用深度学习重校准宇宙学数据，缓解了哈勃与结构张力问题。
Deep Learning Based Recalibration of SDSS and DESI BAO Alleviates Hubble and Clustering Tensions
- 用深度学习直接估算声学视界半径，摆脱传统模型依赖
- 重校准后哈勃常数和结构增长张力显著下降
- 为无模型数据校准提供新思路，适合宇宙学研究者
传统的重子声学振荡（BAO）数据标定依赖于早期宇宙观测对拖拽时刻声学视界半径 $r_d$ 的估计，并假设宇宙学模型。本文采用深度学习技术，对两个独立的BAO数据集——SDSS与DESI——进行模型无关的 $r_d$ 估计，实现数据重校准，并探讨其对 $Λ$CDM 宇宙学参数的影响。结果显示，两种重校准数据集均显著降低了哈勃常数 $H_0$ 与结构增长参数 $S_8$ 的张力。部分其他参数出现适度偏移，提示需进一步探索此类数据驱动方法的潜力。
原文摘要 · Abstract (English)
Conventional calibration of Baryon Acoustic Oscillations (BAO) data relies on estimation of the sound horizon at drag epoch $r_d$ from early universe observations by assuming a cosmological model. We present a recalibration of two independent BAO datasets, SDSS and DESI, by employing deep learning techniques for model-independent estimation of $r_d$, and explore the impacts on $Λ$CDM cosmological parameters. Significant reductions in both Hubble ($H_0$) and clustering ($S_8$) tensions are observed for both the recalibrated datasets. Moderate shifts in some other parameters hint towards further exploration of such data-driven approaches.
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