Galaxy morphology classification using neural ordinary differential equations

Gupta, R. and Srijith, P K and Desai, Shantanu (2022) Galaxy morphology classification using neural ordinary differential equations. Astronomy and Computing, 38. p. 100543. ISSN 2213-1337

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Abstract

We introduce a continuous depth version of the Residual Network (ResNet) called Neural ordinary differential equations (NODE) for the purpose of galaxy morphology classification. We carry out a classification of galaxy images from the Galaxy Zoo 2 dataset, consisting of five distinct classes, and obtained an accuracy between 91%–95%, depending on the image class. We train NODE with different numerical techniques such as adjoint and Adaptive Checkpoint Adjoint (ACA) and compare them against ResNet. While ResNet has certain drawbacks, such as time consuming architecture selection (e.g. the number of layers) and the requirement of a large dataset needed for training, NODE can overcome these limitations. Through our results, we show that the accuracy of NODE is comparable to ResNet, and the number of parameters used is about one-third as compared to ResNet, thus leading to a smaller memory footprint, which would benefit next generation surveys. © 2022 Elsevier B.V.

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IITH Creators:

IITH Creators	ORCiD
Srijith, P K	https://orcid.org/0000-0002-2820-0835
Desai, Shantanu	http://orcid.org/0000-0002-0466-3288

Item Type:

Article

Additional Information:

We would like to thank the galaxy challenge, Galaxy Zoo, SDSS and Kaggle platform for sharing their data. RG is supported by funding from DST-ICPS (T-641). We are grateful to the anonymous referee for useful feedback on our manuscript.

Uncontrolled Keywords:

Galaxy morphology classification; Neural ordinary differential equations; ResNets

Subjects:

Computer science
Physics
Physics > Astronomy Astrophysics

Divisions:

Department of Computer Science & Engineering
Department of Physics

Depositing User:

. LibTrainee 2021

Date Deposited:

27 Jul 2022 07:11

Last Modified:

27 Jul 2022 07:11