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Following the Pluto flyby of the New Horizons spacecraft, the mission provided a unique opportunity to explore the Kuiper Belt in situ. The possibility existed to fly by a Kuiper Belt object (KBO), as well as to observe additional objects at distances closer than are feasible from Earth-orbit facilities. However, at the time of launch no KBOs were known about that were accessible by the spacecraft. In this paper we present the results of 10 yr of observations and three uniquely dedicated efforts—two ground-based using the Subaru Suprime Camera, the Magellan MegaCam and IMACS Cameras, and one with the Hubble Space Telescope—to find such KBOs for study. In this paper we overview the search criteria and strategies employed in our work and detail the analysis efforts to locate and track faint objects in the Galactic plane. We also present a summary of all of the KBOs that were discovered as part of our efforts and how spacecraft targetability was assessed, including a detailed description of our astrometric analysis, which included development of an extensive secondary calibration network. Overall, these efforts resulted in the discovery of 85 KBOs, including 11 that became objects for distant observation by New Horizons and (486958) Arrokoth, which became the first post-Pluto flyby destination.

We present the results of a fourth planetary defense exercise, focused this time on the small near-Earth asteroid (NEA) 2023 DZ2 and conducted during its close approach to the Earth in 2023 March. The International Asteroid Warning Network (IAWN), with support from NASA's Planetary Defense Coordination Office (PDCO), has been coordinating planetary defense observational campaigns since 2017 to test the operational readiness of the global planetary defense capabilities. The last campaign focused on the NEA Apophis, and an outcome of that exercise was the need for a short burst campaign to replicate a real-life near-Earth object impact hazard scenario. The goal of the 2023 DZ2 campaign was to characterize the small NEA as a potential impactor and exercise the planetary defense system including observations, hypothetical risk assessment and risk prediction, and hazard communication with a short notice of just 24 hr. The entire campaign lasted about 10 days. The campaign team was divided into several working groups based on the characterization method: photometry, spectroscopy, thermal IR photometry and optical polarimetry, radar, and risk assessment. Science results from the campaign show that 2023 DZ2 has a rotation period of 6.2745 ± 0.0030 minutes; visible wavelength color photometry/spectroscopy/polarimetry and near-IR spectroscopy all point to an E-type taxonomic classification with surface composition analogous to aubrite meteorites; and radar observations show that the object has a diameter of 30 ± 10 m, consistent with the high albedo (0.49) derived from polarimetric and thermal IR observations.

The aim of this paper is to find a set of photometric passbands that will give optimal results for spectrophotometric classification of asteroids into taxonomic types and classes. For this purpose various machine-learning methods are used, namely multinomial logistic regression, naive Bayes, support vector machines, gradient boosting, and multilayer perceptrons. Sequential feature selection is performed to assess the contribution of each reflectance difference. We find that to determine the taxonomic complexes with a balanced accuracy of 85%, a set of five spectrophotometric bands is required. For taxonomy type determination with the balanced accuracy of 80% a set of eight bands is necessary. Furthermore, only a three-band system is enough for distinguishing the C-complex asteroids with 92% balanced accuracy. These results can be used for designing future asteroid multifilter sky surveys.

Asteroid taxonomies provide a link to surface composition and mineralogy of those objects, although that connection is not fully unique. Currently, one of the most commonly used asteroid taxonomies is that of Bus-DeMeo. The spectral range covering 0.45–2.45 μm is used to assign a taxonomic class in that scheme. Such observations are only available for a few hundreds of asteroids (out of over one million). On the other hand, a growing amount of space and ground-based surveys delivers multi-filter photometry, which is often used in predicting asteroid types. Those surveys are typically dedicated to studying other astronomical objects, and thus not optimized for asteroid taxonomic classifications. The goal of this study was to quantify the importance and performance of different asteroid spectral features, parameterizations, and methods in predicting the asteroid types. Furthermore, we aimed to identify the key spectral features that can be used to optimize future surveys toward asteroid characterization. Those broad surveys typically are restricted to a few bands; therefore, selecting those that best link them to asteroid taxonomy is crucial in light of maximizing the science output for solar system studies. First, we verified that with the increased number of asteroid spectra, the Bus–DeMeo procedure to create taxonomy still produces the same overall scheme. Second, we confirmed that machine learning methods such as naive Bayes, support vector machine (SVM), gradient boosting, and multilayer networks can reproduce that taxonomic classification at a high rate of over 81% balanced accuracy for types and 93% for complexes. We found that multilayer perceptron with three layers of 32 neurons and stochastic gradient descent solver, batch size of 32, and adaptive learning performed the best in the classification task. Furthermore, the top five features (spectral slope and reflectance at 1.05, 0.9, 0.65, and 1.1 μm) are enough to obtain a balanced accuracy of 93% for the prediction of complexes and six features (spectral slope and reflectance at 1.4, 1.05, 0.9, 0.95, and 0.65 μm) to obtain 81% balanced accuracy for taxonomic types. Thus, to optimize future surveys toward asteroid classification, we recommend using filters that cover those features.