Table 3.
Training on tabular data from Virgo and evaluating on Fornax data.
| Averaged per galaxy | Average over all sources | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Method | TPR | FPR | FDR | AUC ROC | TPR | FPR | FDR | AUC ROC | # TPs | # FPs |
| Logistic Regression | 0.63 | 0.05 | 0.33 | 0.93 | 0.70 | 0.05 | 0.16 | 0.94 | 4331 | 832 |
| Support Vector Machine (linear) | 0.66 | 0.05 | 0.31 | – | 0.73 | 0.05 | 0.15 | – | 4474 | 793 |
| Support Vector Machine (radial) | 0.73 | 0.04 | 0.23 | – | 0.81 | 0.04 | 0.12 | – | 4993 | 656 |
| Nearest Neighbour | 0.86 | 0.05 | 0.26 | – | 0.90 | 0.06 | 0.14 | – | 5536 | 915 |
| 12 Nearest Neighbours | 0.86 | 0.04 | 0.19 | 0.98 | 0.91 | 0.04 | 0.11 | 0.98 | 5597 | 678 |
| Decision Tree | 0.87 | 0.05 | 0.24 | 0.97 | 0.89 | 0.05 | 0.13 | 0.98 | 5496 | 803 |
| Random Forest | 0.86 | 0.03 | 0.14 | 0.99 | 0.91 | 0.03 | 0.08 | 0.99 | 5602 | 470 |
| AdaBoost | 0.86 | 0.03 | 0.15 | 0.99 | 0.91 | 0.03 | 0.08 | 0.99 | 5596 | 473 |
| CatBoost | 0.89 | 0.04 | 0.19 | 0.98 | 0.93 | 0.04 | 0.10 | 0.99 | 5709 | 656 |
| Neural Network (29-1) | 0.61 | 0.05 | 0.33 | 0.92 | 0.68 | 0.05 | 0.16 | 0.93 | 4159 | 801 |
| Neural Network (29-100-100-1) | 0.89 | 0.04 | 0.21 | 0.98 | 0.93 | 0.05 | 0.11 | 0.99 | 5754 | 739 |
Notes. The Fornax data contains in total 21 767 sources with 6161 catalogued GCs. Results are given for a decision threshold of 0.5. Support vector machines and nearest neighbour only provide a class label and hence, no AUC ROC is reported. For neural networks, the number of neurons per layer is given in brackets.
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