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

Abstract

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 ∼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 ∼7σ difference between the numbers of Z-wise and S-wise spirals is due to human bias, since the discrepancy drops to <1.8σ with our CE-ResNet classification results. We discuss the potential systematics relevant to future cosmological applications.

Galaxy spin direction asymmetry in JWST deep fields

“…Because the deep neural network classification has a certain degree of error, the experiment used different thresholds of labeling certainty to balance between the number of galaxies and the accuracy of the labeling. When using the most accurate labeling threshold of 0.9, the analysis provided 9,218 SDSS galaxies spinning clockwise and 9,442 SDSS galaxies spinning counterclockwise, as shown in Table 1 in Jia et al (2023). That provides an asymmetry of ∼2.4%, which is comparable to the asymmetry shown in Shamir (2020d) or in Table 2 for the same sky survey and the same footprint of SDSS galaxies with spectra.…”

“…Deep neural networks were also applied by Jia et al (2023) to galaxy images from SDSS and DESI. Because the deep neural network classification has a certain degree of error, the experiment used different thresholds of labeling certainty to balance between the number of galaxies and the accuracy of the labeling.…”

“…But as discussed in Shamir (2023), while neural networks might not be a method that can be fully trusted, the results of the neural network are certainly not in conflict with the analysis shown in Section 3 and in fact in agreement with these observations. Deep neural networks were also applied by Jia et al (2023) to galaxy images from SDSS and DESI. Because the deep neural network classification has a certain degree of error, the experiment used different thresholds of labelling certainty to balance between the number of galaxies and the accuracy of the labelling.…”