Galaxy Spin Classification. I. Z-wise versus S-wise Spirals with the Chirality Equivariant Residual Network

The angular momentum of galaxies (galaxy spin) contains rich information about the initial condition of the universe, yet it is challenging to efficiently measure the spin direction for the tremendous amount of galaxies that are being mapped by ongoing and forthcoming cosmological surveys. We present a machine-learning-based classifier for the Z-wise versus S-wise spirals, which can help to break the degeneracy in the galaxy spin direction measurement. The proposed chirality equivariant residual network (CE-ResNet) is manifestly equivariant under a reflection of the input image, which guarantees that there is no inherent asymmetry between the Z-wise and S-wise probability estimators. We train the model with Sloan Digital Sky Survey images, with the training labels given by the Galaxy Zoo 1 project. A combination of data augmentation techniques is used during the training, making the model more robust to be applied to other surveys. We find an similar to 30% increase in both types of spirals when Dark Energy Spectroscopic Instrument (DESI) images are used for classification, due to the better imaging quality of DESI. We verify that the similar to 7 sigma difference between the numbers of Z-wise and S-wise spirals is due to human bias, since the discrepancy drops to sigma with our CE-ResNet classification results. We discuss the potential systematics relevant to future cosmological applications.

Automated summary

We propose a machine-learning-based classifier to eliminate the polarization ambiguity in galaxy spin measurement. The model is trained with Sloan Digital Sky Survey images and enhanced with data augmentation techniques. Using Dark Energy Spectroscopic Instrument images, a 30% increase in both types of spiral galaxies is observed, and the human bias discrepancy is reduced to zero with our CE-ResNet classification results. We also discuss potential systematic issues for future cosmological applications.