The capability of the Fermi Gamma-ray Burst Monitor (GBM) to localize gamma-ray bursts (GRBs) is evaluated for two different automated algorithms: the GBM Team's RoboBA algorithm and the independently developed BALROG algorithm. Through a systematic study utilizing over 500 GRBs with known locations from instruments like Swift and the Fermi LAT, we directly compare the effectiveness of, and accurately estimate the systematic uncertainty for, both algorithms. We show simple adjustments to the GBM Team's RoboBA, in operation since early 2016, yields significant improvement in the systematic uncertainty, removing the long tail identified in the systematic, and improves the overall accuracy. The systematic uncertainty for the updated RoboBA localizations is $1.8^\circ$ for 52% of GRBs and $4.1^\circ$ for the remaining 48%. Both from public reporting by BALROG and our systematic study, we find the systematic uncertainty of $1-2^\circ$ quoted in GCN circulars for bright GRBs localized by BALROG is an underestimate of the true magnitude of the systematic, which we find to be $2.7^\circ$ for 74% of GRBs and $33^\circ$ for the remaining 26%. We show that, once the systematic uncertainty is considered, the RoboBA 90% localization confidence regions can be more than an order of magnitude smaller in area than those produced by BALROG.

中文翻译：

---

伽马射线爆发的自动费米 GBM 定位的评估

费米伽马射线暴监测器 (GBM) 定位伽马射线暴 (GRB) 的能力针对两种不同的自动算法进行了评估：GBM 团队的 RoboBA 算法和独立开发的 BALROG 算法。通过使用来自 Swift 和 Fermi LAT 等仪器的 500 多个具有已知位置的 GRB 的系统研究，我们直接比较了两种算法的有效性，并准确估计了这两种算法的系统不确定性。我们展示了自 2016 年初开始运行的 GBM 团队的 RoboBA 的简单调整，显着改善了系统不确定性，消除了系统中识别的长尾，并提高了整体准确性。更新后的 RoboBA 定位的系统不确定性为 52% 的 GRB 为 1.8 美元，其余 48% 为 4.1 美元。从 BALROG 的公开报告和我们的系统研究中，我们发现 BALROG 定位的明亮 GRB 的 GCN 通告中引用的 $1-2^\circ$ 的系统不确定性低估了系统的真实幅度，我们发现这是74% 的 GRB 为 $2.7^\circ$，其余 26% 为 $33^\circ$。我们表明，一旦考虑系统不确定性，RoboBA 90% 定位置信区域的面积可能比 BALROG 产生的区域小一个数量级以上。