# Deep learning for studies of galaxy morphology

## TL;DR

This paper demonstrates that deep convolutional neural networks can accurately and rapidly estimate galaxy morphological parameters from simulated telescope images, offering a significant speed advantage over traditional methods.

## Contribution

The study introduces a deep learning approach for galaxy morphology analysis that matches traditional accuracy while being 500 times faster after training.

## Key findings

- Deep learning achieves comparable accuracy to GALFIT.
- The method is 500 times faster post-training.
- Simulated data effectively trains the neural network.

## Abstract

Establishing accurate morphological measurements of galaxies in a reasonable amount of time for future big-data surveys such as EUCLID, the Large Synoptic Survey Telescope or the Wide Field Infrared Survey Telescope is a challenge. Because of its high level of abstraction with little human intervention, deep learning appears to be a promising approach. Deep learning is a rapidly growing discipline that models high-level patterns in data as complex multilayered networks. In this work we test the ability of deep convolutional networks to provide parametric properties of Hubble Space Telescope like galaxies (half-light radii, Sersic indices, total flux etc..). We simulate a set of galaxies including point spread function and realistic noise from the CANDELS survey and try to recover the main galaxy parameters using deep-learning. We com- pare the results with the ones obtained with the commonly used profile fitting based software GALFIT. This way showing that with our method we obtain results at least equally good as the ones obtained with GALFIT but, once trained, with a factor 5 hundred time faster.