Consistency tests between SDSS and DESI BAO measurements

Basundhara Ghosh, Carlos Bengaly
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Abstract

In this work, we investigate whether the baryon acoustic oscillation (BAO) measurements from redshift surveys, like the Sloan Digital Sky Survey (SDSS), and the Dark Energy Spectroscopic Instrument (DESI), are consistent with each other. We do so by obtaining the Hubble and deceleration parameter, respectively $H(z)$ and $q(z)$, from both datasets using a non-parametric reconstruction, so that our results do not depend on any {\it a priori} assumptions about the underlying cosmological model. We find that the reconstructed $H(z)$ and $q(z)$ from SDSS are significantly inconsistent with those obtained from DESI, and that both are only marginally consistent with the $\Lambda$CDM model ($\sim 3\sigma$ confidence level). Interestingly, the combined SDSS and DESI dataset reconciles with the standard model. These results are mostly unchanged with respect to different assumptions on the sound horizon scale value, as well as different reconstruction kernels. We also verify the results for the null diagnostic $\mathcal{O}_{\rm m}(z)$, finding that SDSS favours a quintessence-like dark energy model, whereas a phantom-like dark energy is preferred by DESI data, and once again the combined dataset strongly agrees with $\Lambda$CDM. Therefore, our results call the attention for further examination of such inconsistency, as they can lead to biased and divergent results regarding the validity of the standard model, or the suggestion of new physics.
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SDSS和DESI BAO测量之间的一致性测试
在这项工作中,我们研究了斯隆数字巡天(SDSS)和暗能量光谱仪(DESI)等红移巡天的重子声振荡(BAO)测量结果是否相互一致。我们使用非参数重建方法从这两个数据集中分别获得了哈勃参数和减速参数$H(z)$和$q(z)$,这样我们的结果就不依赖于对底层宇宙学模型的任何{(先验的)假设}。我们发现,SDSS数据集的$H(z)$和$q(z)$与DESI数据集的$H(z)$和$q(z)$明显不一致,而且两者都只与$Lambda$CDM模型略有一致(置信水平为$\sim 3\sigma$)。有趣的是,SDSS和DESI数据集的综合结果与标准模型一致。对于声水平尺度值的不同假设,以及不同的重建核,这些结果基本没有变化。我们还验证了空诊断$\mathcal{O}_{\rm m}(z)$的结果,发现SDSS更倾向于类似五芒星的暗能量模型,而DESI数据则更倾向于幽灵暗能量,合并后的数据集再次与$\Lambda$CDM高度一致。因此,我们的结果呼吁人们注意进一步研究这种不一致性,因为它们可能导致有关标准模型的有效性或新物理学的建议的有偏差和分歧的结果。
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