Galaxy Morphological Classification with Efficient Vision Transformer

Lin, Joshua; Liao, Song-Mao; Huang, Hung-Jin; Kuo, Wei‐Ting; Hsuan-Min, Ou, Olivia

doi:10.48550/arxiv.2110.01024

Cited by 11 publications

(11 citation statements)

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“…In the future, our research will focus on further improvements to the model, such as parameters in the convolutional layer, training the model with a larger amount of data, etc., thus hopefully improving the prediction efficiency and accuracy. In addition, there are now more efficient methods for classification, such as the ViT method used by Joshua Yao-Yu Lin et al [9], and even Xiaohong Gao…”

Section: Discussionmentioning

confidence: 99%

Galaxy morphology classification using VGG16

Chen

2023

J. Phys.: Conf. Ser.

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There are billions of galaxies in the universe, each with millions or billions of stars. Galaxies vary in shape and size, and they interact with one another, sometimes colliding and merging, sometimes tearing apart, resulting in different features such as bars, rings, pseudo-pillars, and so on. These morphological properties play an important role in the study of galaxy evolution, so the classification of galaxy morphology has always been crucial in astrophysics. Earlier, using traditional methods or manual inspection to accomplish the classification task was a great challenge for astronomers. Today, however, machine learning is widely used for galaxy morphology classification because of its evolving efficiency and accuracy. In this paper, a popular convolutional neural network model (VGG16) will be used to classify galaxy morphology with an accuracy of 81%, which means that VGG16 can be used for correct classification.

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Section: Discussionmentioning

confidence: 99%

Galaxy morphology classification using VGG16

Chen

2023

J. Phys.: Conf. Ser.

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“…In ref. [39], they used ViT to solve galaxy morphological classification with the Galaxy Zoo dataset. They found that, although the total accuracy means of CNN were higher than ViT, it performed better in classifying smaller and fainter galaxies than CNN.…”

Section: Jcap11(2023)075mentioning

confidence: 99%

The universe is worth 64³ pixels: convolution neural network and vision transformers for cosmology

Hwang,

Sabiu,

Park

et al. 2023

J. Cosmol. Astropart. Phys.

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We present a novel approach for estimating cosmological parameters, Ω m , σ8 , w 0, and one derived parameter, S 8, from 3D lightcone data of dark matter halos in redshift space covering a sky area of 40° × 40° and redshift range of 0.3 < z < 0.8, binned to 643 voxels. Using two deep learning algorithms — Convolutional Neural Network (CNN) and Vision Transformer (ViT) — we compare their performance with the standard two-point correlation (2pcf) function. Our results indicate that CNN yields the best performance, while ViT also demonstrates significant potential in predicting cosmological parameters. By combining the outcomes of Vision Transformer, Convolution Neural Network, and 2pcf, we achieved a substantial reduction in error compared to the 2pcf alone. To better understand the inner workings of the machine learning algorithms, we employed the Grad-CAM method to investigate the sources of essential information in heatmaps of the CNN and ViT. Our findings suggest that the algorithms focus on different parts of the density field and redshift depending on which parameter they are predicting. This proof-of-concept work paves the way for incorporating deep learning methods to estimate cosmological parameters from large-scale structures, potentially leading to tighter constraints and improved understanding of the Universe.

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“…[103,104] show that CNN-based frameworks estimate photometric redshifts of SDSS galaxies with higher precision than traditional models that use integrated photometric information alone. Recently, an innovative image analysis model called Vision Transformer was proposed, which works as efficient and accurate as CNNs in estimating galaxy properties, especially when a large training dataset is available [105,106]. Vision Transformer is considered to be more suited for capturing correlations between distant pixels in an image than CNN, and thus further development for applications in astronomy is to be explored.…”

Section: Information Extraction From Observed Datamentioning

confidence: 99%

Machine Learning for Observational Cosmology

Moriwaki¹,

Nishimichi²,

Yoshida³

2023

Preprint

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An array of large observational programs using ground-based and spaceborne telescopes is planned in the next decade. The forthcoming wide-field sky surveys are expected to deliver a sheer volume of data exceeding an exabyte. Processing the large amount of multiplex astronomical data is technically challenging, and fully automated technologies based on machine learning and artificial intelligence are urgently needed. Maximizing scientific returns from the big data requires communitywide efforts. We summarize recent progress in machine learning applications in observational cosmology. We also address crucial issues in high-performance computing that are needed for the data processing and statistical analysis.

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Galaxy Morphological Classification with Efficient Vision Transformer

Cited by 11 publications

References 0 publications

Galaxy morphology classification using VGG16

Galaxy morphology classification using VGG16

The universe is worth 64³ pixels: convolution neural network and vision transformers for cosmology

Machine Learning for Observational Cosmology

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Galaxy Morphological Classification with Efficient Vision Transformer

Cited by 11 publications

References 0 publications

Galaxy morphology classification using VGG16

Galaxy morphology classification using VGG16

The universe is worth 643 pixels: convolution neural network and vision transformers for cosmology

Machine Learning for Observational Cosmology

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The universe is worth 64³ pixels: convolution neural network and vision transformers for cosmology