Characterizing Cometary Dust Environments Directly from Rubin LSST Images: The TailKit Tool

The Vera C. Rubin Observatory Legacy Survey of Space and Time will provide an unprecedented view of active small bodies in the Solar System, combining deep imaging, wide sky coverage, multi-band photometry, and repeated observations over a decade-long baseline. For cometary science, this observing strategy will open a new regime in which activity can be detected and monitored for large samples of objects, including faint or new comets discovered at large heliocentric distances. However, the scientific return of Rubin LSST will depend not only on its discovery capability, but also on our ability to extract physically meaningful information directly from survey images.

To address this challenge, we developed TailKit, a tool within the Italian in-kind contribution to Rubin LSST designed to characterize cometary dust environments directly from imaging data. TailKit bridges the gap between comet detections and physical interpretation by combining image analysis with forward modelling of dust coma and tail structures. Its objective is not only to identify the presence of activity, but to quantify the morphology and evolution of the dust environment in a reproducible and scalable way.The modelling core of TailKit extends a probabilistic dust-tail approach previously applied to ground-based observations of distant comets [2–7], translating it into a configurable, automated, and reproducible workflow suitable for survey-like imaging data. The framework combines a physically motivated description of cometary dust emission with a Monte Carlo treatment of dust-particle dynamics under solar gravity and radiation pressure. In this approach, dust lifting can be driven by supervolatile sublimation at large heliocentric distances, following the Water-Enriched Blocks nucleus model [5]. Once released from the nucleus, dust grains are propagated according to their radiation-pressure response, which depends on particle properties such as size and composition, and the resulting synthetic dust distribution is projected onto the observing geometry and compared with the observed coma and tail morphology.

The observed dust environment is described through a compact set of free parameters: the dust ejection velocity, the dispersion of the radiation-pressure parameter distribution, and the heliocentric-distance dependence of the dust production rate. These parameters allow TailKit to connect observed morphology with physically interpretable quantities, including dust-loss rate, dynamical properties of grains, and the temporal evolution of cometary activity.

TailKit is being implemented as a modular Python-based framework including routines for image preparation, moving-object centering, extraction of comet-centred cutouts, generation of synthetic dust distributions, comparison with observed structures, and exploration of the model parameter space. The modular design is intended to support both detailed modelling of individual objects and future application to larger Rubin LSST samples.

The tool is currently being tested on ground-based comet images, including observations acquired with the Telescopio Nazionale Galileo, whose spatial sampling is comparable to that expected for Rubin LSST data. These tests provide a benchmark for validating the modelling approach, assessing the robustness of parameter retrieval, and identifying the observational conditions under which reliable dust-environment characterization can be achieved. They also allow us to refine the pre-processing and optimization steps before systematic application to Rubin data products.

In the context of Rubin LSST, TailKit is intended to contribute to a survey-to-physical-characterization pathway. Rubin will discover and repeatedly observe large numbers of active small bodies, but automated modelling tools are required to transform those images into physical diagnostics. By characterizing dust environments directly from images, TailKit can support statistical studies of cometary activity, identify objects requiring coordinated follow-up, and provide early physical assessment of newly discovered or newly active comets. This is particularly relevant to the preparation of the European Space Agency mission Comet Interceptor [9], for which the rapid assessment of candidate targets requires information on the activity level, dust morphology, and evolution of the surrounding environment. TailKit can provide a physically grounded framework for interpreting survey images in this context, helping to connect Rubin discoveries with target characterization and follow-up prioritization.

In this contribution, we describe the scientific rationale, modelling approach, and current implementation status of TailKit. We discuss how cometary dust environments can be characterized directly from Rubin LSST images, what physical parameters can be constrained from dust-tail morphology, and how this approach can support both population-level comet science and mission-relevant target assessment in the Rubin era.

Acknowledgements. This project is supported by ASI agreement n. 2020-4-HH.0 and subsequent Addenda. It is developed within the framework of the Italian Rubin LSST in-kind contribution and in connection with the Rubin Solar System Science Collaboration.

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