Rotational Light-Curve Recovery & Predictions of the LSST Yield of Hildas

TL;DR

This study simulates LSST's ability to discover and recover Hilda asteroid light curves, predicting a fivefold increase in known Hildas and analyzing biases and recovery efficiencies using synthetic populations.

Contribution

It introduces a synthetic Hilda population model and evaluates LSST's light-curve recovery capabilities, highlighting biases and limitations in current modeling assumptions.

Findings

LSST will discover approximately 33,400 Hildas, five times the current known population.

Recovery rate of Hildas in simulations is about 46%, higher than typical observational searches.

Recovery efficiency drops sharply for amplitudes below 0.1 magnitudes, indicating bias in detection.

Abstract

The Hilda population occupies the stable 3:2 mean-motion resonance of Jupiter and provides a window into Solar System evolution, including collisional processes. The NSF-DOE Vera C. Rubin Observatory will conduct the ten-year Legacy Survey of Space and Time (LSST). We present a simulation of Rubin's discovery of Hildas with the Sorcha (Merritt et al. 2025; Holman et al. 2025) survey simulator and the recovery of their light curves. We constructed a synthetic Hilda population model which includes distributions of orbital properties, sizes, collisional families and colors. We applied three distinct populations of sinusoidal light-curves to this same orbit-size-color model: (1) a Gaussian kernel density estimate (KDE) fit to rotational periods and amplitudes from the Lightcurve Database (LCDB; Warner et al. 2009) (2) a super-fast rotator population and (3) a super-slow rotator population. Over the ten-year simulated survey, we predict LSST will discover 33,400 Hildas, a five-fold increase over the known population. Using a multiband Lomb-Scargle Periodogram via Astropy (Price-Whelan et al. 2022) we confidently recover 45.96% of Hildas in our LCDB-based population, higher than typical in observational searches. This suggests our light-curve population model may differ from the intrinsic population. We find strong biases in light-curve amplitude, with recovery efficiency dropping sharply below 0.1 magnitudes, while biases from rotational period are comparatively weak aside from cadence-related features such as LSST's 36 minute revisit cadence. Our recovery efficiency is likely overestimated due to our assumption of constant sinusoidal light-curves, which correspond to optimal pole orientations. These results are the first test of light-curve recovery from simulated LSST observations.