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Table 3.

Impact of multimodality on different datasets.

Experiences σ η ⟨Δz Count
10−3 % 10−3 103
SDSS

Baseline 07.99 0.18 0.34 516.5
Multimodal 07.85 0.16 0.31 516.5
G(M) 1.74% 10.88% 6.28%
pvalue 0.0 0.0 0.0

CFHTLS

Baseline 16.01 0.85 0.22 108.5
Multimodal 15.35 0.79 0.29 108.5
G(M) 4.13% 7.22% −24.05%
pvalue 0.0 0.0002 0.15

HSC-6b

Baseline 09.14 1.25 1.97 46.8
Multimodal 08.87 1.20 1.63 46.8
G(M) 2.96% 3.94% 17.33%
pvalue 0.0 0.0575 0.04

HSC-9b

Baseline 08.41 1.24 1.58 33.1
Multimodal 07.60 1.19 1.64 33.1
G(M) 10.1% 3.67% −3.1%
pvalue 0.0 0.11 0.40

HSC-9b with 3DHST redshifts

Baseline 14.44 2.46 13.28 2.2
Multimodal 13.88 2.37 10.6 2.2
G(M) 3.93% 3.71% 20.19%
pvalue 0.069 0.27 0.10

HSC-9b with PRIMUS redshifts

Baseline 12.34 2.66 11.84 15
Multimodal 11.38 1.85 09.23 15
G(M) 7.74% 30.4% 22.01%
pvalue 0.0 0.0 0.0

HSC-9b with COSMOS2020 photometric redshifts

Baseline 12.01 1.01 8.74 43.7
Multimodal 11.46 0.83 6.82 43.7
G(M) 4.57% 17.08% 21.97%
pvalue 0.0 0.0 0.0001

Notes. The MAD, the outlier fraction, and the bias are reported for the baseline and the multimodal models, along with the relative difference and the pvalue as a measure of the significativity of the observed difference. The sizes of the datasets down to i = 24 are reported in the last column. For the nine-band experiments, some objects were missing the j band, so we used redshift estimations of models trained in those conditions.

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