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2026

1. Hemmelgarn, S., Moskovitz, N., Vida, D., 2026, Icar, 457, 117128, A machine learning approach to meteor classification
   Abstract

   We use machine learning to develop a framework for classifying meteoroids based on 13 directly observed parameters from the Global Meteor Network. This method adds depth to the Kb parameter, which uses only three parameters. We employ a semi-qualitative approach using 28,177 meteor events observed in 2023 by the Lowell Observatory Cameras for All-Sky Meteor Surveillance (LO-CAMS) network to evaluate multiple normalization, dimensionality-reduction, and clustering algorithms. We find that a combination of Factor Analysis (FA) and a Gaussian Mixture Model (GMM) results in clusters most consistent with traditional models. Three FA-derived factors corresponding to meteoroid kinematics, activation thresholds, and size/geometry effects describe the underlying structure of meteoroid behavior. The activation factor emerged as the most discriminating factor distinguishing whether a meteor is of asteroidal or cometary origin. Resulting 3, 6, and 11 cluster models reveal progressively finer compositional structure, from broad physical regimes to detailed subdivisions within cometary and asteroidal populations. From these results, we introduce a physically motivated hardness classification scheme: Hclass. Hclass is a data-driven extension of Kb which physically interprets clusters in terms of the densest iron meteoroids down to the softest cometary material. Application to nine well-studied meteor showers and analysis of clusters in orbital space aids in the physical interpretation of Hclass groups. The Hclass model is supported by an analytical FAGMM formulation that enables application to future datasets. Our results demonstrate that machine learning methods can extract compositional information from modern optical meteor datasets at scale and offers a new framework for interpreting meteoroid populations.

2. Benecchi, S., Porter, S., Grundy, W., Bannister, M., Verbiscer, A., Kavelaars, J., Noll, K., Parker, A., 2026, Icar, 457, 117165, Colors of Cold Classical KBOs from the HST Solar System Origins Legacy Survey (SSOLS)
   Abstract

   We present the individual F606W-F814W color for 23 well resolved (0.08") binary cold classical Kuiper Belt objects (CC KBOs) observed during the HST Cycle 26 Solar System Origins Legacy Survey. Their overall colors are compared with 19 blended binaries and the remaining apparently single objects; 196 objects sampled in total yielding a binary fraction of 21.6% uncorrected for bias. We find two binaries whose F606W-F814W components are both significantly neutral (0.60.8), 2015VM173 and 2015RP280 with the rest of the binaries clustered around F606W-F814W 1. For resolved binaries there is a concentration of components with similar color consistent with previous studies, but no longer a perfect correlation. The secondaries are redder than their respective primaries by 0.06 magnitudes and a Spearman rank test on the component colors shows no significant color correspondence. We note that unlike previous studies, our sample is dynamically distinct, measuring only CCs. We propose that this difference is due to a combination of size (the secondaries being nearly a magnitude fainter and subsequently smaller) and ejecta exchange within binary systems. The binary systems are, on average, 1.15 magnitude brighter than the solitary objects with the primaries alone being 0.63 magnitudes brighter and the secondaries being comparable to the singles. The blended binaries span the color and magnitude space between the two populations with a color average very near that of the primaries. Among the singles there is a trail of redder objects with decreasing size. Color vs. size differences are either primordial or result from surface weathering within binary systems because smaller objects appear redder than single and binary objects. The differences are not due to fragments of collisions being redder.

2 publications and 0 citations in 2026.

2 publications and 0 citations total.