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Table C.1.

Data and regularization terms used for the RML techniques ehtim, DoG-HIT, and MOEA/D.

Data Type Obj. Description Ref. weight Method
Data χCPh2 fit quality to closure phases 1 10 ehtim, DoG-HIT
χLCA2 log. of closure amplitudes 1 10 ehtim, MOEA/D, DoG-HIT
χamp2 amplitudes 1 0.1 ehtim, MOEA/D
χvis2 complex visibilities 1 0 ehtim
Reg. Rentr entropy 1 100 ehtim, MOEA/D
Rtv total variation 1 1 ehtim, MOEA/D
Rtv2 total squared variation 1 1 ehtim, MOEA/D
Rl1 l1-norm 1 0 ehtim, MOEA/D
Rflux total flux constraint 1 100 ehtim, MOEA/D
Rl1w l1-norm of wavelets 2 DoG-HIT

lin. pol. Data χpvis2 fit quality to LP visibility 𝒫 3 1 ehtim
χm2 visibility polarimetric ratio 𝒫/ℐ 3 1 ehtim
Reg. Rptv total variation of 𝒫 = 𝒬 + i𝒰 3 1 ehtim, DoG-HIT
Rms entropy of 𝒫 = 𝒬 + i𝒰 3 0 ehtim, MOEA/D
Rhw HW-entropy of lin. pol. fraction m 3 100 ehtim
Rhw + cp HW-entropy of total pol. fraction m + v 4 MOEA/D
1ms constrained to Stokes ℐ wavelets 5 DoG-HIT

circ. pol. Data χcvis2 fit quality to CP visibility 𝒱 6 0.1 ehtim, MOEA/D
Reg. Rl1v l1-norm of circ. pol. 6 0.1 ehtim
Rvtv total variation of circ. pol. 6 10.0 ehtim
Rhw + cp HW-entropy of total pol. fraction m + v 4 MOEA/D
1ms constrained to Stokes ℐ wavelets 5 DoG-HIT

Notes. We show the top-set weights for ehtim in the sixth column. MOEA/D surveys all weight combinations internally, DoG-HIT performs constrained optimization. References. 1: EHTC (2019d), 2: Müller & Lobanov (2022), 3: Chael et al. (2016), 4: Toscano et al. (in prep.), 5: Müller & Lobanov (2023a), 6: EHTC (2024c).

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