SB8
The session focuses on contributions that link LSST discoveries to physical interpretation and mission-relevant applications. Emphasis is placed on time-domain observations that constrain activity, surface evolution, fragmentation, rotational, and non-gravitational dynamics, together with their implications for mission planning, including target selection and coordinated follow-up.
Contributions addressing survey-to-mission pathways are particularly encouraged, including methodological advances for extracting physically interpretable, mission-relevant parameters from large time-domain datasets. The session also welcomes studies of rare or non-standard transient phenomena, such as anomalous small bodies, “dark comets,” and statistical searches for compact dark-matter flybys, with emphasis on robust observational constraints.
At the interface between LSST discoveries and mission-enabling applications, the session highlights LSST’s role as a pathfinder for future planetary missions through reflecting the emergence of interconnected, time-domain Solar System science, in which discovery and quantitative physical characterisation increasingly proceed in parallel.
The Vera C. Rubin Observatory will transform the study of solar system minor bodies by providing multi-band photometry over an unprecedented range of temporal and spatial scales. Early Science Validation (SV) observations obtained during 2025–2026 represent one of the first large datasets suitable for asteroid physical characterization, albeit with sparser, irregular, and heterogeneous sampling compared to later survey phases. These data provide a critical testbed for developing methodologies capable of extracting reliable physical parameters under realistic survey conditions.
In this work, we present a unified framework to derive asteroid rotational and compositional properties from Rubin SV observations. Building on methods previously applied to Rubin First Look data, we extend the analysis to multi-band (griz) photometry with reduced temporal coverage. Our approach combines two complementary techniques for rotation period determination: (i) high-order Fourier (HFO) light-curve modeling and (ii) a multi-band Lomb–Scargle (LSM) periodogram analysis. The HFO method models the observed light curve as a Fourier series that incorporates phase-angle corrections and band-dependent magnitude offsets, enabling flexible representation of complex rotational signals. In contrast, the LSM approach emphasizes computational efficiency and robustness to irregular sampling by fitting a shared periodic signal across filters with a fixed-order harmonic model.
Figure 1: LSM periodogram and phased multi-band light curve for 2026 DO14, showing a robust ~1.91 h rotation period and double-peaked morphology.
To ensure reliability, we implement a wrapper-level validation framework that compares the outputs of both methods using quantitative criteria based on band coverage, photometric amplitude, and inter-method consistency. Objects are classified as reliable only when both approaches yield consistent rotation periods within a defined tolerance and the observational dataset satisfies minimum completeness requirements. This strategy mitigates common degeneracies such as aliasing and harmonic ambiguities, which are particularly severe in sparsely sampled datasets.
Once rotation periods are established, we derive light-curve amplitudes and estimate minimum axial elongations under the assumption of triaxial ellipsoid shapes. The amplitude is corrected for phase-angle effects and converted into lower limits on the axis ratio a/b, providing constraints on asteroid shape distributions. For well-sampled objects, these estimates are consistent with expectations for collisionally evolved populations, while extreme values may indicate highly elongated or contact-binary configurations.
A key component of our framework is the derivation of rotation-corrected colors and taxonomic classifications. Because Rubin observations in different filters are not simultaneous, we model all photometric measurements with a shared periodic function and band-dependent offsets, allowing color indices to be extracted without bias from rotational variability. From these offsets, we compute standard color indices (e.g., g-r, r-i, i-z) and derive spectral slopes using calibrated reflectance transformations. Taxonomic classes are then assigned in the (i-z, gri slope) parameter space, following empirical boundaries established from SDSS data.
Figure 2: Taxonomic classification of 2026 DO14 in (i-z, gri slope) space, consistent with a D-type asteroid.
The inclusion of z-band photometry is particularly important, as it provides sensitivity to absorption features near 1 µm and significantly improves compositional discrimination compared to traditional gri-based taxonomy. This enables the identification of distinct asteroid classes, including C-, S-, D-, X-, and V-type objects, and represents one of the first demonstrations of Rubin data being used for taxonomic classification.
We validate the methodology using both real and simulated datasets. A well-sampled asteroid (2026 DO14) serves as a benchmark case, for which both HFO and LSM methods converge on a consistent rotation period (~1.9 hours) and yield compatible color and taxonomy estimates (see Figures 1 and 2). However, most objects in the SV dataset exhibit sparse and clustered temporal sampling. To quantify the impact of observational limitations, we simulate reduced datasets by subsampling observations into a small number of temporal clusters. These experiments indicate that reliable period determination depends strongly on the number of observing clusters and their temporal extent. In particular, HFO methods require at least three well-separated clusters to produce robust solutions, whereas the LSM approach remains more stable under limited sampling conditions.
Application of the framework to the February 2026 Rubin Minor Planet Center dataset yields preliminary rotational and compositional properties for a small sample of asteroids. While only a subset of objects meet strict reliability criteria, the results demonstrate that meaningful physical parameters—including rotation periods, amplitudes, colors, and taxonomy—can be extracted even from early, incomplete datasets. The derived taxonomic distribution is broadly consistent with expectations from previous surveys, confirming the validity of the approach.
Overall, this study emphasizes that data quality—particularly temporal coverage and signal amplitude—is the primary limiting factor in early Rubin analyses. Nevertheless, the methods developed here provide a robust and scalable foundation for future large-scale studies. As Rubin survey operations continue and observational coverage improves, this framework can be applied to millions of objects, enabling systematic investigations of asteroid spin states, shapes, and compositions. These results establish a practical pathway toward population-level characterization of the Solar System in the Legacy Survey of Space and Time era, demonstrating that even early Rubin data can yield valuable scientific insights when analyzed with appropriately designed methodologies.
References
V. Carruba, S. Greenstreet, M. Mastropietro, S. Aljbaaed, G. Caritá, R. C. Domingos, M. Huaman, M. M. Bala, R. D. Z. Pedroso, E. M. D. S. Delfino, 2026, Extracting Asteroid Physical Properties from Vera C. Rubin Observatory Science Validation Survey Data: Rotational Periods, Colors, and Taxonomies, Planetary and Space Sciences, under review.
How to cite: Carruba, V., Greenstreet, S., Mastropietro, M., Aljbaae, S., Caritá, G., C. Domingos, R., Huaman, M., Bala, M., Pedroso, R., and Delfino, E.: Extracting Asteroid Physical Properties from Vera C. Rubin Observatory Science Validation Survey Data: Rotational Periods, Colors, and Taxonomies, Europlanet Science Congress 2026, The Hague, The Netherlands, 7–11 Sep 2026, EPSC2026-64, https://doi.org/10.5194/epsc2026-64, 2026.
Surface color heterogeneity on small Solar System objects (SSOs) can be attributed to a wide variety of geological processes, including internal differentiation [1]; volatile condensation, evaporation, and transportation [2]; deposition of exogenous materials [3,4]; and spectral changes due to space weathering across surfaces of varying ages [5]. In-depth characterization of SSOs displaying surface heterogeneity, whether via ground based observing campaigns or spacecraft based remote sensing and in-situ exploration promises to shed light on these geological processes. Efficiently and accurately identifying those SSOs that display surface color heterogeneity from a growing total population of millions is therefore a priority to focus future research directions towards those objects most likely to provide unique insights into SSO geology.
Large scale, multi-filter time-domain sky surveys, including the LSST, provide a wealth of full-disk photometric measurements of SSOs over time. Taking advantage of the relatively short rotational and orbital timescales of SSOs, given knowledge of an object’s rotational state and illumination conditions, the temporal resolution provided by time-domain surveys can be transformed into a probe of spatial variability. Previous studies have used time-domain photometry to infer the shapes [6] and the color and albedo distributions of possible surface features of SSOs [7]. Unfortunately, most time-domain surveys do not measure the colors of objects as a function of time; photometric measurements taken in different filters are typically widely separated in time in comparison to rotational timescales. Straightforward interpretations of color measurements are therefore complicated by a variety of confounding factors affecting the relative brightness of an object, including its changing effective cross section, motion relative to the observer and Sun, and wavelength dependent phase function [8].
Despite these limitations, we argue that SSOs displaying surface color variability can indeed be detected in non-contemporaneous, multi-filter time-domain survey data [9]. We describe a statistical test to identify such objects and, using simulated photometry of a set of synthetic SSOs with predetermined physical properties, explore the test’s performance. To implement the test, a photometric model of each SSO under consideration must be developed. We assess the detection and false-positive rates as a function of model accuracy to evaluate the performance of the test and consider the feasibility of its application to present day datasets. Finally, we report recent progress, results, and lessons learned from applying the test to real photometric datasets.
[1] Johnson, B.C., Sori, M.M., & Evans, A.J. (2019) Nature Astronomy, 4 41-44
[2] Olkin, C.B. et al. (2015) The Astronomical Journal, 154, 258
[3] McCord, T.B. et al. (2012) Nature 491, 83-86
[4] Reddy, V. et al. (2012) Icarus 221, 544-559
[5] Schröder, S. E. et al. (2015) Planetary and Space Science, 117, 236-245
[6] Kaasalainen, M. & Torppa, J. (2001) Icarus, 153, 24
[7] Lacerda, P. (2009) AJ, 137, 3404
[8] Carry, B., et al., (2024) Astronomy & Astrophysics, 687 A38
[9] Humes, O.A. & Agarwal, J. (2026) Astronomy & Astrophysics, in press
How to cite: Humes, O. and Agarwal, J.: Detecting surface color heterogeneity of small Solar System bodies with LSST, Europlanet Science Congress 2026, The Hague, The Netherlands, 7–11 Sep 2026, EPSC2026-93, https://doi.org/10.5194/epsc2026-93, 2026.
One of the long-standing questions in planetary science concerns the origins of meteorites and their immediate precursors, near-Earth objects (NEOs). It is well-established that most of them originate in the main asteroid belt, which has a complex spatial distribution of taxonomies (DeMeo & Carry, 2014; Carvano et al., 2010; Erasmus et al., 2019), even within asteroid families. However, due to the chaotic long-term orbital evolution of NEOs, tracing the orbit of a single NEO back to its source region in the main belt is impossible.
The current state-of-the-art process for evaluating source regions of NEOs is to utilize orbital models of the NEO population built on forward modeling starting from the source regions in the asteroid belt and the cometary regions (e.g. Nesvorny et al., 2023; Granvik et al., 2018). Such evolutionary models retain probabilistic information of where NEOs on different orbits are most likely to originate. While scientifically useful, the analyses suffer from partly overlapping orbital distributions that lead to the most important source regions carrying too much weight when applied to individual NEOs (e.g. Granvik & Brown, 2018). However, if all NEOs of, say, a given taxonomic class are treated as a group when constructing the orbital model–similar to using all detected NEOs when constructing a generic NEO orbital model—the resulting model will provide a probabilistic assessment of the source regions for that taxonomic class. A practical challenge with this alternative approach is that the construction of an orbital model requires quantitative knowledge about observational selection effects pertaining to the known NEOs used for estimating the model parameters. We investigate the use of the NEO model by Granvik et al. (2018) to determine the selection effects for NEOs with taxonomical information, and present our results for the likely source regions for NEOs belonging to major taxonomic classes.
We also discuss this work in anticipation of Vera C. Rubin Observatory’s upcoming Legacy Survey of Space and Time (LSST), which will obtain multiband photometry in the griz bands for ~4000 NEOs with diameters d≥10 m (Kurlander et al., 2025). While spectroscopy is the primary method for taxonomic classification of small bodies and has been performed for ~1000 NEOs to date (Thomas et al., 2025), it is a follow-up technique that requires high signal-to-noise and therefore has a complex selection bias. Since multiband photometry can also be used to assign taxonomic types to asteroids (Mo et al., 2026; Navarro-Meza et al., 2024) and LSST will be an extremely well-characterised survey (LSST Science Collaborations et al., 2009), the resulting photometric NEO dataset will be the the largest self-consistent sampling of the NEO population compared to current photometric colour surveys (e.g. Birlan et al., 2024; Moskovitz et al., 2026) by a factor of ~20. Complementary near-infrared observations by Euclid (Carry, 2018) will also provide valuable taxonomy information for ~104 moderately-inclined (i≥15°) NEOs, a subset of which will be discovered by LSST.
References
DeMeo, F., Carry, B. Nat 505, 629–634 (2014). doi:10.1038/nature12908
Carvano, J. M., et al. A&A 510, A43 (2010). doi:10.1051/0004-6361/200913322
Erasmus, N., et al. ApJS 242, 15 (2019). doi:10.3847/1538-4365/ab1344
Nesvorný, D., et al. AJ 166, 55 (2023). doi:10.3847/1538-3881/ace040
Granvik, M., et al. Icar 312, 181-207 (2018). doi:10.1016/j.icarus.2018.04.018
Granvik, M., Brown, P. Icar 311, 271-287 (2018). doi:10.1016/j.icarus.2018.04.012
Kurlander, J., et al. AJ 170, 99 (2025). doi:10.3847/1538-3881/add685
Thomas, C., et al. EPSC-DPS Joint Meeting, (2025). doi:10.5194/epsc-dps2025-1016
Mo, F., et al. EPP 10(1), 196-204 (2026). doi:10.26464/epp2025080
Navarro-Meza, S., et al. AJ 167, 163 (2024). doi:10.3847/1538-3881/ad23d0
LSST Science Collaborations, et al. arXiv e-prints (2009). arXiv:0912.0201
Birlan, M., et al. A&A 689, A334 (2024). doi:10.1051/0004-6361/202450495
Moskovitz, N., et al. PSJ 7, 89 (2026). doi:10.3847/PSJ/ae5642
Carry, B. A&A 609, A113 (2018). doi:10.1051/0004-6361/201730386
How to cite: Dorsey, R. and Granvik, M.: Source regions for near-Earth objects sharing taxonomical classification, Europlanet Science Congress 2026, The Hague, The Netherlands, 7–11 Sep 2026, EPSC2026-1111, https://doi.org/10.5194/epsc2026-1111, 2026.
Earth's temporary satellites (Granvik et al., 2012) are small asteroids that occasionally become trapped as Earth's companions. At any given time, the largest temporary moon has a diameter of ~75 cm, and the moon with a diameter of 3 m is captured once every 10 years. On average, a captured temporary moon makes three revolutions around the Earth over a period of 9 months (Fedorets et al., 2017).
Both the transfer from the main asteroid belt and Lunar ejecta are two plausible solutions for the origin of Earth's temporary satellites. Temporary satellites are a key population for distinguishing between the main-belt and Lunar origin of asteroids in Earth's immediate vicinity (Jedicke et al., 2025, Alessi & Jedicke, 2026). Temporary moons would therefore be ideal targets for studying the population of very small asteroids, about which very little information is known. Moreover, due to their low delta v and relatively long residence in Earth's vicinity, temporary satellites are outstanding test mission targets in the emerging field of asteroid mining (e.g. Granvik et al., 2013). Finally, temporary moons provide opportunities to train for navigation around individual boulders which have been observed floating, e.g. in the vicinity of asteroid Bennu during the visit of the OSIRIS-ReX mission.
So far, only two temporary satellites have been discovered, both by the Catalina Sky Survey. LSST is expected to discover temporary satellites on a more regular interval, currently expected to be 1-4 year (Fedorets et al. 2020a). With LSST, not only will it be possible to understand the population of Earth's temporary satellite as a population, but also to narrow the size-frequency distribution gap in the NEA population at 1-10 metres between asteroid survey and bolide data (e.g., Chow & Brown, 2025). However, due to narrow observational windows of Earth's temporary satellites, additional observations and sieving through LSST alerts will be required for their physical characterisation in addition to LSST baseline.
In this presentation, I will discuss the expected discovery methodology of Earth's temporary moons, and consider ideas for their in situ exploration.
References:
Granvik, M., Vaubaillon, J. & Jedicke, R. (2012), Icarus 218, 262 – 277.
Granvik, M. et al. (2013), ’Earth’s Temporarily-Captured Natural Satellites – The First Step Towards Utilization of Asteroid Resources’, in V. Badescu, ed., ‘Asteroids. Prospective Energy and Material Resources’, Springer, pp. 151 – 167.
Fedorets, G., Granvik, M. & Jedicke, R. (2017), Icarus 285, 83 – 94.
Fedorets, G., Granvik, M., Jones, R. L., Jurić, M. & Jedicke, R. (2020a), Icarus 338 113517.
Chow, I & Brown, P. G. (2025) Icarus 429 116444.
Jedicke, R. et al. (2025), Icarus 2025 438 116587
Alessi, E. M. & Jedicke, R. (2026) Icarus 455 117109
How to cite: Fedorets, G.: Earth's temporary satellites as potential space mission targets, Europlanet Science Congress 2026, The Hague, The Netherlands, 7–11 Sep 2026, EPSC2026-271, https://doi.org/10.5194/epsc2026-271, 2026.
The New Horizons spacecraft continues to operate in the Kuiper Belt and retains capability for trajectory correction maneuvers, preserving the possibility of a final close encounter with a small Kuiper Belt Object (KBO). Identifying such a target presents a fundamentally different problem from conventional trans-Neptunian object (TNO) discovery surveys. Viable encounter candidates must satisfy simultaneous constraints on detectability, spacecraft reachability, encounter geometry, available ∆V, and the time required for orbit determination and maneuver planning. The accessible search volume is therefore extremely limited, and maximizing the number of detections is not equivalent to maximizing the probability of identifying a dynamically reachable target.
We present the observational and analysis framework developed to support downstream target searches for New Horizons, with particular emphasis on the transition from conventional survey discovery methods toward mission-driven, time-domain characterization strategies relevant to Rubin Observatory LSST operations. Building on the framework proposed in Kavelaars et al. (2024), the search strategy combines Kuiper Belt population models with spacecraft trajectory constraints to predict the spatial density, apparent motion distribution, and observability of accessible objects downstream of the spacecraft trajectory. These models define optimal search geometries, cadence strategies, and limiting magnitudes for deep imaging campaigns.
We summarize deep searches conducted with the Subaru Hyper Suprime-Cam (HSC) and discuss the first Rubin Observatory trial analyses obtained during early commissioning-era observations in 2025. These searches operate near the practical limits of moving-object detection in wide-field ground-based imaging and therefore require analysis methods that differ substantially from standard survey pipelines. To support this work we have extended the Rubin LSST Science Pipelines infrastructure to enable deep orbital shift-and-stack analyses for extremely slowly moving outer Solar System objects. This includes orbit-based moving-source injection tools for completeness characterization together with integration of pkbmod, a modified implementation of the KBMOD framework, within the LSST middleware environment.
The New Horizons target search provides a demanding test case for the broader problem of converting LSST-scale time-domain discovery streams into physically interpretable and mission-relevant constraints. The methods developed here provide a scalable and reproducible framework for deep searches in wide-field imaging while preserving calibration provenance and quantitative characterization of detection efficiency. Future progress will likely require detection approaches that move beyond classical thresholding methods, including machine-learning-assisted analyses optimized for extremely low signal-to-noise moving-source detection in sparse time-domain datasets.
How to cite: Kavelaars, J., Chandler, C., and Fraser, W. and the The New Horizons Search Team: Deep Ground Based Searches for New Horizons Encounter Targets, Europlanet Science Congress 2026, The Hague, The Netherlands, 7–11 Sep 2026, EPSC2026-751, https://doi.org/10.5194/epsc2026-751, 2026.
The ESA Comet Interceptor mission aims to perform a flyby of a Long Period Comet (ideally a Dynamically New Comet) or an Interstellar Object during its first passage through the inner Solar System.[1] Such targets are selected for their pristine state, as they have not been altered by any previous approach near the Sun and hence carry fundamental clues on the Solar System formation and evolution. Comet Interceptor requires an innovative methodology, as the target is likely to be identified only after the mission launch.[1] The upcoming Vera C. Rubin Observatory, with its Legacy Survey of Space and Time (LSST), will play a crucial role in the early detection of such Targets.
Current estimates suggest that the Rubin Observatory will detect over five million new Solar System objects, addressing the current limitations in Long Period Comet (LPC) datasets. This represents a groundbreaking advancement, as existing records are presently characterised by a limited number of discoveries, non-homogeneous observation methodologies, and a lack of standardized data products across different surveys. To assessthe survey future impact, we developed Horizon-CometScout, a Python pipeline that evaluates LSST’s discovery potential by analysing how much earlier LSST would have discovered currently known comets if it had already been operational.[2] This tool provides a direct comparison with existing survey capabilities by estimating the improvement in detection epoch and distance for potential targets.
Horizons-CometScout is a Python pipeline that combines JPL SBDB/Horizons information with Rubin LSST-like observability criteria to retrospectively screen known long-period and hyperbolic comets for their relevance to Comet Interceptor-like target searches.
The pipeline automatically retrieves the list consisting of all Hyperbolic and Long Period Comets in the Horizons database. It identifies comets with reliable data for absolute luminosity M1 and the comet total magnitudeslope parameter k1, then it automatically flags comet according to the criteria in Table 3 of [3]. Moreover, a list of Comet Interceptor Potential target defined according to [2] is also obtained.
The pipeline produces an historical set of observation for the 10 years prior to the perihelion epochs with an estimate of the apparent magnitude in the LSST r-band and check its observability from Rubin Observations. The estimation of LSST performance is further refined through synthetic source injections in Data Preview (DP0.2)[4] images available on the Rubin Science Platform, which simulate cometary appearances in LSST images, assessing the efficiency of available photometric tools in characterizing extended sources.
We will present this tool and show its application to highlight how the Rubin Observatory’s capabilities will enhance the selection and study of pristine cometary targets well in advance of their perihelion passage,providing an advantageous lead time for opportune planning of the mission.
References
[1] Geraint H. Jones, Colin Snodgrass, Cecilia Tubiana, Michael Küppers, Hideyo Kawakita, Luisa M. Laraetal et al. The Comet Interceptor Mission. SpaceScienceReviews(2024)220:9
[2] Laura Inno, Margherita Scuderi, Ivano Bertini, Marco Fulle, Elena Mazzotta Epifani, Vincenzo Della Corte, Alice Maria Piccirillo, Antonio Vanzanella, Pedro Lacerda, Chiara Grappasonni, et al. How much earlier would lsst have discovered currently known long-period comets? arXiv preprint arXiv:2412.12978, 2024.
[3] C Snodgrass, E Mazzotta Epifani, C Tubiana, JP Sánchez, N Biver, L Inno, MM Knight, P Lacerda, J De Keyser, A Donaldson, et al. Considerations on the process of target selection for the comet interceptor mission. Icarus, page 116887, 2026.
[4] O’Mullane, William, et al. "Data Preview 0.2 and Operations rehearsal for DRP." Vera C. Rubin Observatory Technical Note RTN-041 (2023).
How to cite: Serio, S., Inno, L., Bertini, I., Fulle, M., Rotundi, A., Giordano Orsini, M., Ferone, A., Mazzotta Epifani, E., Tonietti, L., Della Corte, V., Ammannito, E., Grappasonni, C., and Sindoni, G.: Horizons-CometScout – Enhancing Target Scouting for the Comet Interceptor Mission through the Vera C. Rubin Observatory , Europlanet Science Congress 2026, The Hague, The Netherlands, 7–11 Sep 2026, EPSC2026-995, https://doi.org/10.5194/epsc2026-995, 2026.
By the classical definition, Long-Period Comets (LPCs) are comets with an orbital period longer than 200 years. With the exception of Comet 153P/Ikeya-Zhang, all known long-period comets have been observed at one perihelion passage only. Currently, long-period comets are typically discovered a few months or a year before perihelion.
Comet Interceptor is the first Fast (F-class) mission in ESA’s Cosmic Vision program, and the first mission targeting an LPC, ideally a dynamically new one that enters the solar system for the first time. Those comets have changed little since the formation of the solar system and contain information about the formation conditions of the solar system. Comet Interceptor is also the first rapid response mission: It will be launched in the second half of 2028 or the first half of 2029 into a halo orbit around the Sun-Earth Lagrange point L2, and wait there on the right moment to start the transfer to the target comet. The encounter will then take place close to earth’ orbit, generally at one of the ecliptic nodes of the cometary orbit.
The rapid response concept of waiting in space allows the target to be discovered even after the spacecraft is launched. However, with a typical transfer time from L2 to the comet encounter of 1-3 years, in most cases the detection is still too late for Comet Interceptor to get to its target. Here is where the Vera Rubin telescope comes in: According to simulations, the median time between discovery and perihelion of a Long-Period Comet increases to 4-6 years, allowing to detect most comets in time (Fig. 1).
Figure 1: Time between detection and perihelion for comets with a perihelion distance inside 2 AU. Left: Observed comets with perihelion between 2010 and 2022. Right: Simulation of LSST performance (Source of simulation: Comet Interceptor proposal).
Early target discovery with LSST may enable further rapid response missions, like planetary defence missions to small asteroids passing near earth or a Comet Interceptor like mission to an Interstellar Object. Comet Interceptor would be capable of flying by an Interstellar Object, however, the likelihood that a reachable one will be found is low. A similar mission with a larger delta-v capability could be considered to encounter an Interstellar Object, with its target likely being found by LSST.
How to cite: Küppers, M. and the Comet Interceptor Science Working Team: LSST: Discovering the Target of the Comet Interceptor mission, Europlanet Science Congress 2026, The Hague, The Netherlands, 7–11 Sep 2026, EPSC2026-110, https://doi.org/10.5194/epsc2026-110, 2026.
Kuiper Belt Objects (KBOs) preserve primitive material from the early Solar System providing valuable clues to irradiation, volatile transport, impacts, and long-term surface evolution in the outer Solar System. However, detailed spectroscopic characterisation of large KBO samples remains observationally expensive, creating a strong need for survey-scale photometric diagnostics that can identify physically interesting targets for further study. In the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) era (Schwamb et al 2023), this question becomes especially timely: can broadband multiband photometry provide a useful first-stage physical discriminator for large outer Solar System populations, helping to connect survey discovery to physical interpretation, follow-up prioritisation, and eventually mission-relevant target selection (Kovačević, Mason, Ćiprijanović 2025)?
We explore this question using a prototype forward-modelling pipeline that maps different KBO surface and activity states into Rubin photometric space. The pipeline first generates synthetic spectra for a hypothetical KBO at 40 AU using the NASA Planetary Spectrum Generator (PSG, Kofman et al 2024), expressed in observer-frame flux appropriate for Rubin photometry. A second module then convolves these spectra with Rubin/LSST ugrizy throughput curves to derive synthetic AB magnitudes and colours.
We consider five representative, physically motivated cases spanning inactive, compositionally altered, mixed, and weakly active states (Figure 1a and Table 1). Run 1 is an inactive rocky baseline, modelled as an achondrite-dominated surface. Run 2 represents an organic-rich irradiated surface using achondrite plus an organic proxy. Run 3 represents an ice-rich, highly reflective surface. Run 4 is a mixed ice-organic state. Run 5 adopts the same mixed surface as Run 4 but adds an aerosol coma in order to test whether weak activity can produce a measurable displacement in colour-colour space.
The main result is that the simulated KBO surface and activity states occupy different regions of Rubin colour space, but the discriminatory power depends strongly on wavelength. Organic-rich cases produce systematically redder visible colours than the inactive baseline, especially in g-r and r-i. Ice-rich cases remain much closer to the baseline, consistent with a weaker reddening of the visible spectral slope. The mixed ice-organic case lies between these end members, as expected for a blended surface state. When a weak aerosol coma is added, the colours shift back toward flatter values: in the adopted example, g-r decreases from 0.5584 in the mixed-surface case to 0.4632 in the aerosol-coma case, while r-i decreases from 0.1991 to 0.1631. This behaviour is consistent with a greying effect in which coma scattering partially suppresses the red slope of the underlying surface (Figure 1 b1).
The colour-colour analysis shows that the clearest separation between physical states is found in the shorter-wavelength combinations, especially in the r-i versus g-r plane (Figure 1 b1). In the i-z versus r-i plane (Figure 1 b2) the same sequence remains visible, but it is more compressed. In the z-y versus i-z plane (Figure 1 b3), the sequence becomes nearly one-dimensional and the different physical cases are much harder to distinguish.
The weak-activity test is particularly relevant for Rubin time-domain Solar System science. To assess the detectability of weak activity, we compare the colour vector of the aerosol-coma case with that of the same mixed surface without an aerosol coma. The addition of a weak aerosol coma shifts the object to a distinct location in Rubin colour space. For the adopted uncertainty model, this displacement is statistically significant, with a SNR of approximately 5 (Figure 2). The corresponding displacement is quantified relative to propagated photometric uncertainties using a chi-squared metric and an overall significance-like quantity, SNR ≅√χ2. In the adopted proof-of-concept setup, a representative uncertainty of 0.01 mag per band is assumed. Although this proof-of-concept result should not be generalised to all weakly active KBOs, it shows that broadband photometry can retain a detectable signature of weak activity even without spectroscopy.
In this sense, Rubin photometry may help identify candidate objects whose colours are consistent with organic-rich, ice-modified, mixed, or weakly active states, thereby supporting prioritisation for spectroscopy, time-domain follow-up, and future mission-oriented target selection. The present study therefore serves as a proof of concept and as a starting point for a more systematic framework for prioritising outer Solar System targets in Rubin survey data.
Figure 1: Prototype KBO classification for the LSST photometry era is deep integrations in the g, r, i bands. The top panel shows the simulated spectra over the LSST filters, illustrating how different surface and activity states map into Rubin photometric space. The bottom panels compare the diagnostic value of LSST filter pairs in terms of topology and dynamic range, showing that g−r, versus r−i is the primary discriminator, i−z provides only secondary validation for more extreme cases, and extension into the z−y regime yields diminishing returns because the sequences become increasingly compressed and degenerate.
| Run | Composition | Physical Driver | Expected Photometric Effect |
| R1 | Inactive Baseline | 100% Achondrite | Featureless rocky baseline |
| R2 | Irradiated Organics | 40% Organic Proxy, 60% Achondrite | Reddening (strong shift toward red visible colors) |
| R3 | Ice-Rich Surface | 30% Antarctica, 70% Achondrite | Reflective / Greying (closer to baseline) |
| R4 | Mixed State | 40% Achondrite, 30% Antarctica, 30% Organic | Blended red/grey signature |
| R5 | Aerosol Coma | R4 Composition + Weak Coma | Aerosol suppression of underlying red slope |
Table 1: Composition definitions for the five representative simulated KBO states, R1–R5.
Figure 2: Detectability of non-grey signatures in LSST colour space for KBOs with a faint coma (σ = 0.01). Left: the coma induces a coherent displacement in colour space relative to the grey no-coma reference; grey dimming cancels, while the non-grey signal remains detectable. The displacement corresponds to SNR ≈ 5 under LSST uncertainties. Right: the underlying band-wise flux changes show a nearly grey dimming pattern, highlighting that only colour-space projections reveal the coma signature.
REFERENCES
Schwamb, M. E., et al. 2023, Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System Science, ApJS, 266, 22.
Kofman, V., et al. 2024, The Pale Blue Dot: Using the Planetary Spectrum Generator to Simulate Signals from Hyperrealistic Exo-Earths, Planetary Science Journal, 5 197
Kovačević, A. B., Mason, N. J., & Ćiprijanović, A. 2025, Multiscale astrobiology with the Vera C. Rubin Observatory Legacy Survey of Space and Time, Frontiers in Astronomy and Space Sciences, 12, 1594485.
How to cite: Long, B., Mason, N., and Kovacevic, A.: Broadband Rubin photometry as a first-stage physical discriminator for outer Solar System targets, Europlanet Science Congress 2026, The Hague, The Netherlands, 7–11 Sep 2026, EPSC2026-84, https://doi.org/10.5194/epsc2026-84, 2026.
Abstract:
The long-term dynamical evolution of asteroid families is governed by the interplay between orbital and rotational evolution driven by thermal forces and collisions. The upcoming Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) era will increase spin state measurements by more than an order of magnitude, enabling population-level tests of evolutionary models.
We aim to observationally trace the rotational evolution of main-belt asteroid families over gigayear timescales. We demonstrate a methodology that will scale directly to LSST datasets, enabling testing thermal evolution model and constraining asteroid family ages on large scales.
We analyzed rotational properties of 8739 asteroids with spin period measurements and 3794 asteroids with obliquity determinations across 28 asteroid families spanning ages from 14~Myrs to 3~Gyrs. We introduced a dimensionless timescale that normalizes each asteroid's family age by its classical YORP timescale, enabling a direct comparison of the rotational states across different evolutionary stages. For each asteroid we defined this quantity as t = τage\τYORP,
where τage is the age of the family, and τYORP is the normal YORP timescale [1]. This dimensionless evolutionary parameter provides a scalable metric suited to large survey datasets such as the LSST.
We examined two key observables: the fraction of slow rotators fslow (periods greater than or equal to 30 hours), and the polarization fraction fpol(the degree to which asteroid spin poles align correctly with their position in the family's V-shape distribution due to the Yarkovsky drift [2]).
Evolution of both quantities were fit to identify characteristic transition timescales. These observables are obtained from byproducts (i.e., spin vectors) of photometric light curve analysis, which is time-consuming and usually obtained for single objects. However, they are also expected products of LSST, which will provide physical characterization for thousands of asteroids every year.
We discovered that the slow-rotator fraction increases steeply with t, saturating at fslow~0.25 around t~20 (Fig. 1). This implies a stochastic YORP timescale of τYORP,stoc~10 τYORP, in comparison with the rotational evolution models that include tumbling and weakened YORP torques [3]. The result suggests that stochastic processes, driven by evolving surface features such as craters and boulders, significantly modify rotation evolution rates.
The polarization fraction reaches a maximum of ~0.8 at t~15, indicating that YORP initially dominates by driving asteroids toward extreme obliquities and enhancing Yarkovsky drift efficiency. However, the subsequent decay toward the random limit fpol→0.5 for t>20 (Fig. 2) reveals that collisional spin reorientation progressively breaks the connection between current spin states and Yarkovsky drift history. This transition marks the limit beyond which V-shape age estimates become unreliable due to partial erasure of the Yarkovsky signature, with important implications for family age dating methods.
The rotational evolution trends of fslow and fpol can be further tested with the forthcoming LSST data release, which is expected to increase the available spin-state sample by at least an order of magnitude [4]. With such datasets, these trends could provide an additional dimension for constraining asteroid family ages, and testing thermal evolution models at unprecedented statistical precision.
References: [1] Rubincam, D. P. 2000, Icarus, 148, 2 [2] Vokrouhlický, D., et al. 2015, in Asteroids IV (University of Arizona Press) [3] Zhou, W.-H., et al. 2025, Nature Astronomy, 9, 493 [4] Ivezi´c, Ž., et al. 2019, The Astrophysical Journal, 873, 111
Figures:
Fig. 1, Fraction of slow rotators as a function of the dimensionless time t. The green points are the binned-collected observational data. Black triangles are simulation data from the [3] model. The red line is the fit function. The shaded area is the 95% confidence interval of the fit.
Fig. 2, Polarization fraction as a function of the dimensionless time t. The green points are the binned data; the red line is the fit function. The black triangles are the distribution of the polarization fraction for the Eos family. The general trend is visible in single asteroid families. The shaded area is the 95% confidence interval of the fit.
How to cite: Bertinelli, G., Zhou, W.-H., and Tanga, P.: Exploring rotational properties and the YORP effect in asteroid families: methodology and prospects for LSST, Europlanet Science Congress 2026, The Hague, The Netherlands, 7–11 Sep 2026, EPSC2026-91, https://doi.org/10.5194/epsc2026-91, 2026.
The Legacy Survey of Space and Time (LSST) conducted by the Vera C. Rubin Observatory is expected to provide unprecedented time-domain photometric datasets for Solar System objects. In this study, we investigate whether photometric observations from the LSST reported to the Minor Planet Center can be used to identify complex rotational states of asteroids. Using the dataset updated on Apr. 15, 2026, we identified 69 asteroids with more than 500 reported photometric measurements (Fig. 1), offering relatively dense sampling compared to typical asteroid lightcurves.
We search for rotation periods using Fourier-based lightcurve analysis and inspect the phased lightcurves for non-periodic behavior potentially associated with tumbling rotation or binary systems. Our focus is on irregular amplitudes, multi-periodic variations, and mutual-event-like features.
As a test case, we investigate the known main-belt binary asteroid (7344) Summerfield. Long-term dense photometric observations obtained from Bohyunsan Optical Astronomy Observatory (BOAO), Lemmonsan Optical Astronomy Observatory (LOAO), Sobaeksan Optical Astronomy Observatory (SOAO), and archival datasets were combined with photometric observations from the LSST acquired during similar epochs to examine how binary-related lightcurve structures may appear within the LSST dataset.
Our goal is to evaluate the effectiveness of LSST observations for identifying complex rotational states and potential binary asteroid candidates within large photometric datasets.
Figure 1. Distribution of asteroids by the number of photometric observations in the LSST asteroid dataset updated on Apr. 15, 2026. The highlighted region indicates 69 asteroids with more than 500 observations, selected for rotational period and complex variability analyses.
How to cite: Kim, H., JeongAhn, Y., Kim, M.-J., Lee, H.-J., Moon, H.-K., and Kim, Y.: Rotation Period Analysis and Search for Complex Rotational States in LSST-Reported Asteroids, Europlanet Science Congress 2026, The Hague, The Netherlands, 7–11 Sep 2026, EPSC2026-382, https://doi.org/10.5194/epsc2026-382, 2026.
The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) will provide multi-band photometry for a large number of Solar System small bodies. Observations obtained by Rubin Observatory and submitted to the Minor Planet Center under the observatory code X05 are also compiled by the Asteroid Institute, providing convenient access to reported Rubin asteroid photometry.
We use Rubin X05 photometry to search for asteroids showing possible surface heterogeneity. While the standard Wide-Fast-Deep survey cadence is generally sparse for detailed rotational analyses, some observing sequences contain repeated measurements over relatively short time intervals. We focus on such well-sampled cases, selecting asteroids with several hundred or more observations, and construct phased lightcurves separately for each filter.
For a homogeneous asteroid in principal-axis rotation, lightcurves obtained in different filters should show nearly the same rotational morphology, apart from color-dependent magnitude offsets. In contrast, repeatable differences in amplitude, shape, or phase among filters may indicate rotational color variation, albedo variation, or surface heterogeneity. Objects with known or suspected binary or non-principal-axis rotation are excluded from the analysis, and observations obtained under substantially different viewing or phase angle conditions are removed. Multi-filter measurements are then compared at their corresponding rotational phases.
This study explores the potential of Rubin LSST multi-band photometry for identifying surface variations on asteroids. As the number of X05 observations increases, this approach will help identify candidate asteroids with color variations across rotational phase and possible heterogeneous surfaces, providing useful targets for follow-up spectroscopy and dense lightcurve observations.
How to cite: JeongAhn, Y. and Kim, H.: Searching for Surface Heterogeneity in Asteroids Using Rubin LSST Multi-band Photometry Submitted to the MPC, Europlanet Science Congress 2026, The Hague, The Netherlands, 7–11 Sep 2026, EPSC2026-800, https://doi.org/10.5194/epsc2026-800, 2026.
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.
References.
[1] Ivezić Ž. et al., ApJ 2019, 873, 111; [2] Fulle M., Blum J., Rotundi A., Gundlach B., Güttler C., Zakharov V. V., MNRAS 2020, 493, 4039; [3] Fulle M. et al., MNRAS 2022, 513, 5377; [4] Fulle M. et al., A&A 2010, 522, A63; [5] Ciarniello M. et al., MNRAS 2023, 523, 5841; [6] Mazzotta Epifani E. et al., A&A 2014, 561, A6; [7] Bertini I. et al., MNRAS 2026, 548, stag550; [8] Inno L. et al., in prep.; [9] Jones G. H. et al., Space Sci. Rev. 2024, 220, 9; [10] Chandler C. O. et al., ApJ 2026, 1001, L35.
How to cite: Inno, L., Giordano Orsini, M., Bertini, I., Fulle, M., Mazzotta Epifani, E., Ferone, A., Tonietti, L., Serio, S., Della Corte, V., Ammannito, E., Grappasonni, C., Sindoni, G., and Rotundi, A.: Characterizing Cometary Dust Environments Directly from Rubin LSST Images: The TailKit Tool, Europlanet Science Congress 2026, The Hague, The Netherlands, 7–11 Sep 2026, EPSC2026-859, https://doi.org/10.5194/epsc2026-859, 2026.
The Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory is predicted to discover ~6 million new solar system objects (SSOs) over 10 years of operation. For every visit the solar system processing pipeline (SSP) will automatically associate difference image sources with known SSOs and issue public alert packets containing details of each detection within minutes of exposure. The depth and resolution of the LSST Camera (LSSTCam) means that these SSO alerts will be an excellent resource to search for transient activity of asteroids and comets. Such events could be caused by the onset of cometary activity, outbursts, fragmentation, and collisional or rotational induced activity. Each visit is expected to generate ~10,000 alerts (with ~100s of SSOs) and so the ability to rapidly identify and narrow down events of interest will help to prioritise follow up observations. The analysis of activity across the SSO populations will help inform us of their physical properties and the processes by which they evolve.
The Adler software package is an LSST:UK in-kind contribution of analysis tools to detect and characterise SSO activity in the LSST alert stream. It is an open source package which is intended to allow an automated analysis of alerts served by a broker, or for users to perform their own analysis on the Rubin Science Platform (RSP). Adler will digest both the photometric measurements and cutout images provided in alerts. By using a range of models describing asteroid brightness, through phase angle and rotational effects, Adler will compare the new photometric measurement to the predicted brightness of an SSO and identify outliers which could indicate an activity event. Furthermore, Adler will perform image analysis techniques specific to active SSOs. Wedge photometry measures fluxes in azimuthal bins centred on the target in order to identify the presence and direction of cometary tails. We also apply a noise-based non-parametric detection technique (NoiseChisel; originally designed for detection of low surface brightness galaxies) in order to characterise the extent of faintly active SSOs. Such image analyses could run on the small cutout images supplied with the alert packet and on larger cutouts retrieved via the RSP after the standard 80 hour embargo period.
At the time of writing the Rubin Observatory is in the final stages of preparation prior to commencement of the LSST proper. As such Adler is being developed and tested on the available early science data, namely Data Preview 1 (taken with the Commissioning Camera), intermittent pre-survey LSSTCam alerts, and the database of photometry submitted to the Minor Planet Center as part of SSP validation. We present the capabilities of Adler's photometric and image-based analysis and initial results from this early data.
Figure 1. Adler (1) provides a python wrapper for Gnuastro (2) image analysis routines to aid in active solar system object detection and characterisation: wedge photometry (3) and NoiseChisel (4).
a. Input ZTF difference image of the Didymos asteroid system, where the activity was initiated by the DART mission impact on the secondary Dimorphos.
b. Azimuthal bins used in the wedge photometry analysis.
c. Radial plot of the sum of flux in each azimuthal bin, showing a flux excess due to the dust tail.
d. Identification of sources and their extent using NoiseChisel.
e. Image showing some of the source properties from ellipsoidal fits to the pixels identified by NoiseChisel.
References:
1. https://github.com/lsst-uk/lsst-adler
2. GNU Astronomy Utilities 0.24, https://doi.org/10.5281/zenodo.17726900
3. Raúl Infante-Sainz et al 2024 Res. Notes AAS 8 22, https://doi.org/10.3847/2515-5172/ad1ee2
4. Akhlaghi, M.; Ichikawa, T. Noise Based Detection and Segmentation of Nebulous Objects. ApJS 2015, 220 (1), 1. https://doi.org/10.1088/0067-0049/220/1/1
How to cite: Robinson, J. E., Schwamb, M. E., Opitom, C., and Snodgrass, C.: Adler: Detection and Characterisation of Activity in LSST Alerts, Europlanet Science Congress 2026, The Hague, The Netherlands, 7–11 Sep 2026, EPSC2026-1164, https://doi.org/10.5194/epsc2026-1164, 2026.
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will provide multiband time-domain photometry for large Solar System populations. We present a Rubin-like simulation framework in which synthetic multiband light curves are analysed under irregular LSST cadence using cross-band correlation, same-night coherence, colour scatter, amplitude stability, and phase stability. The simulated coherent class includes stable cross-band modulation on tens-of-days timescales, best interpreted as slowly evolving activity, dust/coma evolution, or observing-geometry effects rather than rapid rotational light curves.
Applied to 500 synthetic Rubin-like light curves, the framework separates coherent, natural stochastic, and noise-dominated variability into distinct regions of temporal–chromatic coherence space. This provides a survey-to-mission filtering layer for identifying weakly active, volatile-rich, or transitional small bodies whose organized multiband behaviour makes them promising candidates for spectroscopic follow-up and future spacecraft characterization. Rubin/LSST coherence diagnostics can therefore bridge large-scale discovery, physical interpretation, and mission-target prioritization.
How to cite: Korczakowska, D., Mason, N., and Kovacevic, A.: Temporal–Chromatic Coherence in Rubin/LSST Light Curves as a Pathfinder for Small-Body Missions, Europlanet Science Congress 2026, The Hague, The Netherlands, 7–11 Sep 2026, EPSC2026-1358, https://doi.org/10.5194/epsc2026-1358, 2026.
