On the Detectability and Measurement of Galactic Bars as a Function of Redshift
Abstract
The cosmic evolution of galactic bars offers key insights into the dynamical history of disk galaxies. However, studies of high-redshift bars are severely hampered by observational effects such as resolution degradation and surface brightness dimming. To quantify these biases and establish a robust methodology of bar detection and measurement for high-redshift galaxies, we develop an automated pipeline integrating isophotal ellipse fitting and Fourier decomposition. We benchmark its performance using a comprehensive set of mock observations generated by artificially redshifting high-quality images of local barred galaxies to match the background and resolution of HST (z ≈ 0.5−3) and JWST (z ≈ 1−6) observations. We find that applying standard bar detection criteria derived from local galaxies to high-redshift data results in a severe underestimation of the bar fraction, missing >50% of bars at z>0.5 in HST imaging and a similar fraction at z>2 in JWST imaging. Using adaptive, redshift-dependent criteria optimized via mock data achieves recovery rates above 60% out to z ≈ 3 for HST and z ≈ 6 for JWST. The ellipse fitting method significantly outperforms Fourier decomposition in measurement reliability. While ellipse fitting systematically underestimates bar length by ∼ 4%--36% due to surface brightness dimming, this bias is consistent and correctable (σ≈ 0.88--$2.22$); in contrast, the Fourier method is prone to catastrophic failures (σ≈ 2.40--$6.14$) in the regime of low signal-to-noise ratio. We propose a standardized workflow that prioritizes mock-derived adaptive criteria and ellipse fitting, providing quantitative correction factors and empirical relations that enable future studies to recover intrinsic bar statistics from raw observations.
Related
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BibTeX
@misc{lyu2026detectability,
title = {{On the Detectability and Measurement of Galactic Bars as a Function of Redshift}},
author = {Yi-Xiao Lyu and Luis C. Ho and Zhao-Yu Li and Ming-Yang Zhuang},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.23501},
eprint = {2609.23501},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.23501},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - On the Detectability and Measurement of Galactic Bars as a Function of Redshift
AU - Yi-Xiao Lyu
AU - Luis C. Ho
AU - Zhao-Yu Li
AU - Ming-Yang Zhuang
PY - 2026
DA - 2026/09/20
PB - arXiv
DO - 10.48550/arxiv.2609.23501
UR - https://arxiv.org/abs/2609.23501
AB - The cosmic evolution of galactic bars offers key insights into the dynamical history of disk galaxies. However, studies of high-redshift bars are severely hampered by observational effects such as resolution degradation and surface brightness dimming. To quantify these biases and establish a robust methodology of bar detection and measurement for high-redshift galaxies, we develop an automated pipeline integrating isophotal ellipse fitting and Fourier decomposition. We benchmark its performance using a comprehensive set of mock observations generated by artificially redshifting high-quality images of local barred galaxies to match the background and resolution of HST (z ≈ 0.5−3) and JWST (z ≈ 1−6) observations. We find that applying standard bar detection criteria derived from local galaxies to high-redshift data results in a severe underestimation of the bar fraction, missing >50% of bars at z>0.5 in HST imaging and a similar fraction at z>2 in JWST imaging. Using adaptive, redshift-dependent criteria optimized via mock data achieves recovery rates above 60% out to z ≈ 3 for HST and z ≈ 6 for JWST. The ellipse fitting method significantly outperforms Fourier decomposition in measurement reliability. While ellipse fitting systematically underestimates bar length by ∼ 4%--36% due to surface brightness dimming, this bias is consistent and correctable (σ≈ 0.88--$2.22$); in contrast, the Fourier method is prone to catastrophic failures (σ≈ 2.40--$6.14$) in the regime of low signal-to-noise ratio. We propose a standardized workflow that prioritizes mock-derived adaptive criteria and ellipse fitting, providing quantitative correction factors and empirical relations that enable future studies to recover intrinsic bar statistics from raw observations.
ER -