Weak-lensing shear measurement with machine learning - Teaching artificial neural networks about feature noise

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Volume 621 (January 2019)

A&A, 621 (2019) A36

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Table 1.

Galaxy parameters of the fiducial experiments.

Parameter	Distribution
Shear components g₁, g₂	𝒰(−0.1, 0.1)
Intrinsic galaxy ellipticity modulus ε^true	ℛ(0.2)_[0,0.6]
Sérsic index^an	𝒰(1.0, 4.0)
Half-light radius R (pix)	𝒰(2.0, 8.0)
Surface brightness S (pix⁻²)	𝒰(1.0, 15.0)

Notes.

We use 𝒰(a, b) to denote the uniform distribution between a and b, 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.

^(a)

In practice, we grid the values for the Sérsic index instead of drawing them from a continuous distribution. This significantly speeds up the galaxy stamp generation, as GalSim can reuse cached Sérsic profiles.

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