# Automated Prototype for Asteroids Detection

## TL;DR

This paper presents an automated Python-based prototype pipeline designed to enhance asteroid detection, especially aiding amateurs and small surveys, by replacing manual blink methods with automated analysis.

## Contribution

The paper introduces a novel automated asteroid detection pipeline that leverages existing astrophysics libraries, improving detection efficiency for small-scale and amateur astronomers.

## Key findings

- Automated pipeline successfully detects asteroids in test images.
- Reduces reliance on manual blink detection methods.
- Facilitates asteroid discovery for smaller projects and amateurs.

## Abstract

Near Earth Asteroids (NEAs) are discovered daily, mainly by few major surveys, nevertheless many of them remain unobserved for years, even decades. Even so, there is room for new discoveries, including those submitted by smaller projects and amateur astronomers. Besides the well-known surveys that have their own automated system of asteroid detection, there are only a few software solutions designed to help amateurs and mini-surveys in NEAs discovery. Some of these obtain their results based on the blink method in which a set of reduced images are shown one after another and the astronomer has to visually detect real moving objects in a series of images. This technique becomes harder with the increase in size of the CCD cameras. Aiming to replace manual detection we propose an automated pipeline prototype for asteroids detection, written in Python under Linux, which calls some 3rd party astrophysics libraries.