it has the lowest BIC value.
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