# Identifying MgII Narrow Absorption Lines with Deep Learning

## TL;DR

This paper demonstrates that deep convolutional neural networks can efficiently and accurately detect MgII absorption lines in quasar spectra, significantly outperforming traditional methods in speed while maintaining high accuracy.

## Contribution

The study introduces a deep learning approach for MgII absorption line detection, achieving high accuracy and rapid analysis, with insights into optimal neural network filter sizes.

## Key findings

- Deep neural networks achieve ~94% accuracy in MgII line detection.
- The method is 10,000 times faster than traditional detection techniques.
- Optimal filter size in the neural network is crucial for best performance.

## Abstract

Metal absorption line systems in distant quasar spectra probe of the history of gas content in the universe. The MgII $\lambda \lambda$ 2796, 2803 doublet is one of the most important absorption lines since it is a proxy of the star formation rate and a tracer of the cold gas associated with high redshift galaxies. Machine learning algorithms have been used to detect absorption lines systems in large sky surveys, such as Principle Component Analysis (PCA), Gaussian Process (GP) and decision trees. A very powerful algorithm in the field of machine learning called deep neural networks, or '' deep learning'' is a new structure of neural network that automatically extracts semantic features from raw data and represents them at a high level. In this paper, we apply a deep convolutional neural network for absorption line detection. We use the previously published DR7 MgII catalog (Zhu et al. 2013) as the training and validation sample and the DR12 MgII catalog as the test set. Our deep learning algorithm is capable of detecting MgII absorption lines with an accuracy of $\sim$94% . It takes only $\sim 9$ seconds to analyze $\sim$ 50000 quasar spectra with our deep neural network, which is ten thousand times faster than traditional methods, while preserving high accuracy with little human interference. Our study shows that Mg II absorption line detection accuracy of a deep neutral network model strongly depends on the filter size in the filter layer of the neural network, and the best results are obtained when the filter size closely matches the absorption feature size.