AI 中文总结
本研究采用宇宙计时仪方法，利用超致密巨大星系测得z=0.15处的哈勃参数，为解决哈勃张力提供了独立测量结果。
AI 中文摘要
测量宇宙膨胀率是宇宙学的核心挑战，尤其是早期和晚期探测得到的哈勃常数H0之间存在持续且统计显著的差异，即所谓的哈勃张力。因此，独立且稳健的方法对于验证现有测量结果、评估潜在系统效应至关重要。本研究采用宇宙计时仪（CC）方法，该方法利用宁静星系的微分年龄演化来测量哈勃参数H(z)，且无需假设任何基础宇宙学模型。与以往的CC研究不同，本研究将分析限制在遗迹天体——拥有最古老恒星种群的超致密巨大星系（UCMGs），从而最小化与恒星形成历史和并合历史相关的不确定性。本研究独立测得红移z=0.15处的哈勃参数H(z=0.15)=85±53 km/s/Mpc。
英文摘要
Measuring the expansion rate of the Universe is a central challenge in cosmology. Independent and robust methods are essential to validate existing measurements and assess potential systematic effects. We employ the cosmic chronometer (CC) approach, which uses the differential age evolution of quiescent galaxies to measure the Hubble parameter H(z) without assuming an underlying cosmological model. In contrast to previous CC studies, we restrict our analysis to relics -- ultra compact massive galaxies (UCMGs) hosting the oldest stellar populations -- thereby minimising uncertainties related to star formation history and merger history. We select a sample of 189 relic galaxies in the redshift range 0.07< z <0.22 from the E-INSPIRE UCMGs catalogue. Using the D_n4000 spectral index of relic galaxies and its redshift evolution, combined with MILES stellar population synthesis models, we derive the differential age relation required to infer H(z). We account not only for metallicity effects but also for the impact of alpha-element enhancement, which has not been explicitly propagated into the systematic uncertainty budget of the D_n4000 cosmic chronometer method. We obtain an independent measurement of H(z=0.15) = 85\pm53 km/s/Mpc. The total uncertainty is dominated by statistical limitations. The systematic component is 13% (8.7% when alpha-enrichment is not propagated), among the tightest estimates in current CC studies. We find that alpha-enrichment plays a major role in the systematic error budget, particularly in the high metallicity regime, and must be properly accounted for in future analyses. We demonstrate the proof of concept that relic galaxies provide a promising pathway to reduce systematic uncertainties in the CC method. The statistical uncertainties, which dominate the error budget, can be significantly reduced by the increase of data volume expected during the next years.
