Table 3.
Parameter distributions of the two types of simulations used to generate the training data for each GREAT3 subfield.
| Branch type: | Ground-based |
Space-based |
||
|---|---|---|---|---|
| Training set to learn the prediction of: | Point estimate | Weight | Point estimate | Weight |
| Simulation type: | Uniform | Mock | Uniform | Mock |
| Shear components g1, g2 | 0 | 𝒰(−0.1, 0.1) | 0 | 𝒰(−0.1, 0.1) |
| Galaxy ellipticity modulus εtrue | ℛ(0.2)[0,0.7] | ℛ(0.2)[0,0.7] | ℛ(0.2)[0,0.7] | ℛ(0.2)[0,0.7] |
| Sérsic indexan | 𝒰(0.5, 4) | 𝒰(0.5, 2.5) | 𝒰(0.5, 4) | 𝒰(0.5, 4) |
| Flux F (counts) | 𝒰(10, 100) | 𝒩(15, 20)[10,200] | 𝒰(10, 100) | 𝒩(0, 30)[10, 200] |
| Half-light radius R (pix) | 𝒰(0.75, 3.0) | 𝒩(1.0, 0.8)[0.75, 3.0] | 𝒰(1.25, 10.0) | 𝒩(2.5, 3.5)[1.25, 10.0] |
Notes.
We use 𝒰(a, b) to denote the uniform distribution between a and b, 𝒩(μ, σ) to denote a normal distribution with mean μ and variance σ2, and ℛ(σ) for a Rayleigh distribution with mode σ. Intervals in subscript denote the range to which we clip a distribution, so that no sample falls outside of the given interval.
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