Data used in this tool are about unconfirmed objects and all information should therefore be treated as potentially unreliable


NEOScan is a system dedicated to the scan of the Minor Planet Center NEO Confirmation Page (NEOCP). The goal is to identify asteroids as NEAs, MBAs or distant objects to confirm or remove from the NEOCP and to give early warning of imminent impactors, to trigger follow-up observations. NEOScan uses a method based on the systematic ranging and on the Admissible Region theory (Milani et al. (2004)). The mathematical theory and a detailed description of the algorithms are presented in Del Vigna (2018), PhD thesis, Spoto et al. (2018), and Del Vigna (2020).

The main steps of the service can be summarised as follows:

Impact flag

We assign to each NEOCP object an integer flag to quantify the impact risk. It is conceived as a simple and direct communication tool to assess the importance of collision predictions and to give the priority for the follow-up activities. It is called impact flag and it depends on the impact probability value and on the arc curvature, as shown in the table below. We say that an arc has significant curvature if χ2 > 10, where χ is the chi-value of the geodesic curvature and the acceleration. The impact flag can take the integer values from 0 to 4: a 0 value indicates a negligible chance of collision with the Earth, whereas the maximum value 4 express an elevated impact risk.

Impact flag Risk Condition
0 Negligible IP ≤ 10-4
1 Very small 10-4 < IP ≤ 10-3
2 Small 10-3 < IP ≤ 10-2
3 Moderate IP > 10-2 and no significant curvature
4 Elevated IP > 10-2 and significant curvature

The impact probabilities computed by the software are based on observation error statistics, assigned by means of an astrometric observations error model based upon Gaussian (normal) distributions. Because the number of individual observations for each object is very small, the law of large numbers does not apply. Thus the actual errors of the observations included in a single tracklet, normally between 3 and 5, may not be a representative sample of the corresponding random variable, which is normally distributed.

In simple words, the probability for a single tracklet to have large errors is small, but not as small as detecting an imminent impactor (less than 1 in a million tracklets). Thus some apparent detections of imminent impactors can be spurious and there is no way to avoid this, short of abandoning the statistical error model and resorting to a careful human inspection of the image to reveal possible causes of degradation of the data.

Score computation

The sampling of the Admissible Region and of the MOV allow the computation of some scores for each object. The scores give us a first insight into the nature of the object, even though the asteroid were not a potential impactor.

The systematic ranging allows one to express the object orbital elements as a function of the range and the range-rate. Thus if we identify a class of objects through a property involving its orbital elements (e.g. q < 1.3 au for NEOs), each MOV orbit can be matched to this class and thus the class of objects actually corresponds to a subset of the Admissible Region. A numerical computation of the probability integral over this subset gives the probability of the object to belong to the given class.

NEOScan currently computes several scores: the probability to belong to different classes (NEO, MBO, DO, SO, and PHA) or to be on a geocentric orbit. The definitions of the classes are listed below.

where a is the semi-major axis (in au), e is the eccentricity, MOID is the Minimum Orbit Intersection Distance, and H is the absolute magnitude.