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Table 2.

MDN performance evaluation, without any clipping for the average and rms, without any threshold on branch membership probabilities.

IGMM Photometry ⟨Δz⟩ rms(Δz) 3σ outliers ⟨Δz⟩, rms(Δz), 3σ outliers, rms(Δz), rms(Δz),
implementation (all) (all) (all) range1 (a) range1 (a) range1 (a) range2 (b) range3 (c)
Fully unsup. griz, W1, W2 0.0152 0.2174 3.08% 0.0007 0.0177 0.28% 0.0988 0.0945
Spec. class griz, W1, W2 0.0111 0.2069 1.31% 0.0006 0.0167 0.41% 0.0822 0.0783
Spec. class (d) griz, W1, W2 0.0356 0.2300 1.35% 0.0110 0.0260 0.71% 0.0953 0.0903
Redshift (zs) griz, W1, W2 0.0176 0.2131 3.21% −0.0009 0.0174 0.38% 0.0896 0.0873
Spec. class, zs griz, W1, W2 0.0047 0.1990 2.66% 0.0036 0.0181 0.57% 0.0675 0.0664
Spec. class ugriz, W1, W2 0.0135 0.1592 1.62% 0.0007 0.0160 0.23% 0.0601 0.0611

Notes. Spectroscopic sample for all IGMM implementations containing stars, galaxies and quasars.

(a)

Restricted to galaxies with zs < 0.3;

(b)

restricted to galaxies with zs < 0.4;

(c)

restricted to galaxies with zs < 0.5;

(d)

expectation value.

Fully unsup.: Fully unsupervised IGMM implementation. Spec. class: uses spectroscopically classified objects (e.g., stars, galaxies, and quasars) for those which SDSS provides spectroscopic information (i.e., ≈2% of the photometric data set) to classify the data. Redshift (zs): uses the spectroscopic redshift of objects from ≈2% of the photometric data set to classify the data. Spec. class, (zs): uses both, spectroscopically classified objects and the spectrsocopic redshift of objects from ≈2% of the photometric data set to classify the data.

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