# Transfer Learning in Astronomy: A New Machine-Learning Paradigm

## TL;DR

This paper discusses how transfer learning can enhance astronomical data analysis by leveraging related datasets to improve model accuracy despite differences in data properties.

## Contribution

It introduces transfer learning as a new paradigm in astronomy, demonstrating its potential to address challenges with limited labeled data and domain differences.

## Key findings

- Transfer learning improves classification accuracy in astronomy tasks.
- Leverages spectroscopic data to enhance photometric data analysis.
- Addresses domain mismatch issues in astronomical datasets.

## Abstract

The widespread dissemination of machine learning tools in science, particularly in astronomy, has revealed the limitation of working with simple single-task scenarios in which any task in need of a predictive model is looked in isolation, and ignores the existence of other similar tasks. In contrast, a new generation of techniques is emerging where predictive models can take advantage of previous experience to leverage information from similar tasks. The new emerging area is referred to as transfer learning. In this paper, I briefly describe the motivation behind the use of transfer learning techniques, and explain how such techniques can be used to solve popular problems in astronomy. As an example, a prevalent problem in astronomy is to estimate the class of an object (e.g., Supernova Ia) using a generation of photometric light-curve datasets where data abounds, but class labels are scarce; such analysis can benefit from spectroscopic data where class labels are known with high confidence, but the data sample is small. Transfer learning provides a robust and practical solution to leverage information from one domain to improve the accuracy of a model built on a different domain. In the example above, transfer learning would look to overcome the difficulty in the compatibility of models between spectroscopic data and photometric data, since data properties such as size, class priors, and underlying distributions, are all expected to be significantly different.