A new approach to distant solar system object detection in large survey data sets

TL;DR

This paper introduces a novel heliocentric parallactic fitting method for detecting distant solar system objects in large survey data, successfully identifying Makemake and demonstrating potential for discovering elusive objects like Planet Nine.

Contribution

The paper presents a new detection algorithm based on heliocentric distance fitting, effective for sparse, long-term survey data, and demonstrates its feasibility with real data analysis.

Findings

Successfully detected the outer SSO Makemake in WISE data.

Did not detect Planet Nine, setting constraints on its possible location.

Proposed method is robust for sparse, long-term observational data.

Abstract

The recently postulated existence of a giant ninth planet in our solar system has sparked search efforts for distant solar system objects (SSOs) both via new observations and archival data analysis. Due to the likely faintness of the object in the optical and infrared regime, it has so far eluded detection. We set out to re-analyze data acquired by the Wide-Field Infrared Survey Explorer (WISE), an all-sky survey well suited for the detection of SSOs. We present a new approach to SSO detection via parallactic fitting. Using the heliocentric distance as a fit parameter, our code transforms groups of three or more single observation point sources to heliocentric coordinates under the assumption that all data stem from an object. The fact that the orbit of a distant SSO is approximately linear in heliocentric coordinates over long time-scales can be utilized to produce candidates, which can then be confirmed with follow-up observations. We demonstrate the feasibility of the approach by a posteriori detecting the outer SSO Makemake within WISE data. An all-sky search for Planet Nine yielded no detection. While the postulated Planet Nine eluded detection by our algorithm, we tentatively predict that this new approach to moving-object analysis will enable the discovery of new distant SSOs that cannot be discovered by other algorithms. Especially in cases of sparse data observed over long time spans, our approach is unique and robust due to the use of only one fit parameter.