# Towards Efficient Detection of Small Near-Earth Asteroids Using the Zwicky Transient Facility (ZTF)

## TL;DR

This paper introduces ZStreak, a machine-learning enhanced pipeline for real-time detection of small, fast-moving near-Earth asteroids using ZTF data, achieving significant discoveries and highlighting detection challenges.

## Contribution

The paper presents ZStreak, an improved, real-time detection pipeline with a deep learning model, tailored for the increased data rate of ZTF, enabling new NEA discoveries.

## Key findings

- Discovered 45 new NEAs, mostly under 100 meters in diameter.
- Most objects are detectable within a 2-hour observation window.
- Identified challenges in tracking and false positive suppression.

## Abstract

We describe ZStreak, a semi-real-time pipeline specialized in detecting small, fast-moving near-Earth asteroids (NEAs) that is currently operating on the data from the newly-commissioned Zwicky Transient Facility (ZTF) survey. Based on a prototype originally developed by Waszczak et al. (2017) for the Palomar Transient Factory (PTF), the predecessor of ZTF, ZStreak features an improved machine-learning model that can cope with the $10\times$ data rate increment between PTF and ZTF. Since its first discovery on 2018 February 5 (2018 CL), ZTF/ZStreak has discovered $45$ confirmed new NEAs over a total of 232 observable nights until 2018 December 31. Most of the discoveries are small NEAs, with diameters less than $\sim100$ m. By analyzing the discovery circumstances, we find that objects having the first to last detection time interval under 2 hr are at risk of being lost. We will further improve real-time follow-up capabilities, and work on suppressing false positives using deep learning.