Table D.5.
Comparison between our image-based CNN model and two different photometric catalogue-based approaches, referred to the EXP3 experiment.
R2248 z = 0.346 | |||||
---|---|---|---|---|---|
CNN | RF | Bayesian | Δ | ||
AE | 88.1 | 86.5 | 85.9 | 1.6 | |
pur | 88.3 | 87.7 | 80.9 | 0.6 | |
CLM | compl | 89.8 | 87.7 | 96.1 | −6.3 |
F1 | 89.1 | 87.7 | 87.8 | 1.3 | |
pur | 87.9 | 85.1 | 94.4 | −6.5 | |
NCLM | compl | 86.1 | 85.1 | 74.4 | 1.0 |
F1 | 87.0 | 85.1 | 83.2 | 1.9 | |
μΔ | −0.91 ± 1.42 | ||||
M0416 z = 0.397 | |||||
CNN | RF | Bayesian | Δ | ||
AE | 92.2 | 89.2 | 87.1 | 3.0 | |
pur | 93.3 | 93.0 | 84.6 | 0.3 | |
CLM | compl | 87.1 | 86.5 | 91.2 | −4.1 |
F1 | 91.5 | 89.7 | 87.8 | 1.8 | |
pur | 89.0 | 84.5 | 90.0 | −1.0 | |
NCLM | compl | 96.9 | 92.3 | 82.7 | 4.6 |
F1 | 91.5 | 88.3 | 86.2 | 3.2 | |
μΔ | 1.11 ± 1.12 | ||||
M1206 z = 0.439 | |||||
CNN | RF | Bayesian | Δ | ||
AE | 89.7 | 87.9 | 85.0 | 1.8 | |
pur | 89.9 | 90.4 | 80.2 | −0.5 | |
CLM | compl | 86.5 | 81.9 | 91.2 | −4.7 |
F1 | 88.2 | 85.9 | 85.3 | 2.3 | |
pur | 89.6 | 86.3 | 90.8 | −1.2 | |
NCLM | compl | 92.3 | 92.9 | 79.4 | −0.6 |
F1 | 90.9 | 89.7 | 84.7 | 1.2 | |
μΔ | −0.24 ± 0.90 | ||||
M1149 z = 0.542 | |||||
CNN | RF | Bayesian | Δ | ||
AE | 89.4 | 86.9 | 85.5 | 2.5 | |
pur | 82.3 | 78.8 | 71.8 | 3.5 | |
CLM | compl | 91.3 | 88.5 | 98.0 | −6.7 |
F1 | 86.6 | 83.4 | 82.9 | 3.2 | |
pur | 94.5 | 92.7 | 98.6 | −4.1 | |
NCLM | compl | 88.3 | 86.0 | 78.4 | 2.3 |
F1 | 91.3 | 83.4 | 87.4 | 3.9 | |
μΔ | 0.66 ± 1.60 |
Notes. The comparison involves two different model: a Random Forest and a Bayesian method, applied on photometric tabular information of four clusters: R2248 (z = 0.346), M0416 (z = 0.397), M1206 (z = 0.439) and M1149 (z = 0.542). Last column (Δ) shows the difference between CNN estimators and the best between the two photometric approaches, i.e. Δestim = estimCNN − max{estimRF, estimBayesian} for estim ∈ [pur, compl, F1, AE], while rows μΔ list the averages among these Δs for each cluster.
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