用机器学习减少错误上报近地天体，每年省下数百小时观测时间。
Improving the discovery of near-Earth objects with machine-learning methods
- 基于digest2参数设计筛选规则，结合机器学习分类模型。
- 准确率超95%，可剔除80%以上非近地天体，仅损失5.5%真实天体。
- 适合天文观测资源优化与自动化预警系统开发者参考。
我们对2019至2024年间近地天体确认页（NEOCP）候选者所使用的digest2参数进行了全面分析。尽管近年近地天体发现数量大幅增加，但仅有约一半候选者最终被确认为近地天体，导致大量观测时间浪费在非目标对象上。此外，约11%的候选者因后续观测不足而无法确认，每年接近600例。为降低误报并减少资源浪费，我们基于对30类digest2参数的详细分析，提出新的发布筛选标准。通过筛选机制，可排除20%的非近地天体，同时几乎不丢失真实近地天体。进一步应用梯度提升机（GBM）、随机森林（RF）、随机梯度下降（SGD）及神经网络（NN）四种机器学习方法，以digest2参数为输入，模型在区分近地天体与非近地天体方面达到约95%的精度。结合参数过滤与机器学习模型，实现对非近地天体的剔除超过80%，且真实天体跟踪记录损失控制在5.5%以内。重要的是，多数最初误判的近地天体后续仍能被正确识别。
原文摘要 · Abstract (English)
We present a comprehensive analysis of the digest2 parameters for candidates of the Near-Earth Object Confirmation Page (NEOCP) that were reported between 2019 and 2024. Our study proposes methods for significantly reducing the inclusion of non-NEO objects on the NEOCP. Despite the substantial increase in near-Earth object (NEO) discoveries in recent years, only about half of the NEOCP candidates are ultimately confirmed as NEOs. Therefore, much observing time is spent following up on non-NEOs. Furthermore, approximately 11% of the candidates remain unconfirmed because the follow-up observations are insufficient. These are nearly 600 cases per year. To reduce false positives and minimize wasted resources on non-NEOs, we refine the posting criteria for NEOCP based on a detailed analysis of all digest2 scores. We investigated 30 distinct digest2 parameter categories for candidates that were confirmed as NEOs and non-NEOs. From this analysis, we derived a filtering mechanism based on selected digest2 parameters that were able to exclude 20% of the non-NEOs from the NEOCP while maintaining a minimal loss of true NEOs. We also investigated the application of four machine-learning (ML) techniques, that is, the gradient-boosting machine (GBM), the random forest (RF) classifier, the stochastic gradient descent (SGD) classifier, and neural networks (NN) to classify NEOCP candidates as NEOs or non-NEOs. Based on digest2 parameters as input, our ML models achieved a precision of approximately 95% in distinguishing between NEOs and non-NEOs. Results. Combining the digest2 parameter filter with an ML-based classification model, we demonstrate a significant reduction in non-NEOs on the NEOCP that exceeds 80%, while limiting the loss of NEO discovery tracklets to 5.5%. Importantly, we show that most follow-up tracklets of initially misclassified NEOs are later correctly identified as NEOs.
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