论文标题
检测到对火星的新影响时，使用机器学习来减少观察性偏见
Using Machine Learning to Reduce Observational Biases When Detecting New Impacts on Mars
论文作者
论文摘要
当前对火星（新鲜）影响的库存表明，对低热惯性的地区有很大的偏见。这些区域通常在视觉上明亮，影响会产生黑暗的冲刷和射线，从而使它们易于检测。预计在较高的热惯性区域的影响速率相似，但这些影响不足。这项研究研究了使用训练有素的机器学习分类器，以增加CTX数据对火星的新鲜影响的检测。这种方法发现了69种新的新鲜影响，这些影响已通过后续的Hirise图像得到了证实。我们发现，检查由热惯性（TI）值分区的候选人，仅由于大量的机器学习候选物而可能有助于减少观察偏置并增加已知的高TI影响的数量。
The current inventory of recent (fresh) impacts on Mars shows a strong bias towards areas of low thermal inertia. These areas are generally visually bright, and impacts create dark scours and rays that make them easier to detect. It is expected that impacts occur at a similar rate in areas of higher thermal inertia, but those impacts are under-detected. This study investigates the use of a trained machine learning classifier to increase the detection of fresh impacts on Mars using CTX data. This approach discovered 69 new fresh impacts that have been confirmed with follow-up HiRISE images. We found that examining candidates partitioned by thermal inertia (TI) values, which is only possible due to the large number of machine learning candidates, helps reduce the observational bias and increase the number of known high-TI impacts.
