Detecting Moving Objects With Machine Learning

TL;DR

This paper reviews machine learning methods for detecting moving objects in astronomical images, highlighting recent techniques, example networks, and best practices to avoid pitfalls like overfitting.

Contribution

It provides a comprehensive overview of machine learning applications in astronomical moving object detection, including example neural networks and guidelines for robust implementation.

Findings

Convolutional neural networks are commonly used for detection tasks.

Example networks include a Residual Network and a brightness prediction CNN.

Best practices for training and validation are discussed to prevent overfitting.

Abstract

The scientific study of the Solar System's minor bodies ultimately starts with a search for those bodies. This chapter presents a review of the use of machine learning techniques to find moving objects, both natural and artificial, in astronomical imagery. After a short review of the classical non-machine learning techniques that are historically used, I review the relatively nascent machine learning literature, which can broadly be summarized into three categories: streak detection, detection of moving point sources in image sequences, and detection of moving sources in shift and stack searches. In most cases, convolutional neural networks are utilized, which is the obvious choice given the imagery nature of the inputs. In this chapter I present two example networks: a Residual Network I designed which is in use in various shift and stack searches, and a convolutional neural network that was designed for prediction of source brightnesses and their uncertainties in those same shift-stacks. In discussion of the literature and example networks, I discuss various pitfalls with the use of machine learning techniques, including a discussion on the important issue of overfitting. I discuss various pitfall associated with the use of machine learning techniques, and what I consider best practices to follow in the application of machine learning to a new problem, including methods for the creation of robust training sets, validation, and training to avoid overfitting.