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.

AI and ML enable rapid processing of large datasets, and offer advanced feature extraction and pattern recognition capabilities that deliver meaningful insights, enhancing human analysts' ability to identify correlations within complex, multi - variable datasets. This is especially needed for astrobiology, where m odels must distinguish complex biotic patterns fro m intricate abiotic backgrounds. As data volume outpaces the capacity for timely data analysis, AI and ML become essential for data processing. They could also prove invaluable for the complex data analysis that will accompany flight instruments ' advancements. ML has been widely applied in image processing of large datasets in astrophysics and Earth observation ( e.g., crater identification [2 - 4], sample targeting [5]). Similar techniques that share methodology but are improved for onboard computational rest rictions could be leveraged for astrobiology missions to identify key features [6].

In one of the most profound examples, the first image of a black hole was captured by applying a machine learning algorithm to petabytes of data collected from eight telescopes [1]. Since planetary science's last decadal survey, the use of machine learning has increased in each division of NASA's Science Mission Directorate (SMD). However, even though the number of planetary science publications involving machine learning has grown exponentially over the last ten years, it lags in both percent share and growth rate compared to heliophysics, astrophysics, and Earth science (Figure 1). In this white paper, we assert that planetary science, similar to related disciplines, has the opportunity to leverage machine learning methods for scientific advancement in our field.

Forty years ago this coming Tuesday, a car-sized piece of equipment launched from Cape Canaveral in Florida. Thirty five years later, it became the first and only man-made object to enter interstellar space. Along the way, the Voyager probes (there were two) made headlines for flybys of Jupiter, Saturn and Titan. Fran Bagenal was a student when the Voyager probes launched, and wrote her doctoral thesis on data the probes collected around Jupiter. The professor of astrophysical and planetary science at the University of Colorado at Boulder, and former chair of NASA's Outer Planet Assessment Group, has also worked on the Galileo, Deep Space 1, New Horizons and Juno missions. Nautilus caught up with Bagenal to discuss the legacy of Voyager and the future of manned and unmanned exploration of space.