Open Access
Table 1
Features used for the machine learning classifier.
| Name | Description | Importance |
|---|---|---|
| pm | Estimated proper motion | 0.01 |
| e_pm | Statistical error of estimated proper motion | 0.17 |
| N | Number of epochs with detections | 0.10 |
| corr_30 | Pearson correlation coefficient of the coordinates vs time for all points within 3″ from expected position at different epochs | 0.23 |
| corr | Pearson correlation coefficient of the coordinates vs time for all points within 3σ brightness dependent coordinate uncertainty from expected position at different epochs | 0.16 |
| magstd | Actual RMS of individual magnitude measurements in W2 band | 0.14 |
| magerr | Mean error of individual magnitude measurements in W2 band | 0.06 |
| mag | Mean magnitude in W2 band | 0.05 |
| magchi2 | Reduced χ2 of individual magnitude measurements in W2 band | 0.02 |
| b | Galactic latitude | 0.01 |
| NNNN1 | Number of detections with 1 or more catalog points not belonging to the track within its 3σ positional uncertainty | 0.02 |
| NNNN3 | the same for 3 or more points | 0.01 |
| NNNN2 | the same for 2 or more points | 0.01 |
| NNNN4 | the same for 4 or more points | 0.001 |
| NNNN5 | the same for 5 or more points | 0.001 |
Notes. The classifier using these features is described in Section 2.1. The last column shows the relative importance of the features for the classification at the final iteration.
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