# Searching for Moving Objects in HSC-SSP: Pipeline and Preliminary Results

## TL;DR

This paper presents a new pipeline for detecting trans-Neptunian objects in the HSC-SSP survey data, overcoming the survey's non-optimized cadence and dithering patterns, and reports preliminary detection results.

## Contribution

The authors develop a novel detection pipeline that re-arranges survey data, applies machine learning to filter false positives, and links orbits to identify TNOs in a non-dedicated survey.

## Key findings

- Successfully identified TNO candidates in early HSC-SSP data
- Demonstrated the pipeline's effectiveness despite survey limitations
- Provided initial catalog of detected TNOs

## Abstract

The Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) is currently the deepest wide- field survey in progress. The 8.2 m aperture of Subaru telescope is very powerful in detect- ing faint/small moving objects, including near-Earth objects, asteroids, centaurs and Tran- Neptunian objects (TNOs). However, the cadence and dithering pattern of the HSC-SSP are not designed for detecting moving objects, making it difficult to do so systematically. In this paper, we introduce a new pipeline for detecting moving objects (specifically TNOs) in a non-dedicated survey. The HSC-SSP catalogs are re-arranged into the HEALPix architecture. Then, the stationary detections and false positive are removed with a machine learning al- gorithm to produce a list of moving object candidates. An orbit linking algorithm and visual inspections are executed to generate the final list of detected TNOs. The preliminary results of a search for TNOs using this new pipeline on data from the first HSC-SSP data release (Mar 2014 to Nov 2015) are also presented.