# Photometric Redshift Analysis using Supervised Learning Algorithms and Deep Learning

## TL;DR

This paper develops and evaluates supervised learning and deep learning models to estimate galaxy redshifts from photometric data, improving accuracy by incorporating morphological features from Galaxy Zoo.

## Contribution

It introduces a comprehensive analysis of photometric redshift estimation using various algorithms, including convolutional neural networks, and demonstrates the benefit of morphological features for accuracy.

## Key findings

- XGBoost achieved RMSE of 0.0707 on the first dataset.
- Morphological features improved redshift prediction accuracy.
- Deep learning models achieved RMSE of 0.117 on unseen data.

## Abstract

We present a catalogue of galaxy photometric redshifts for the Sloan Digital Sky Survey (SDSS) Data Release 12. We use various supervised learning algorithms to calculate redshifts using photometric attributes on a spectroscopic training set. Two training sets are analysed in this paper. The first training set consists of 995,498 galaxies with redshifts up to $z \approx 0.8$. On the first training set, we achieve a cost function of 0.00501 and a root mean squared error value of 0.0707 using the XGBoost algorithm. We achieved an outlier rate of 2.1\% and 86.81\%, 95.83\%, 97.90\% of our data points lie within one, two, and three standard deviation of the mean respectively. The second training set consists of 163,140 galaxies with redshifts up to $z\approx0.2$ and is merged with the Galaxy Zoo 2 full catalog. We also experimented on convolutional neural networks to predict five morphological features (Smooth, Features/Disk, Star, Edge-on, Spiral). We achieve a root mean squared error of 0.117 when validated against an unseen dataset with over 200 epochs. Morphological features from the Galaxy Zoo, trained with photometric features are found to consistently improve the accuracy of photometric redshifts.