DESI全形预测：增长的层析Ω_m(z)趋势
A Prediction for DESI Full-Shape: Increasing Tomographic $Ω_m(z)$ Trend
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- Atlantic Technological University（大西洋理工大学）
- Institute for Research in Fundamental Sciences (IPM)（基础科学研究院）
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中文总结 AI 辅助
该研究结合DESI DR1、DR2全形建模约束得到增长的Ω_m(z)趋势，探讨其与暗能量模型的关联，预测DESI最终数据发布的约束改进并分析模型选择意义
中文摘要 AI 辅助
为了确认ΛCDM模型的偏差源于缺失的物理（而非系统误差），需证明模型拟合参数在不同独立可观测量上呈现出定性相似的红移演化趋势，这是验证新物理的唯一途径。本文展示，近期暗能量光谱仪（DESI）DR2全形（FS）建模在有效红移z_eff=2.33处的莱曼-阿尔法约束，与早期DR1全形建模在0.295≤z_eff≤1.491范围内的约束相结合，得到Ω_m(z)=mz+c的直线关系，其斜率m=0.022±0.012，与常数Ω_m的偏差为1.8σ。赤池信息准则和贝叶斯证据均证实，常数Ω_m与增长的Ω_m(z)在统计上无法区分。通过Om(z)诊断，本文回顾了增长和下降的Om(z)分别对应幻影和精质暗能量（DE）区域。全形建模约束对应幻影暗能量，而DESI的重子声学振荡（BAO）及DESI结合外部数据的Ω_m(z)趋势（下降和增长）使幻影穿越不可避免。由于动态暗能量只是Ω_m(z)趋势的一种解释，在得出物理结论前，不同数据集必须收敛于各自的Ω_m(z)趋势，这一点至关重要。本文还预测了DESI全形建模的Ω_m约束在最终数据发布前的改进情况，并探讨了其对模型选择的意义。
英文摘要
To confirm $Λ$CDM deviations are due to missing physics (not systematics), one should demonstrate that the model fitting parameters exhibit qualitatively similar redshift drift across independent observables. This is the only way one guarantees new physics. Here, we show that a recent Dark Energy Spectroscopic Instrument (DESI) DR2 Full-Shape (FS) modelling Lyman-$α$ constraint at $z_{\rm eff} = 2.33$ combined with earlier DR1 FS modelling constraints with $0.295 \leq z_{\rm eff} \leq 1.491$ leads to a straight line $Ω_m(z) = m z + c$ with slope $m = 0.022 \pm 0.012$, $1.8 σ$ removed from constant $Ω_m$. Akaike Information Criterion and Bayesian evidence confirm that constant $Ω_m$ and increasing $Ω_m(z)$ are statistically indistinguishable. Through the $Om(z)$ diagnostic, we review how increasing and decreasing $Ω_m(z)$ trends map to phantom and quintessence dark energy (DE) regimes, respectively. While FS modelling constraints map to phantom DE, the decreasing and increasing $Ω_m(z)$ trends in DESI BAO and DESI with external data make a phantom crossing inevitable. Since dynamical DE is but one interpretation for $Ω_m(z)$ trends, it is imperative that different datasets converge on their $Ω_m(z)$ trends before one jumps to physical conclusions. We forecast how DESI FS modelling $Ω_m$ constraints will improve up to the final data release and explore the implications for model selection.
