Table 2.
PDF models fit to the histogram of the flux measurements.
Model | Component | N0(a) | FmaxPD(b) | σlnF(c) | ΔTS (d) | Δdof (e) |
---|---|---|---|---|---|---|
(%) | (10−6 cm−2 s−1) | |||||
Single log-normal | … | 100 | 1.56 ± 0.06 | 0.94 ± 0.03 | 0 | 0 |
Double log-normal | A (f) | 5.6 ± 1.0 | 0.066 (fixed) | 0.51 (fixed) | 180.4 | 1 |
B | 94.4 ± 1.0 | 1.82 ± 0.05 | 0.67 ± 0.02 | |||
Triple log-normal | X (f) | 3.3 ± 1.0 | 0.066 (fixed) | 0.51 (fixed) | 233.6 | 4 |
Y | 31.2 ± 13.0 | 1.14 ± 0.33 | 1.07 ± 0.18 | |||
Z | 65.5 ± 13.9 | 2.10 ± 0.08 | 0.45 ± 0.06 |
Notes.
The normalization for scaling a component. In each model, the sum of normalizations of all components must be 1.
The flux corresponding to the maximum probability density. It is mathematically equivalent to the exponential of the mean of the flux’s natural logarithm.
The natural logarithm of the square of the likelihood ratio of a model compared to the single log-normal. The likelihood function is for a Poisson distribution.
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