Systematic KMTNet Planetary Anomaly Search. VI. Complete Sample of 2018 Sub-prime-field Planets

We complete the analysis of all 2018 sub-prime-field microlensing planets identified by the KMTNet AnomalyFinder. Among the 9 previously unpublished events with clear planetary solutions, 6 are clearly planetary (OGLE-2018-BLG-0298, KMT-2018-BLG-0087, KMT-2018-BLG-0247, KMT-2018-BLG-0030, OGLE-2018-BLG-1119, and KMT-2018-BLG-2602), while the remaining 3 are ambiguous in nature. The above ordering of these events is made to facilitate grouping of their Bayesian estimates: the first two are lower-mass gas giants while the last four are Jovian-class planets; the first three most likely lie in the bulge, the last in the disk, and the remaining two are equally likely to be in either population. More specifically, these six planets have host masses ${M}_{mathrm{host}}=({0.69}_{-0.30}^{+0.34},{0.10}_{-0.05}^{+0.14},{0.29}_{-0.14}^{+0.28},{0.51}_{-0.31}^{+0.43},{0.48}_{-0.28}^{+0.35},{0.66}_{-0.36}^{+0.42}){M}_{odot }$ , planet masses ${M}_{mathrm{planet}}=({0.14}_{-0.06}^{+0.07},{0.23}_{-0.12}^{+0.32},{2.11}_{-1.04}^{+2.09},{1.45}_{-0.88}^{+1.23},{0.91}_{-0.52}^{+0.66},{1.15}_{-0.63}^{+0.73}){M}_{mathrm{Jup}}$ , and distances ${D}_{L}=({6.54}_{-1.23}^{+0.95},{7.02}_{-1.15}^{+1.03},{6.76}_{-1.24}^{+0.99},{6.48}_{-1.96}^{+1.28},{5.76}_{-2.48}^{+1.43},{4.31}_{-1.84}^{+1.97})mathrm{kpc}$ . In addition, there are 8 previously published sub-prime-field planets that were selected by the AnomalyFinder algorithm. Together with a companion paper on 2018 prime-field planets, this work lays the basis for comprehensive statistical studies. We carry out two such studies, one on caustic topologies and the other on the role of Gaia data. From the first, as expected, half (17/33) of the 2018 planets likely to enter the mass-ratio analysis have non-caustic-crossing anomalies. However, only 1 of the 5 noncaustic anomalies with planet-host mass ratio q < 10<SUP>-3</SUP> was discovered by eye (compared to 7 of the 12 with q > 10<SUP>-3</SUP>), showing the importance of the semiautomated AnomalyFinder search. From the second, we find that Gaia has played a major role in the interpretation of 16% of the sample and a supplementary role in 6%.