Asteroids in the High cadence Transient Survey

TL;DR

This paper demonstrates how high cadence wide field surveys, originally aimed at variable stars, can be repurposed to detect and analyze Solar System objects, including near Earth objects and trans-Neptunian objects, using machine learning and simple trajectory linking algorithms.

Contribution

It introduces a method to identify Solar System objects in high cadence survey data and derives their orbital parameters, showcasing the survey's potential for Solar System science.

Findings

Detected 7,700 Solar System object orbits, including NEOs and TNOs.

Achieved typical orbital parameter errors of ~0.07 AU in semi-major axis and ~0.5 degrees in inclination.

Validated detection efficiency using known asteroids from MPC database.

Abstract

We report on the serendipitous observations of Solar System objects imaged during the High cadence Transient Survey (HiTS) 2014 observation campaign. Data from this high cadence, wide field survey was originally analyzed for finding variable static sources using Machine Learning to select the most-likely candidates. In this work we search for moving transients consistent with Solar System objects and derive their orbital parameters. We use a simple, custom detection algorithm to link trajectories and assume Keplerian motion to derive the asteroid's orbital parameters. We use known asteroids from the Minor Planet Center (MPC) database to assess the detection efficiency of the survey and our search algorithm. Trajectories have an average of nine detections spread over 2 days, and our fit yields typical errors of $\sigma_a\sim 0.07 ~{\rm AU}$, $\sigma_{\rm e} \sim 0.07 $ and $\sigma_i\sim 0.^{\circ}5~ {\rm deg}$ in semi-major axis, eccentricity, and inclination respectively for known asteroids in our sample. We extract 7,700 orbits from our trajectories, identifying 19 near Earth objects, 6,687 asteroids, 14 Centaurs, and 15 trans-Neptunian objects. This highlights the complementarity of supernova wide field surveys for Solar System research and the significance of machine learning to clean data of false detections. It is a good example of the data--driven science that LSST will deliver.