Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging

Abstract

There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or a investigation for maximising their effectiveness. We carry out a comparison between seven common machine learning methods for galaxy classification (Convolutional Neural Network (CNN), K-nearest neighbour, Logistic Regression, Support Vector Machine, and Neural Networks) by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these seven methods in our study. Using a sample of ∼2,800 galaxies with visual classification from GZ1, we reach an accuracy of ∼0.99 for the morphological classification of Ellipticals and Spirals. The further investigation of the galaxies that were misclassified but with high predicted probabilities in our CNN reveals the incorrect classification provided by GZ1, and that the galaxies having a low probability of being either spirals or ellipticals are visually Lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both Es and Spirals. We confirmed ∼2.5% galaxies are misclassified by GZ1 in our study. After correcting these galaxies' label, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).

Galaxy Zoo DECaLS: Detailed visual morphology measurements from volunteers and deep learning for 314 000 galaxies

“…The EfficientNet family of models includes several architectural advances o v er the standard convolutional neural network architectures commonly used within astrophysics (e.g. Dieleman et al 2015 ;Huertas-Company et al 2015 ;Khan et al 2019 ;Cheng et al 2020 ;Ferreira et al 2020 ), including auto-ML-derived structure (He, Zhao & Chu 2019 ;, depthwise convolutions (Howard et al 2017 ), bottleneck layers (Iandola et al 2017 ), and squeeze-andexcitation optimization (Hu, Shen & Sun 2018 ). The EfficientNet B0 model was identified using multi-objective neural architecture search , optimizing for both accuracy and FLOPS (i.e.…”

Machine learning technique for morphological classification of galaxies from the SDSS

“…Their results were as follows: support-vector machine -75.8 %, neural networks -76.0 %, classification trees -69.0 %, and classification trees with random forest -76.2 %. Cheng et al (2020) used the Dark Energy Survey data combined with human labeling from the GZoo1 project to compare the effectiveness of several machine learning methods, among which CNN, k-nearest neighbors, logistic regression, support-vector machine, random forest, and neural networks. These authors obtained that CNN is the most successful method for the binary morphological classification dealing with galaxy images; using a sample of ∼2 800 galaxies at z < 0.25, they attained an accuracy of ∼99 %.…”

Towards robust determination of non-parametric morphologies in marginal astronomical data: resolving uncertainties with cosmological hydrodynamical simulations

“…The first is through crowd-sourcing, whereby the power of the human brain continues to be tapped, through the contributions of citizen scientists (Darg et al 2010;Lintott et al 2011;Casteels et al 2014;Simmons et al 2017;Willett et al 2017). Recently, artificial intelligence is replacing humans and machine learning algorithms are increasingly being applied to the challenge of large imaging datasets, either for general morphological classification (Huertas-Company et al 2015;Domínguez Sánchez et al 2019;Cheng et al 2020;Walmsley et al 2021) or the identification of particular galaxy types/features (Bottrell et al 2019b;Pearson et al 2019;Ferreira et al 2020;Bickley et al 2021). An alternative automated approach, which has been in use for several decades, is to compute some metric of the galaxy's light distribution, a technique which is readily applicable to large datasets.…”