Introduction of Machine Learning for Astronomy (Hands-on Workshop)

TL;DR

This paper provides an accessible introduction to machine learning in astronomy, demonstrating how neural networks can be trained for tasks like redshift inference, classification, and gravitational wave data analysis.

Contribution

It offers a practical tutorial on applying machine learning techniques to astronomical data, highlighting ease of use and versatility of basic neural networks.

Findings

Basic CNN achieves high accuracy in redshift inference

Neural networks can be adapted for classification tasks

Simple modifications enable processing of gravitational wave data

Abstract

This article is based on the tutorial we gave at the hands-on workshop of the ICRANet-ISFAHAN Astronomy Meeting. We first introduce the basic theory of machine learning and sort out the whole process of training a neural network. We then demonstrate this process with an example of inferring redshifts from SDSS spectra. To emphasize that machine learning for astronomy is easy to get started, we demonstrate that the most basic CNN network can be used to obtain high accuracy, we also show that with simple modifications, the network can be converted for classification problems and also to processing gravitational wave data.