A Robust Study of High-Redshift Galaxies: Unsupervised Machine Learning for Characterising morphology with JWST up to z ~ 8

Abstract

Galaxy morphologies provide valuable insights into their formation processes, tracing the spatial distribution of ongoing star formation and encoding signatures of dynamical interactions. While such information has been extensively investigated at low redshift, it is crucial to develop a robust system for characterising galaxy morphologies at earlier cosmic epochs. Relying solely on the nomenclature established for low-redshift galaxies risks introducing biases that hinder our understanding of this new regime. In this paper, we employ variational auto-encoders to perform feature extraction on galaxies at z $>$ 2 using JWST/NIRCam data. Our sample comprises 6869 galaxies at z $>$ 2, including 255 galaxies z $>$ 5, which have been detected in both the CANDELS/HST fields and CEERS/JWST, ensuring reliable measurements of redshift, mass, and star formation rates. To address potential biases, we eliminate galaxy orientation and background sources prior to encoding the galaxy features, thereby constructing a physically meaningful feature space. We identify 11 distinct morphological classes that exhibit clear separation in various structural parameters, such as CAS-$M_{20}$, Sérsic indices, specific star formation rates, and axis ratios. We observe a decline in the presence of spheroidal-type galaxies with increasing redshift, indicating a dominance of disk-like galaxies in the early universe. We demonstrate that conventional visual classification systems are inadequate for high-redshift morphology classification and advocate the need for a more detailed and refined classification scheme. Leveraging machine-extracted features, we propose a solution to this challenge and illustrate how our extracted clusters align with measured parameters, offering greater physical relevance compared to traditional methods.

29 pages, 17 figures, accepted for publication in ApJ

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• The dawn of disks: unveiling the turbulent ionised gas kinematics of the galaxy population at $z\sim 4-6$ with JWST/NIRCam grism spectroscopy
• Galaxy morphologies at cosmic noon with JWST: A foundation for exploring gas transport with bars and spiral arms
• The ALMA-CRISTAL survey: Resolved kinematic studies of main sequence star-forming galaxies at 4<z<6
• The rate and contribution of mergers to mass assembly from NIRCam observations of galaxy candidates up to 13.3 billion years ago
• Galaxy Zoo JWST: Up to 75% of discs are featureless at $3<z<7$
• Classification of Radio Sources Through Self-Supervised Learning
• Bridging Simulations and Observations: New Insights into Galaxy Formation Simulations via Out-of-Distribution Detection and Bayesian Model Comparison
• Deciphering galaxy images using machine vision -- Combining variational autoencoder and principal component analysis for feature extraction