Exploring the potential of neural networks in early detection of potentially hazardous Near-Earth Objects

The solar system is home to a diverse population of small celestial objects, including asteroids, comets, and meteoroids. Most small bodies in the solar system are found in two distinct regions, known as the Main Belt and the Kuiper Belt. However, certain small bodies, such as Near-Earth Objects (NEOs), have orbits that bring them into close proximity with the Earth and in some cases even collide with our planet. The goal of Impact Monitoring (IM)
is to assess the risk of collision of a small body with Earth. Understanding the potential risk posed by an asteroid and monitoring objects with a higher risk of collision is crucial for developing strategies for planetary defense. Since in the following years, vast amounts of data from astronomical surveys will become available, it is essential to implement a preliminary filter to determine which objects should be prioritized for follow-up using traditional IM methods.

We present a novel method for estimating the Minimum Orbit Intersection Distance (MOID) of a NEO based on artificial Neural Networks (NNs). The MOID is defined as the minimum distance between the two osculating Keplerian orbits of the Earth and the NEO as curves in the three-dimensional space; it is usually used as an indicator of the possibilities of a collision between the asteroid and the Earth, at least for the period during which the Keplerian orbit of the asteroid provides a reliable approximation of the actual orbit. Since Machine Learning (ML) has gained enormous popularity in the last few years and has been applied also to some Celestial Mechanics problems, we decided to try to estimate the MOID with a multilayer feedforward NN, which takes as input the coordinates of the asteroid at a specified epoch. After being trained on an artificial dataset of about 800,000 NEOs generated with NEOPOP, the NN has been tested on the currently known population of Near-Earth Asteroids. The network exhibits near-instantaneous predictions of the MOID and achieves a mean absolute error of approximately 10−3 on the test set. Fig. 1 shows the histogram of the actual and predicted values. The overestimation of the number of asteroids with a MOID value of 0 is due to the activation function used in the final layer of the NN, namely ReLU, which, by definition, outputs 0 for any negative input. By selecting a threshold value of 0.05, we transformed the regression problem into a classification problem. In particular, we consider the positive class the one formed by all asteroids with a predicted MOID exceeding the threshold. The resulting accuracy and false positive rate (FPR) are approximately 96.61% and 2.56%, respectively. To reduce false positives, we propose to prioritize testing with classical IM methods every object with a predicted MOID of 0.10 or less. In fact, we believe that ML should serve as an initial screening tool, enabling us to prioritize follow-up assessments using traditional IM methods when managing large volumes of data.

Figure 1: Histogram of the actual and predicted values

As a follow-up, we are testing the possibility of developing a NN capable of predicting the MOID starting from computable quantities derived directly from the observations. This would eliminate the need to calculate a preliminary orbit and apply the differential corrections procedure.
Specifically, we intend to use as input vector for the NN an attributable (α, δ, ˙ α, ˙δ ), together with the second derivatives of right ascension and declination. In fact, given m ≥ 3 optical observations (αi, δi) at times ti, it is easy to compute, with a quadratic fit of both angular variables separately, the quantities α, ˙ α, ¨α and δ, ˙δ, ¨δ. Although this task is more difficult, both in terms of data acquisition and NN training, the preliminary findings are promising.

In conclusion, this research represents a step forward in addressing the urgent need for effective IM techniques, partially answering the question of whether ML can serve as a preliminary filter for some orbit determination problems.

[1] Vichi, V., Tommei, G. Exploring the potential of neural networks in early detection of
potentially hazardous near-earth objects, Celest Mech Dyn Astron 137, 17 (2025).